Prediction of inclusions in the slabs from the process

Prediction of inclusions in the slabs from the process characteristics (PREDINC)

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EUROPEAN COMMISSION Directorate-General for Research and Innovation Research Fund for Coal and Steel Unit Contact: RFCS publications Address: European Commission, CDMA 0/178, 1049 Bruxelles/Brussel, BELGIQUE/BELGIË Fax +32 229-65987; e-mail: [email protected]

European Commission

Research Fund for Coal and Steel Prediction of inclusions in the slabs from the process characteristics (PREDINC) L.F. Sancho ArcelorMittal España SA Centro de Desarrollo Tecnológico, PO Box 90, 33400 Avilés (Asturias), SPAIN

V. Colla/S. Cateni Scuola Superiore di Studi e di Perfezionamiento Sant’Anna (SSSA) Piazza Martiri della Libertà 33, 56127 Pisa, ITALY

S. Fera ILVA SpA Viale Certosa, 249, 20151 Milan, ITALY

J. Laine Helsinki University of Technology (HUT) Otakaari 1, FI-02015 HUT, Espoo, FINLAND

C. Mapelli Politecnico di Milano (POLIMI), Piazza Leonardo Da Vinci 20133 Milan, ITALY

D. Senk IEHK-RWTH Aachen Intzestrasse 1, 52072 Aachen, GERMANY

F. Ortega/F. Rodríguez Universidad de Oviedo (UNIOVI) Independencia 13, 33004 Oviedo, SPAIN

Contract No RFSR-CT-2005-00006 1 July 2005 to 31 December 2008

Final report

Directorate-General for Research and innovation

2011

EUR 24992 EN

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CONTENTS Page

FINAL SUMMARY REPORT ...............................................................................................................5 WORK PACKAGE 1 – Data gathering and study of the different methodologies for on-line assessment of steel cleanliness.......................................................................................................................................................... 6 WORK PACKAGE 2 –Data analysis and processing. ............................................................................................. 6 WORK PACKAGE 3 –Sampling campaign and analysis. First trials with the model and first conclusions. ............ 6 WORK PACKAGE 4 – Final model development. Testing and tuning. .................................................................. 7 WORK PACKAGE 5 – Final inclusion modelling implementation ......................................................................... 7 OVERALL CONCLUSIONS..................................................................................................................................... 8 OVERALL POSIBILITIES FOR EXPLOITATION......................................................................................................... 9

SCIENTIFIC AND TECHNICAL DESCRIPTION OF THE RESULTS ........................................ 10 WORK PACKAGE 1: – Data gathering and study of the different methodologies for on-line assessment of steel cleanliness........................................................................................................................................................ 10 Task 1.1: State of the art.....................................................................................................................................10 Task 1.2: Study of methodology and selection of parameters. .........................................................................11 WORK PACKAGE 2 – Data analysis and processing ........................................................................................... 14 Task 2.1: Data pre-processing.............................................................................................................................14 Task 2.2: Collection of the experts’ knowledge. ................................................................................................16 Task 2.3: Data Analysis and Predictor Design. ...................................................................................................17 WORK PACKAGE 3 – Sampling campaign and analysis...................................................................................... 22 Task 3.1: Analysis of samples..............................................................................................................................23 Task 3.2: Identifying the type and quantity of inclusions..................................................................................26 WORK PACKAGE 4 – Final model development. Testing and tuning. ................................................................ 40 Task 4.1: Development of the model. ................................................................................................................41 Task 4.2: Testing and tuning. ..............................................................................................................................46 WORK PACKAGE 5 – Final inclusion modelling implementation. ...................................................................... 51 Task 5.1: User interface and integration. ...........................................................................................................51 Task 5.2: Installation and final tuning. ...............................................................................................................53

LIST OF FIGURES .............................................................................................................................. 57 LIST OF TABLES ................................................................................................................................ 61 LIST OF REFERENCES...................................................................................................................... 63 APPENDIX 1: TECHNICAL LITERATURE SURVEY, STUDY OF METHODOLOGIES, PARAMETERS SELECTION (ALL)................................................................................................. 67 A1.1

TECHNICAL LITERATURE SURVEY ......................................................................................................... 67

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A1.2

STUDY OF METHODOLOGIES................................................................................................................ 69

A1.3

SELECTION OF PARAMETERS................................................................................................................ 75

APPENDIX 2: MODEL DEVELOPMENT (ALL)........................................................................... 78 A2.1

MODELLING TASKS .............................................................................................................................. 78

A2.2

THERMODYNAMIC MODEL (POLIMI/RWTH) ........................................................................................ 78

A2.3

AI MODELS (ARCELORMITTAL/UNIOVI/SSA/HUT)................................................................................ 84

A2.3.1

DATA PRE-PROCESSING .................................................................................................................. 84

A2.3.2

COLLECTION OF THE EXPERT’S KNOWLEDGE ................................................................................... 94

A2.3.3:

MODEL DEVELOPMENT ................................................................................................................... 95

APPENDIX 3: SAMPLING CAMPAING AND ANALYSIS (ARCELORMITTAL ESPAÑA, ILVA, POLIMI, RWTH)...................................................................................................................102 A3.1

SAMPLES ANALYSIS ........................................................................................................................... 102

A3.2

IDENTIFYING THE TYPE AND QUANTITY OF INCLUSIONS.................................................................... 114

APPENDIX 4: IDENTIFICATION OF THE PROBLEMS OF REPRESENTATIVENESS OF PARSYTEC – (SSSA)........................................................................................................................124

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FINAL SUMMARY REPORT PREDINC: PREDICTION OF INCLUSIONS IN THE SLABS FROM THE PROCESS CHARACTERISTICS ArcelorMittal España S.A. RFCS Contract No. RFSR-CT-2005-00006 Period 1 July 2005 to 31 December 2008 This project has been developed by ArcelorMittal España, SSSA, ILVA, Helsinki University of Technology, Politenico di Milano, RWTH-IEKH Aachen University and University of Oviedo and coordinated by ArcelorMittal España. The aim is to develop a system capable to integrate production planning and control with quality assurance in the field of inclusions. Consortium combines an ideal proportion of final users, expert metallurgists, and Data Mining technicians, all them working co-ordinately for the global aim. Cross Correlation and tests were done in order to characterize the capabilities of the models, as reflected in the figure.

Figure 1: Global structure of the project

Seven project meetings were held at each of the partners’ sites. The objectives were completed according to the Technical Annex of the contract, in the levels described in the Scientific and Technical Description of Results. A six month prolongation was demanded and accepted due to a delay occurred in some tasks concerning data gathering for modelling. Work continues even after the end of the project and finally it was closed in December 2009. The project did not exceed the overall budget. The outcome come of each task is summarised below. The principal results, conclusions and potential exploitations are described in the Scientific and Technical Description of Results in this report, while methods and other results are given in the appropriate appendices.

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WORK PACKAGE 1 – Data gathering and study of the different methodologies for on-line assessment of steel cleanliness. Task 1.1: State of the art. A literature survey on inclusions formation and evaluation and data mining techniques was made at the beginning of the project. During the development of the project attention was paid by every partner to new advances in the fields of their research. Task 1.2: Study of methodology and selection of parameters. The output of this task is the selection of an initial set of possible input parameters for model development and data gathering as well as the selection of techniques used for inclusions analysis and modelling. All partners based input parameters selection on the knowledge of the experts. The selection was done also according to the type of model to be developed. ArcelorMittal España, UniOvi, HUT, ILVA & SSSA worked in the field of Data Mining while POLIMI and RWTH were focused in the analysis and metallurgical aspects of formation and growth of inclusions. It was decided to follow well contrasted methodologies to make verifiable and comparable the results. In this case, CRISP/DM was the most common methodology and it was adopted as the standard in the project for Data Mining development. WORK PACKAGE 2 –Data analysis and processing. Task 2.1: Data pre-processing. Selection of the techniques for data pre-processing and development of specific ones, such as outlier detection method, blank analysis and filling, variable transformation and first selection were done. The Data bases, according to the parameters selected in WP1, were extracted and data prepared for modelling. ArcelorMittal España and UniOvi performed a pre-processing stage and applied dimension reduction techniques due to the high amount of input variables. SSSA developed a specific outlier detection method to set-up the final database to be used for model construction in WP4. Moreover, a new variable “Inclusion Index” IIND was built combining selected variables at pre-processing. POLIMI compiled the thermodynamics and kinetics aspects of inclusion formation and growth in which the model is based. Task 2.2: Collection of the experts’ knowledge. Expert’s knowledge was the base for model design in each case, defining input variables selection and results validation. The outcome of this task was used for other tasks fulfilment. Data Mining partners kept a continuous contact with the steel makers along the project. UniOvi introduced an hybrid system containing a previous “Knowledge based step” to include the information from ArcelorMittal España experts. ILVA evaluated the reliability of Parsytec data to be used as input in modelling comparing the cases in which the first evaluation of Parsytec is confirmed as inclusion by SEM analysis. RWTH carried out a theoretical study on inclusions nucleation and growth. Task 2.3: Data Analysis and Predictor Design. The final databases for model development were built and the final modelling techniques selected. The relation between casting disturbances and Parsytec data were investigated by HUT. The thermodynamic model is available to be tested in WP3 with project partner’s data. WORK PACKAGE 3 –Sampling campaign and analysis. First trials with the model and first conclusions. Task 3.1: Analysis of samples. In order to get knowledge on inclusion evolution through the steel making process, several sampling campaigns on specific grades and routes were defined and carried out. For sampling at liquid steel

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process lollipop samples were used, but for slabs samples specific sampling criteria were defined taking into account casting direction and probable inclusions distribution. Samples obtained at ArcelorMittal España were sent to RWTH Aachen University for inclusions analysis. On first tests done by ILVA, it was proved that PDA can be used to discriminate inclusions from different ladle treatments and provide information on process evolution. Task 3.2: Identifying the type and quantity of inclusions. For samples analysis, OES-PDA, SEM and thermodynamics calculations were used. Similar results were obtained by each partner analyzing similar steel grades. The testing of the thermodynamic model with partner’s data gave successful results in predicting the inclusions present in selected steels. WORK PACKAGE 4 – Final model development. Testing and tuning. Task 4.1: Development of the model. This task is devoted to model development based in Data Analysis (ArcelorMittal España & UniOvi, SSSA, HUT). The results of the predictors are given in terms of the following parameters: True Negative Rate, False Negative Rate, True Positive Rate, False Positive Rate and Accuracy. UniOvi and ArcelorMittal España obtained a good approximation to the problem by means of data analysis techniques. SSSA and HUT models achieved no so good results. Low reliability of Parsytec detections as inclusions (confirmed by checking Parsytec results with SEM investigations) was identified as the main reason of the unsatisfactory results. Task 4.2: Testing and tuning. The developed models were tested and tuned in order to select the best modeling parameters and to improve their performance. WORK PACKAGE 5 – Final inclusion modelling implementation Task 5.1: User interface and integration. The user interface in order to integrate the developed models at the workshop general application were designed and developed in the cases where results were good enough to be applicable at the steel-plants. Task 5.2: Installation and final tuning. SSSA developed a software interface for internal use of the models. Polimi developed a software integrated in a steel making plant tested in Arvedi. UniOvi implemented its model for tinplate steels on a computer for ArcelorMittal as it will be used for What-If simulations. WORK PACKAGE 6 – To report the results according to the commission rules Task 6.1: Preparation and presentation of progress and final reports. All reports were submitted and approved by the TGS2. Present report is a new version of final report describing the new developments as well as explaining in more detail the characteristics of the global work according with the remarks of reviewers related to the draft final report presented in the TGS2 meeting held on May 27, 2009.

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OVERALL CONCLUSIONS Two approaches of inclusion prediction in steel were carried out in this project, one based in data analysis techniques and the other in the study of the thermodynamics and kinetics of inclusions formation and growth in steel. From the kinetic and thermodynamic point of view, POLIMI produced a model that proved very good results in predicting inclusions content distribution when tested with industrial data, mainly in clean steels and applicable to different steel making routes. The work done by RWTH with ArcelorMittal España samples allowed to characterize their inclusion content. The results found by thermodynamics calculations match the results of the inclusion analysis done by ILVA during CAB treatment. Data Based modelling was done with different approaches, that can be divided in two depending on the way to represent the cleanliness of the steel. - Those based in Parsytec data, developed by SSSA and HUT and - Models based on direct information of the steel, selected by ArcelorMittal España and UniOvi. First approach provides an advantage as more information is available, important aspect for generalization, but information is of worse quality. In the second approach, information from the tests is more accurate but it is necessarily reduced in number. In order to feed the developed models, sampling campaigns and analysis were carried out. In both cases, pre-processing techniques were applied to get a final data set for model development. After a complex task of data preparation, the most significant variables affecting the inclusion content in selected steel grades for the model developed by UniOvi and ArcelorMittal España were defined. For the cases where Parsytec data were used as target values to develop the model, the results were not good enough. For HUT case, data were considered at slab level, and also an extension of the slab into slabs segments was done to correlate the associated casting disturbances with Parsytec data. The attempts made by HUT to correlate Parsytec data and casting disturbances were not enough accurate to be implemented. In the case of ILVA and SSSA investigations, data was organized at cast level, considering as defective those heats with at least one defective coil detected in order to increase the number of defective heats in order to get statistically significant figures. This may have led to a misclassification of defective heats. However, some significant process parameters (in particular continuous casting parameters such as mould level variations change of SEN, casting without ladle shroud protection, ladle and tundish change) were considered in the investigation. This is due to the fact that such events have no significance at heat level but only at slab/coil level. It was found that the defects detected by Parsytec are not reliable because they show defects derived from inclusions and scale (iron oxide) being only the former somehow linked with steelmaking process parameters. Further interpretation of the data provided by Parsytec must be done in order to correctly be used for detecting and classifying inclusions. ILVA analyses demonstrate the existence of more than 60% of information on images not directly linked to endogenous inclusions. Exogenous inclusions are usually correctly classified. UniOvi and ArcelorMittal España worked to develop hybrid models based on data mining techniques for two different steel grades selected after discussions with the steel plant experts. Results show that it is possible to determine a range of higher probability of inclusions formation from process and steel data previously to continuous casting. As the number of data is limited, modelling was applied to two types of steel: ULC and tinplate. Model is based in hybrid techniques including expert rules and Data Based methods. As results were promising a user interface was developed to be used as a “What-If” simulation tool. The transformation of the model to analytic equations - capable to be directly tuned by technicians - has been a key factor to improve its acceptability and capacity to be understood and interpreted by metallurgists. All the models were advised by the two industrial partners providing their expert knowledge to guide the development. Cross testing of similar data was done by UniOvi/ArcelorMittal España and Polimi showing the possibilities of hybrid techniques to even improve the results of traditional approaches

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.

OVERALL POSIBILITIES FOR EXPLOITATION The generic information about the most significant variables affecting the steel quality during the refining and continuous casting can be extracted from the work done by UniOvi / ArcelorMittal España to develop the ULC and Tinplates grades model and it is directly exportable to similar facilities with the same production characteristics. Cross tests from 4 different steel making companies were done by three partners to demonstrate this capability of extension. Anyway, different characteristics of the installation could introduce new affections, so models must be retuned before being applied elsewhere. This correction can be easily done directly by the technicians using traditional coefficients on the parameters. The confusing information provided by Parsytec did not allow integrating the results obtained with the models developed by HUT and ILVA / SSSA for industrial operation. However, the specific developed techniques for data pre-processing are available to continue with future works on this subject including new process parameters and classification criteria. Also methodologies for data analysis models and pre-processing stages have proven their goodness and reliability. The model developed by ArcelorMittal España and UniOvi represents a good tool for a first indication about the danger of reaching problematic inclusional situations. The model developed by Polimi works only on a little group of data that allows to follow also the time evolution of the situation (nucleation, growth and floatation of the non metallic inclusions) and so it can add the indication about the chemical composition and the final statistical distribution of the non metallic inclusions. Therefore, both models complement each other as aimed. The analytic expression of hybrid models developed by UniOvi increases the acceptance by the metallurgists as they lose the concept of black box of their techniques, becoming easier to understand and ensuring the real exploitation. Concerning the samples analysis work, the SEM based methodology assessed to carry out a reliable characterization of inclusion population is currently adopted for process investigation and products characterization at the ILVA site. User interface as well as the integration methodology and layout are directly extensible to other plants in the steel industry.

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PREDINC: PREDICTION CHARACTERISTICS

OF

INCLUSIONS

IN

SLABS

FROM

THE

PROCESS

ArcelorMittal España RFCS Contract No. RFSR-CT-2005-00006 Final Technical Report for the Period 1 July 2005 – 31 December 2008 SCIENTIFIC AND TECHNICAL DESCRIPTION OF THE RESULTS The main objective of the project was the development of a system capable to determinate, from the production step the quality assurance of the steel attending to inclusions. The following deliverables were envisaged: -

The functional and technical specifications of the model and the selection of the most suitable modelling technique based on pre-processing results. - The model for inclusions prediction - The conclusions about the model accuracy after testing and tuning for its integration at the plant - The Specifications of interface and integration with the process of the steel plant. - Detailed information about the evolution of non-metallic inclusions from ladle metallurgy to solidification. All results were successfully found considering the global point of view of the project. The parallelisation of modelling tools has allowed creating successful models tested in different environments. WORK PACKAGE 1: – Data gathering and study of the different methodologies for on-line assessment of steel cleanliness. Objectives − − −

To provide a state of the art on the different existing techniques for on-line and off-line control of the inclusions. To define a common strategy between the partners in order to collect data for the subsequent work of data analysis and model development. To choose the viable creation of models. Selection of the best technology using classical methods, Sammon projections, unsupervised neural networks and algorithms.

Comparison of initially planned activities and work accomplished All tasks were completed according to the original plan. Description of activities and discussion Task 1.1: State of the art At the beginning of the project, a literature survey was done on two subjects: inclusions formation and avoidance in steel and modelling techniques. Since all partners were involved in this task, the distribution of subjects shown in Figure 2 was done. Appendix 1 contains a summary of the information compiled. Bibliography was the base of the rest of the tasks of the project.

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PREDINC

INCLUSIONS

FORMATION & AVOIDANCE

DATA MINING

EVALUATION

METHODOLOGIES

ILVA

ILVA

ArcelorMittal

RWTH

HUT

ArcelorMittal

POLIMI

POLIMI

PRE-PROCESSING

UNIOVI

MODELING & INTERFACE

SSSA

SSSA

UNIOVI

HUT UNIOVI

Figure 2: Literature survey topics distribution

Task 1.2: Study of methodology and selection of parameters. Work done in the frame of this task comprises: -

the definition of the most important process and product quality parameters which have influence on the formation of micro/macro inclusions

-

the description of the techniques used for inclusion evaluation and the completion of the first stages of model development.

Methodology The project main objective is to develop different solutions to get the best one for prediction inclusion. As there are different teams developing models to be compared, it was decided to adopt the CRISP-DM methodology (Annex 2), as uniform tool for development of data mining models. Also results of classification will be given in the same measures: % of success for continuous values and confusion matrixes for classification (Annex 3.3). Definition of the main process and quality parameters ArcelorMittal España, HUT and ILVA have available data bases from the LDA steel shop in Avilés, Ruukki Raahe Steel Works and Taranto Works respectively. Anyway data is stored in different formats and levels, so work was done to gather all the necessary data to create the data sets for modelling. That is especially important for the data based models. ILVA focused on the production of Taranto Works, one of the largest integrated steelmaking plants in Europe. The overall yearly output is roughly 9 Mt of crude steel. Steel is manufactured in two steelmaking shops (namely ACC1 and ACC2) by means of LD process. The main features of the available devices in the iron making / steelmaking areas are shown in Figure 3. The three available process routes in secondary metallurgy are: - Ar bubbling - RH-OB (vacuum degassing unit with oxygen blowing) - CAB (Ca treatment by Ca-Si powder injection) The route selected depends on steel grade. In few and special cases, combination of RH and CAB are foreseen. The main families of grades produced and the relevant secondary metallurgy routes are: - Deep drawing – low C (Ar bubbling route) - Ultra Low Carbon (ULC) Interstitial Free (IF) steels (RH-OB route)

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-

HSS (CAB route)

Only the first two routes (Ar bubbling and RH-OB) have been included in the database since they are used for the production of coils, while the CAB route is only used for production of plates.

Figure 3: Main features of iron-making and steelmaking devices available in Taranto Works

The definition of the most important parameters was done in agreement with the experts of each site. These parameters include among others: chemical composition of the steel, tapping conditions, secondary steelmaking parameters (temperatures, time of treatment, amount and type of additions…), casting speed, tundish level, special events occurred during casting (alumina detection, oxygen opening of the SEN, change of tundish,… ), slab dimensions and classification of defects from Parsytec or from laboratory tests. Each partner made its own selection of parameters, but at the same time discussions were held in order to exchange experience and knowledge to improve the model development stage and to easy up the installation of the developed models in steel plants and making them capable to work with real conditions. UniOvi & ArcelorMittal España, made variables pre-selection by means of meetings with experts in the steel plant and in the frame of the first CRISP-DM methodology stage (Data Understanding). Although in ArcelorMittal España there are three treatments of secondary metallurgy: RH, CAS-B and Injection, the experience has shown that the inclusions are more critical in the steel obtained in the RH treatment than in the others. Besides this, among the different qualities of steel that are produced in this treatment modelling efforts were focused to ULC and Tinplate quality. For this analysis, data from BOF, secondary metallurgy, continuous casting and slab yard were analyzed. These original data base contained 108 different tables with a total of 1636 input variables. Data included general information, such as the heat number used as key data, the number of tundish in which the heat is cast, and more specific information such as the chemical composition of steel. HUT made the definition of the most important process and product quality parameters, which have influence on the formation of macro-inclusions, together with the experts at Ruukki Raahe Steel Works. The parameters come from several sources: secondary steelmaking, casting, casting disturbances, steel composition and Parsytec quality inspection system. Selected parameters are described in Appendix 2. Process and rolled product quality data were collected by Ruukki Raahe Steel Works, Finland. The data were delivered to HUT to be used in the development of the model for predicting inclusions in the slabs. Process data were from the casting machines CCM4, CCM5 and CCM6. Casters No. 4 and No. 5

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are curved slab casters equipped with a curved mould. Caster No. 6 is a vertical-bending caster with a straight mould. Rolled product quality data originate from the hot strip mill Parsytec system. The data were collected from the time period of November 2003 to March 2006. The basic database contained over 40 000 heats. Politecnico di Milano defined and measured with ISP Arvedi the fundamental aspects involved in the genesis, growth and motion of the non metallic inclusions within the melt: oxygen activity after tapping, carbon concentration after tapping, chemical analysis of the steel melt at the beginning of the refining period, at the end of the ladle furnace period and within the tundish, final overall oxygen content of the steel bath, measurements of the Ar rate flow producing the circulation of the steel within the ladle, temperature measurement of the melt at the tapping, at the beginning of the refining period, at the end of the ladle furnace period and within the tundish. The experimental measurements of such variables and the empirical relations stated with the observed inclusion content pointed out that these factors of influence are fundamental aspects for the prediction of inclusion features. Representation of the cleanliness The comparison of the different methods for analysing and detecting inclusions - made by RWTH – concludes that no single method exists to obtain enough information of the steel cleanness. But in fact the combination of some of these methods enables a good entire assessment of steel cleanness. Some of the methods are more applicable to determine micro inclusions and some are more to detect macro inclusions. For example, the combination of metallographic microscope analysis, which gives valuable information on the size distribution and amount of micro inclusions, and SEM/EPMA analysis, which reveals the three-dimensional morphology and the composition of micro- and macro inclusions, together with Slime method, covers the complete range of inclusion evaluation. A reduced set of sample analysis was taken with data from ArcelorMittal España by RWTH for model testing but it is not possible to produce a complete analysis of each slab. So cleanliness must be represented by a simpler variable set. There are two main approaches for output representation: Huge amount of data based on direct line measures (Parsytec) and smaller sets based in more certain lab information. The selection of different methods has decreased the risk of the project ensuring that final modelling is possible if any of the different approaches is valid but it also made possible to discover errors in the interpretation of usual techniques as Parsytec, as it will be shown later in this report. Description the techniques for modelling Detailed information on techniques for inclusion analysis is contained in Appendix 1. For the development of the model done by UniOvi and ArcelorMittal España, the CRISP- DM (Standard Cross-Industry for Process Data Mining) has been followed (Appendix 1). Data mining techniques selected to develop the model have been: - Classical statistical methods - ROC curves 0[2] - Projections (PCA, CCA, Sammon) - Unsupervised neural networks SOM (self-organized maps) [3] [4] - APIMARS algorithm [5] UniOvi used hybrid models, combining these techniques with expert rules. Conclusions A literature survey both on inclusions formation and evaluation in steel making and on Data Mining Techniques was carried out at the beginning of the project reflecting the state of the art as the base of project development. Data from 4 companies, 5 steel making plants has been collected and predefined from the expert knowledge. Three different approaches are used by four partners working in parallel to find the best solution. Data from analysis and Parsytec will be used in different models. The main parameters to be taken into account for modelling and the techniques to develop a Data Mining project were selected.

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Moreover, the techniques to carry out the inclusions assessment in WP3 were selected too. Exploitation The results of this work package are exploitable mainly through the other work packages that make use of the results obtained in it. WORK PACKAGE 2 – Data analysis and processing Objectives − To develop strategies and software to remove outliers from the database − To collect the technical personnel experience − To point out the factors those mostly affect the steel cleanness. Comparison of initially planned activities and work accomplished All tasks were completed according to the original plan. Description of activities and discussion Task 2.1: Data pre-processing. This task refers to data gathering and input variables selection. Different techniques were used or developed by partners involved in it. ArcelorMittal España & UniOvi collaborated in the development of a model focused on two steel qualities: ULC and Tinplate. During the pre-processing stage data were prepared (blank spaces filling, outliers elimination, etc.) and their dimensionality reduced due to the high amount of available input variables. It corresponds to steps 3 and 4 in CRISP-DM methodologies (Data understanding and Data Pre-processing) i. Data preparation The objective of this step is to prepare the data for the modelling process. For that, it is necessary to produce a computable data set. The main works involved in this task were: Filling in the blank spaces, using statistical methods like mean, moving average or mode. Transformation of non-numerical parameters in numeric variables using Boolean, binary or a modification of the binary transformation called Dummy transformation Removing outliers using two different techniques: - Statistical values of the parameters (values of centralization, position, dispersion and shape) - Projection methods, like PCA, CCA and Sammon, which allow the visualization of the data set in a two-dimensional space. An example of the results is shown in Appendix 2. ii. Dimensionality reduction Given the high number of input variables, a dimensionality reduction has been required, by means of iterative pruning of variables. The aim is to improve the speed of the learning process, the generalization capacity and the simplicity of representation. Therefore, the results will be better understood, the data storage volume will be smaller, the generated noises of the less important parameters will be reduced and the useless knowledge will be eliminated. Thus, it is necessary to look for the variables with major influence over the output. The elimination of the less important variables was done using three different techniques (Figure 53- Appendix 2): - The receiving operating characteristic (ROC). -

Self organizing map (SOM) that uses neuronal networks to find the similarities among variables.

-

APIMARS, explained in Appendix 2.

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ILVA activity has been addressed to assemble the database from the so called “coil card”, an existing tool giving the metallurgical history of each coil, starting from steelmaking up to cold rolling inspection. When this tool has been developed, its aim was just to provide information without any possibility of processing data. In the frame of this project, the information available from the “coil card” has been transferred into a new database suitable for elaboration. This task was performed at different times by SSSA in cooperation with ILVA Technical Staff. SSSA worked with three different databases, all with data from the ILVA plant: - Database from Taranto works, containing parameters of 121667 casts and where casts affected by inclusions are marked with the code “A01”. - As the data belonging to the previous database were the result of measurement methods which were no longer applied, a new database was collected from the Novi Ligure plant in order to make the model developed for the former compatible with the new evaluation methods (Appendix 2) - Data from Novi, with the same variables as the second and new heats. The whole database runs from January the 01st 2006 to March the 31st, 2008 and contains 73024 casts. For each data set, work was done concerning pre-processing. DB 1: the first treatment applied to data was to create a matrix, where each row is related to a single cast, while each column refers to a single measured variable, a part from the last column containing a binary code (0/1) representing the absence/presence of inclusions (code “A01”). An outlier detection method was developed (see Appendix 2), and a correlation analysis was done. After this, four variables (Mn, Al, O2, C) have been extracted among the variables which are more correlated to the presence of inclusions and a dataset containing 40.000 samples has been prepared by selecting such variables from the original database. An amount of 822 samples of such dataset contain inclusions (about 2%). Finally data went through a Low Pass filter and Fuzzy system (See Appendix 3) DB 2: The following process variables are considered as interesting for the prediction of inclusions occurrence: Aluminium [kg], Oxygen blown [nm3], Reheating Temperature, Oxygen in ppm measured at the treatment, duration of treatment [min], Silicon variation (difference between Si during continuous cast and Si in ladle), Nitrogen variation, Phosphorous variation, oxygen at the converter [Nm3], mean casting speed [cm/min], Tapping temperature, ladle Temperature, end treatment temperature, Tundish Temperature. First, the available data were grouped in three different clusters on the basis of the employed treatments: - Mandatory RH treatment. In this group there were 1991 casts that underwent a RH treatment and 28 of them resulted defective. - Reheating treatment. In this group there were 10892 casts that underwent a reheating treatment at a temperature greater than 10ºC and 105 of them result defective - Direct cycle. This group included 526 casts that didn’t undergo any treatment (besides the stirring treatment that is used in each cast) and went from converter directly to continuous casting. 7 casts of them resulted defective. Pre-processing and filtering operations were performed on the data in order to eliminate unreliable values. Afterwards a statistical analysis was performed [6] using defective-distribution calculation preprocessing technique which was not used for DB1 (Appendix 2). After applying it, four variables were selected: Nitrogen variation, Reheating Temperature, Tundish Temperature and Oxygen blown as most representative. These, were turned into new variables that can assume integer values from one to three. Four new variables PN, R, TT and OC were recalculated following the rules contained in Appendix 2. And a new variable called “inclusion index” (IIND) was defined as follows: IIND= PN * R * TT * OC.

15

(4)

According to the experience (Task 2.2), this index should increase in the case of defective heats. The percentage of defective heats for each class of IIND has been calculated and plotted in Figure 4: Percentage of defectiveness vs. Inclusion Index. From this figure an increase of the percent of defective heats is seen for lower IIND values. This is the opposite with respect to the expected trend.

Figure 4: Percentage of defectiveness vs. Inclusion Index

The red circle shows that for lower values of IIND, the % of heats with inclusions increases. This conclusion, which is in conflict with common experience, seems to be due to a probabilistic effect because in this zone the highest number of heats is concentrated. DB3: Results are presented in WP4, since this Data Base is the one used for final model development. POLIMI developed a model with data from a subcontractor, Arvedi. A general model has been developed on the basis of the chemical-physical laws which rule the factors identified with influence on inclusions formation and the movements of the non-metallic inclusions as a function of their chemical composition, growth rate, size. This model involves thermodynamic and kinetic aspects. - Thermodynamic aspects associated to: - Definition of the type of the formed non-metallic inclusions as a function of the addition of the deoxidizing elements (Al, Si) and modifiers of the non-metallic inclusions (Ca) (precisely defined in the 1st Report); - Definition of the excess concentration of oxygen and carbon with respect to the thermodynamic equilibrium; - Kinetic aspects associated to: - Formation rate of CO; - Growth rate of non-metallic inclusions controlled by the collision among the non-metallic compounds, the diffusion of the chemical elements around the formed non-metallic compounds, the turbulence energy introduced into the metal bath by the rate of Arstirring and the time o its application; - Efficiency in the floatation of the non-metallic inclusions as a function of Ar-stirring, main geometric characteristics of LF and Tundish. Task 2.2: Collection of the experts’ knowledge. Although ArcelorMittal España had no specific man power allocated to this task, meetings of UniOvi with experts at the LDA steel plant were continuously held to select input variables to the model and to validate the results. SSSA also validated intermediate modelling results with the knowledge existing on inclusion formation. ILVA made work to assess the reliability of data got from Parsytec which is described in the Appendix 2. Work undertaken by RWTH Aachen University concerns thermodynamic and kinetic aspects of inclusions formation; detailed information is contained in Appendix 1. HUT took information from experts of Ruuki and Polimi from Arvedi. Meetings were very useful also for contrasting results and conclusions through project development.

16

Task 2.3: Data Analysis and Predictor Design. For ArcelorMittal España, the decision of the model design was done after meetings with the experts in the steel plant (task 2.2) deciding to focus modelling efforts on two steel qualities: Ultra Low Carbon Steel (ULC) and Tinplate. Moreover the sampling campaigns described in WP3 cover also these qualities. On the other hand, a review of the variables available in the LDA steel plant process database, as well as the expert selection on main parameters affecting inclusions, provided a list of parameters to be recorded for Data Analysis. The model will be based on Data Mining techniques, being the selected techniques described in Appendix 2 of this report. Variables preselected include HIM, Heating, Argon inside the nozzle, Time of inclusions cleaning, Temperature of water, Waiting time, Average speed, Weight of the shell, High variation of level, Aluminum fall during casting, Low weight in tundish, Low speed of casting, Negative strip, Segregation index, Volume in superior sheet, Oxygen volume in west nozzle, Snorkel life, Scrap iron, Heating time, Ladle weight, Temperature in segment # 17, Deviation in the refrigeration of the segment 17, Difference of temperature in narrow faces, Maximum velocity, Variables are pruned according to an iterative scheme, as shown in the following figure.

Figure 5: Variable selection by UniOvi

Random samples of 85% of data are used to train a MARS model which provides a variable rank where those adding less than 5% value are discarded. After deleting them from the pool, SOM are redone and new pool of variables is selected repeating the process until the number of variables is reduced according to the data available. SSSA proposed the structure shown in Figure 6 for the predictor to be developed. Next intermediate results on the data groups managed are given. Correlation analysis

Low pass filter

FUZZY SYSTEM

SOM

Threshold decisor

Figure 6: Predictor scheme of SSSA

DB 1: The predictor developed by SSSA for DB1 was based in SOM algorithm. From the 40.000 samples (after correlation) several groups of 5000 were extracted, that represented two different Training Set and five different Validation Sets. The prediction was done training the network for 50 epochs by varying nodes and thresholds. The threshold was chosen by considering the ratio between the number of inclusions found in a cluster and 2%. By varying Training Sets and Validations Sets, the optimal nodes and threshold have been calculated and the performance have been evaluated by considering the number of correct predictions, false alarms

17

(data referring to products without inclusions which are misclassified as containing inclusions) and missed detections (data referring to products with inclusions which are misclassified as not containing inclusions). DB 2: The prediction is made similar to DB1 (SOM algorithm, network trained with 50 epochs by varying the network dimension and thresholds). The threshold was varied in the range 1 – 2.4. The network dimension can be varied within a limit given by the number of data available in the training set, i.e. usually the number of the available data should be at least 4 times greater than the number of free parameters of the network. By varying these parameters and the selected variables several trials were performed, where the percentages of correctly classified data, false alarms and missed detections were evaluated. The obtained results are still not enough satisfactory. DB3: Results are given in WP4. In order also to develop a predictor based in Data Analysis, HUT built a database and made a preprocessing stage consisting in analyzing quality data (casting and hot rolled – Parsytec). i. Construction of basic database. The database contains data from the following sources: (Appendix 2) Converter data, Stirring station data, Steel chemical analysis data, Casting data, Casting disturbance data, Rolled product quality data (Parsytec). ii. Data description. The main parameters for the model have been selected together with the experts of Ruukki. A preliminary data analysis has been performed for the data set. There exist two main parameters in the data set, which have been taken into the detailed examination; casting disturbances and rolled product quality value. Casting disturbances are measured automatically or entered manually into the process automation system. The rolled product quality value is obtained from the Parsytec system and needs a careful analysis, as well. Casting disturbances have a significant influence on the occurrence of exogenous inclusions in the slabs. Disturbance parameters cover 41 different cases, which are coded into the database. 27 of them are entered manually into the automation system while 14 are saved automatically. Disturbance duration is saved as a function of the cast length within a sequence. A critical task will be to correctly locate the disturbance data with the slab segments. Especially manually entered disturbances need a careful examination. Parsytec quality parameters are the other interesting items to be analyzed in a more detailed way. These parameters contain classified defects from the hot strip mill Parsytec system (data stored since 9/2003). Inclusion defects from the Parsytec system are most probably the mid range defects: sliver, big sliver and inclusion line. Parsytec defect data have also the same locating problem than the casting disturbance data. For example data of extra cuttings of a slab or a strip are not saved into the automation system. Therefore, the correct locating of defects related to the slab segments is very important when expecting good predicting results of the model. iii. Analysis between casting disturbances and Parsytec defects- Casting disturbance data and Parsytec quality data have been analyzed in a more detailed way. In the database the percentage of heats where exist no disturbance is 60 %. In the 40 % of heats there exists at least one casting disturbance. Parsytec defects, which are originated in the smelting shop, have been observed in the 75 % of heats while the rest 25 % of heats have been free of Parsytec defects. All disturbances have been correctly attached to slabs and furthermore to slab segments. The length of one slab segment is 0.5 m. The most common casting disturbances occurring in 30000 heats were: - Automatic mould level control not in use - Low level at tundish - Last slab of the casting sequence - Casting speed: < 0.7 m/min - Big single mould level fluctuation Delays in process and manually entered disturbances bring an additional difficulty in finding the correlation between casting disturbances and Parsytec defects. Because of this, the disturbance area has

18

been expanded in the analysis. Parsytec defects are not only analyzed from the casting disturbance area, but also before and after the actual area (±2 m and ±4 m). Detailed information about the data analysis can be found in the Appendix 2. iv. Self-Organising Map (SOM) analysis of casting disturbances/Parsytec defects A new database has been created for the analysis of relations between casting disturbances and Parsytec defects. Casting disturbance data were coded as binary vectors, where every element represents a certain disturbance type (0=NoDisturbance, 1=Disturbance). A Self-Organising Map (SOM) analysis was performed for the binary coded database. The SOM was trained using only the binary values of casting disturbances and the corresponding defect class values were left out. The aim of this analysis was to find out the influence of different combinations of disturbances into the existence of detected Parsytec defects. The SOM analysis did not show any clear correlations between casting disturbances and defects (more in Appendix 2). Concerning the Model developed by POLIMI, variables implied are Total weight, Tapping Temperature, Oxygen at the tapping, Carbon content at tapping, Delaying time after de-carburation, LF average temperature treatment, LF useful height, LF treatment, Argon flow rate in ladle furnace or Argon stirring time. Pre-processing is simpler as it is a physical model and data is used only for testing. Development of the physical model Data mining models are indicated for being used when physical modelling cannot be complete due to the difficulties to model all the interactions, unknown internal procedures, difficulty of measurements, etc. AI techniques complement avoids the problems of the simplifications needed for resolution of Finite Differences. So, Physical modelling will be the base of comparison, the base level of the rest of the models. A complete thermodynamic model has been developed by POLIMI. Later this models is contrasted with the rest of them in order to validate them (WP4 and 5). All the problems involving the differential equations were solved through iterative procedure based on classical finite difference technique. Next description of the two parts of the model (thermodynamic and kinetic) is presented, but further details are given in Appendix 2. Thermodynamic model The computational procedure followed by the model can be briefly described through several steps: - the species produced by each reaction has been taken with an activity of 1, because this condition grants the formation of the produced phase as pure and separated from the steel, although the produced phase stays within the volume of the steel bath; - the activity of the reactants has been computed through the chemical composition of the steel and through the Wagner formalism exploiting the activity coefficient and the interaction ones (Appendix 2 – A2.4); - the oxygen activity associated with the slag composition has been computed through ( Appendix 2 – A2.1 & A2.2) and compared to the ones associated with each specific reaction at a particular temperature; - the reactions featured by an equilibrium oxygen activity lower than the one imposed by the slag has been considered as possible and their development is allowed by the presence of an available thermodynamic driving force:

∆G = RT ln -

-

(aO ) slag [a O ]reaction



(3.10)

[ Si ] + 2[Ca ] + 4[O ] →< SiO 2 .2CaO >

(3.11)

[ Si ] + 3[Ca ] + 5[O ] →< SiO 2 .3CaO >

(3.12)

2[ Si ] + 3[Ca ] + 7[O ] →< 2 SiO 2 .3CaO >

(3.13)

2[ Si ]+ < Al 2 O3 > +[Ca ] + 5[O] →< 2 SiO2 .CaO. Al 2 O3 >

(3.14)

[ Si ]+ < Al 2 O3 > +2[Ca ] + 4[O] →< SiO2 .2CaO. Al 2 O3 >

(3.15)

2 < SiO2 > + < Al 2 O3 > +[Ca ] + 4[O] →< 2 SiO2 .CaO. Al 2 O3 >

(3.16)

< SiO2 > + < Al 2 O3 > +2[Ca ] + 2[O] →< SiO2 .2CaO. Al 2 O3 >

(3.17)

< SiO2 > +2[ Al ] + 3[O] →< 2 SiO2 .3 Al 2 O3 >

(3.18)

< SiO 2 > +[Ca ] +[O ] →< SiO 2 .CaO >

(3.19)

< SiO 2 > +2[Ca ] + 2[O ] →< SiO 2 .2CaO >

(3.20)

< SiO 2 > +3[Ca ] + 3[O ] →< SiO 2 .3CaO >

(3.21)

2 < SiO 2 > +3[Ca ] + 3[O ] →< 2 SiO 2 .3CaO >

(3.22)

< MgO > +2[ Al ] + 3[O] →< MgO. Al 2 O3 >

(3.23)

[ Mg ] + 2[ Al ] + 4[O] →< MgO. Al 2 O3 >

(3.24)

< MgO > +2[ Al ] + 3[O] →< MgO. Al 2 O3 >

(3.25)

and each variation of the Gibbs free energy associated to their development has been computed on the basis of the Gibbs free energy of each chemical species involved in the reactions (Table 32). Then, the constants of equilibrium of each reaction have been computed by the variation of the Gibbs free energy determined through the difference among the energies of the right side compounds and the left side ones. Chemical species

G0 (J/mol)

Si

-58.9T+30004

Ca

-99.2T+44799

Al

-84.2T+42591

Mg

-136T+98253

O

-145.2T-76028

CaO

-117.6T-580918

SiO2

-134.5T-848440

Al2O3

-235.2T-1539859

3CaO.Al2O3

-601T-3312118

CaO.2Al2O3

-586T-3710542

12CaO.7Al2O3

-3173T-17894624

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2SiO2.Al2O3

-523.3T-2946164

2SiO2.3Al2O3

-992.6T-6273164

SiO2.CaO

-256.4T-1463400

SiO2.2CaO

-364.2T-2069624

SiO2.3CaO

-507.5T-2643282

2SiO2.3CaO

-628.6T-3533520

2SiO2.CaO.Al2O3

-636T-3913789

SiO2.2CaO.Al2O3

-612T-3700167

3SiO2.3CaO.Al2O3

-911T-6187916

SiO2.CaO.Al2O3

-479T-3063983

MgO

-235.2T-1539859

MgO.Al2O3

-326T-2139278

Table 32: Gibbs free energy at standard states for chemical specimens included in the studied system.

The activity of each alloying element present in solution has been obtained through the Wagner formalism [16] for a system including Fe-Si-Mg-Al-Ca-O:

a B = γ B0 X B + X B exp(ε BB X B + ε BC X c + .... + ε Bn X n )

(4)

while the equilibrium activity of the oxygen for each reaction has been evaluated through the associated constant of equilibrium. The activities of Al2O3, SiO2 involved as reactants in the reactions have been taken with the values computed in reactions (3.2) (3.3) at the beginning of the LF treatment, provided the measured oxygen activity and the computed activities of Al and Si. The MgO has been considered as a pure phase, because it is present in this state in the refractory lining of dolomite and magnesia, so its activity has been stated at 1. Kinetic section of the developed model involving mass transport and the associated phenomena Type of the non-metallic inclusions The pattern for the formation of the type of non-metallic inclusions has been detailed within the 1st report and it has been also analysed and evaluated through a peer review procedure for the publication on Steel Research International (in the paper the financial support of the Commission has been pointed out). The model is based on the minimization of the oxygen potential featuring the different possible chemical reaction. Oxygen removal by the formation of CO after tapping The experimental observations performed on the Arvedi plant have pointed out that the CO effervescence performed by a delayed introduction of the deoxidizing element can allow to remove a quantity of oxygen and so to decrease the overall quantity of the oxide non-metallic inclusions. The generalization of the empirical relation found for the specific plant of Arvedi has been based on the application of a consistent chemical physical relation for the definition of the CO formation rate per unity of reaction surface.    pCO RT [O]    +  [C] [O] β  [C ] [O] RT RT  βO βC  jCO = O  + + − + + 1+ 4  2  βC  βO βC βCO KCO  βO βC βCOKCO   [C ] [O]   RT  βC KCO  − +      βO βC βCOKCO    

(6)

In square brackets is indicated the molar concentration of the chemical elements, KCO is the equilibrium constant for the formation of CO while β represent the transport coefficients of the indicated chemical species. The comparison among the experimental data and the computational procedure has permitted to state a reliable value of 7*10-3 molm-2s-1 for C and O. The oxygen removal through these reactions

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has to be excluded from the amount which contributes to the formation of the overall final inclusion content. Growth model for the non- metallic inclusions The prediction of the type of the formed non-metallic inclusions has to be associated with an inclusion growth model. The growth model of the predicted type aims at defining the final size distribution of the inclusion population. The importance of this kinetic aspect is due to the influence played by the floatation mechanism for the definition of the final volume fraction of the non-metallic inclusions. The growth of the non-metallic inclusions has been related to the collision mechanisms among the nonmetallic compounds which are ruled by Brownian movement, Turbulence promoted collisions, Stokes collision and Gradient velocity collisions. The frequency collision functions has been proposed by Aachen university as embedded in very sophisticated numerical models which are difficult to be efficiently implemented on industrial scale. Thus, for the construction of the model the collision frequency functions have been preserved in their form but they have been modified in order to point out the average and the standard deviation of the population:

βB =

2 kB T  1  21+  3 µ1  µ + σ 

(7)

where kB is the Boltzman constant, µis the viscosity, µ is the average radius and σ represents the standard deviation of the distribution 1

β T =1.3π 2Ct (2µ + σ )

3

(8)

where Ct is a parameter depending on the turbulent energy

4 3

β G = (2r + σ ) 3

du dy

(9)

Where: du/dy is the velocity gradient in laminar shear zone. The sum of these terms represents the total collision frequency featuring the inclusion population. Under the hypothesis of the model the main role in the growing process is played by the collision phenomena. The collision frequency functions have the physical dimension of m3s-1, thus the multiplication for a time step permits to obtain the increase of the average size featuring the non-metallic inclusions. This is the first step of an iterative procedure, because once the new average size has been computed a new standard deviation can be computed inverting the frequency collision function and taking into account the floatation of the metallic inclusion. Moreover, this permits to incorporate into the model the effect of the Ar stirring in the terms of increasing the size distribution during the process and to estimate the corresponding floatation. The floatation of the non- metallic inclusions implies two effects: - the decreasing of the overall volume fraction of the non-metallic inclusions; - the elimination of the non metallic inclusions featured by the largest sizes reducing the standard deviation to be considered in the growth model during the successive iterations. This floatation velocity (and the related rate of elimination) of each size class of non-metallic inclusions is modelled by an iterative procedure taking into account the kinetic energy induced to the steel by the Ar plume and the balance among the gravitational force (that favours the floatation), the inertial and the viscous frictional forces (that resist to floatation). The new values of the average and standard deviation are inserted in the collisions frequency function and the procedure starts again for a successive time step. RWTH RWTH Aachen University has carried out theoretical work of inclusion generation and growth. The nucleation mechanism as well as the different growth mechanisms of inclusions has been investigated in terms of time dependency. Figure 48 shows an overall overview of nucleation and growth mechanisms depending on time [61]. It is to see, that nucleation and Ostwald-ripening only takes place in a few milliseconds. The growth of particles is determined by different collision mechanisms and

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5

4

these take the most time for inclusion growth in comparison to the nucleation. Furthermore the growth mechanisms are most responsible to form large inclusions in the steel making process.

6

2

2 3 4 5

1

Particle size in µm

3

1

Nucleation and Ostwaldripening Diffusion growth Gradient collision Stokes collision Precipitation of big particles

Time in s

Figure 48: Overview of nucleation and growth mechanism

Concerning nucleation mechanism nuclei of inclusions begin to when ∂G/∂r = 0, which corresponds to a critical radius rc and critical free energy change ∆Gc.

2σVm RT ln S 2 16π σ 3Vm ∆Gc = 3 (RT ln S)2 rc =

σ = Interfacial tension Vm = Molar volume of particle S = Super-saturation of system equal to dimensionless number density of monomers

In the initial stage of de-oxidation process under a homogenous system, a nucleation process is followed by diffusion growth and coarsening (Ostwald-ripening) of inclusions. These processes are followed by particle growth due to different collision mechanisms of particles. These various collision-growth mechanisms are Brownian collision, turbulent collision, Stokes collision and gradient collision [58]. After Zhang and Pluschkell [59] the shape generation of inclusions is derived as follows: When the radius of particles is smaller than 1µm, the particle growth is dominated by diffusion of pseudo-molecules and Brownian collision of particles. The irregular thermal motion is independent of fluid flow and is not directional. The inclusions tend to grow in every direction, leading to a spherical product. When the radius of particles is larger than 2µm, the particle growth is dominated by the turbulent collision of particles. The morphology tends to be more jagged and results in a cluster shape. Thus, sizes of inclusions from 1 to 2µm are thresholds where the growth mechanism of inclusions changes. The basic steps of shape generation of inclusions are illustrated in Figure 49 [59].

82

Brownian scale: r 2µm, fluid flow affecting inclusion growth

Figure 49: Schematic of alumina inclusion formation [58] The inclusions in liquid melt start to grow after nucleation during de-oxidation as a result of different growth mechanisms, including oxygen diffusion, Ostwald-ripening and several collision growth mechanisms. It is certain that different mechanisms play different roles during growth processes. Zhang and Lee [58] have calculated the interrelation of different growth mechanism with a numerical model.

Figure 50: Time evolution of mean particle size and total number density of particles [61]

Figure 50 describes the variation in total number density and mean size of stable Al2O3 particles in molten steel at different stages within a 0-10 min time range. Figure 50 a) and b) compare diffusion growth, Ostwald-ripening and Brownian collision (N-Nucleation; O-Ostwald-ripening; B-Brownian coagulation; T-Turbulence coagulation; S-Stokes coagulation; G-Gradient coagulation). Figure 50 c) and d) compare Brownian collision, turbulent collision, Stokes collision and Gradient collision. The diagrams illustrate the dominating growth mechanism of inclusions at a certain time. Figure 51 shows the time evolution of particle size distribution based on the model of Zhang and Lee.

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Figure 51: Time evolution of particle size distribution.

A2.3

AI MODELS (ARCELORMITTAL/UNIOVI/SSA/HUT)

A2.3.1 DATA PRE-PROCESSING The objective of the Data Pre-processing step is to prepare the data for the modelling process. ArcelorMittal España and UniOvi used, among others, projection methods like PCA, CCA and Sammon in order to reduce input data dimensionality and get a smaller and usable data set. Figure 52: Examples of PCA and CCA projections Figure 52 shows an example of the groups identified by PCA and CCA projections.

Figure 52: Examples of PCA and CCA projections

APIMARS has been used to identify those input variables with a higher influence on the output one. It combines the neuronal network qualities with the adaptive technique: Multivariate Adaptive Regression Splines. This tool allows obtaining correlation maps and weighted sensitivity analysis. Figure 53 compiles the process followed for data pre-processing.

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Figure 53: Techniques employed in the elimination of variables In the following paragraphs, the techniques used by SSSA for data pre-processing are described. Outlier detection method An outlier in a dataset can be defined as an element which is far from the centroid of the distribution of the available data, has few points in its neighbourhood, it is quite far from the other patterns and has a quite low degree of membership to any cluster which can be pointed out within the dataset itself [56]. In order to point out outliers according to the above described criteria, some preliminary operations on the whole data set are required, such as the centroid evaluation (usually represented as the mean vector of the data distribution) and a clustering operation, where each pattern is attributed to one of the clusters. In the developed method, the adopted clustering algorithm is the Fuzzy C-means that is a method of clustering which allows one piece of data to belong to two or more clusters. This method (developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition [57]. For each pattern the following four features are extracted and fed as input of the FIS: - the distance between each element and the centroid of the overall data distribution - the number of elements that are near to the pattern itself. - the mean distance between the considered pattern and the remaining patterns - the degree of membership of the patterns to the cluster to which it has been assigned by the preliminary fuzzy c-means clustering stage.

Figure 54: Block diagram of the overall out-liers detection system

85

The initial data are vectors representing chemical elements concentrations and other interesting variables. In this case the database is supplied by ILVA and includes 21 variables and 121667 observations. The fuzzy logic analysis and control method can be explained as follows: - Receipt of one, or a large number, of measurement or other assessment of conditions existing in some system we wish to analyze or control. -

Processing all these inputs according to human based, fuzzy "If-Then" rules, which can be expressed in plain language words, in combination with traditional non-fuzzy processing.

-

Averaging and weighting the resulting outputs from all the individual rules into one single output decision or signal which provides the output value of interest and thus suitably control the system. The output signal is a precise and de-fuzzed, "crisp" value.

Fuzzy System Fuzzy inference system (FIS) is the process of formulating the mapping from a given input to an output by using fuzzy logic. The mapping then provides a basis from which decisions can be made or pattern discerned. The process of fuzzy inference involves: fuzzy sets defined through the associated membership functions (MF), i.e. some curves that define how much each point in the input space belongs to the different fuzzy sets, through a membership value or degree of membership lying in range [0, 1]; fuzzy logic operators (and, or, not); if-then rules (Task 2.2). In the proposed application, the rules are six and they have a precise syntax: - If (distance from centroid is very high) and (neighbour points are very few) and (degree of membership is low) and (mean distance is high) then (outlier risk is very high). - If (distance from centroid is considerable) and (neighbour points are few) and (degree of membership is quite low) and (mean distance is little) then (outlier risk is quite high) - If (distance from centroid is low) and (neighbour points are considerable) and (degree of membership is quite low ) and (mean distance is very little) then (outlier risk is low ) - If (distance from centroid is considerable) and (neighbour points are very few ) and (degree of membership is quite low ) and (mean distance is little ) then (outlier risk is high) - If (distance from centroid is little) and (neighbour points are few ) and (degree of membership is high) and (mean distance is quite high) then (outlier risk is low ) - If (distance from centroid is little) and (neighbour points are considerable) and (degree of membership is high) and (mean distance is low) then (outlier risk is low). Since decisions are based on the joint application of all of the rules in a Fuzzy Inference System (FIS), the rules must be combined in some manner in order to make decisions. Aggregation is the process by which the fuzzy sets that represents the outputs of each rule are combined into a single fuzzy set. Aggregation only occurs once for each output variable, just prior to the final step, i.e. the so-called defuzzy. The input for the de-fuzzying process is a fuzzy set and the output is a single number. However, the aggregation of a fuzzy set encompasses a range of output values, and therefore must be de-fuzzed in order to resolve a single output value from the set. The output of the developed system, as result of aggregation, is the degree of outlier, which provides a measure of the probability the input pattern is an outlier. For each element, the output (in the range [0;1]) is fed to a threshold operator which classifies each input pattern as an outlier when its probability of outlier exceeds the value 0.7. As the low pass filter, the fuzzy system is used to improve the prediction system and to clean the dataset. Correlation analysis The correlation coefficient is a measure of how well trends in the predicted values follow trends in past actual values. It can be considered also a measure of how well the predicted values from a forecast model "fit" the real-life data. Low pass filter A low-pass filter is a filter that outputs mainly the input signal components lying at low frequencies, while attenuates (or reduces) the components corresponding to frequencies higher than the so-called

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cut-off frequency. The actual amount of attenuation for each frequency varies from filter to filter. The band of the applied low pass filter is chosen as five times the standard deviation. This filter is used to remove casts that have at least one input variable out of band. Defective-distribution distance calculation Besides the analysis of correlation [63], a new parameter was calculated, which represents the distance between the mean value of each variable for defective heats and all population, in order to understand the behaviour of each variable in the defective heat group This distance parameter ∆i is defined as follows: ∆i = {[Xi- Di]/ Xi ]}*100

(3) th

where Xi represents the mean value of the i variable which is evaluated by considering all the casts, while Di is the mean value of the ith variable evaluated only on the defective casts. This approach seemed not to lead to any reasonable conclusion, as most of the variables show trends that are different with respect to what was expected. After an analysis on the histogram representing each variable, four variables have been selected: variation of nitrogen, reheat, Tundish Temperature and Oxygen at the converter. Four new variables PN, R, TT and OC have been recalculated following the rules: - if 0 ≤ PkN ≤ 10 => PN =1 - if 11 ≤ PkN ≤ 20 => PN =2 - if PkN > 20 => PN =3 - if 0 ≤ Reaheat ≤ 20 => R=1 - if 21 ≤ Reaheat ≤ 30 => R=2 - if Reheat > 30 => R=3 - if 0 ≤ Tundish Temperature ≤ 1550 => TT=1 - if 1551 ≤ Tundish Temperature ≤ 1570 => TT=2 - if Tundish Temperature > 1570 => TT=3 - if 0 ≤ O2Cov ≤ 500 => OC = 1 - if 501 ≤ O2Cov ≤ 700 => OC = 2 - if O2Cov > 700 => OC = 3 HUT pre-processing consisted in the following. Construction of basic database The database construction work includes handling of data, which are obtained from several sources of the process. These data are saved with a different accuracy during the process. The most demanding task is to find the temporal and spatial correctness between all process data, casting disturbance data and rolled product quality data. Converter, stirring station and chemical analysis data are heat level data while casting data include both heat and segment level data. The segment data contain an average value of variables in a slab segment of length 0.5 m. Casting disturbance data are saved as a function of the cast length, but the data will be utilized at the segment level. Rolled product quality data will be converted to slab segment level data. Clear outliers have been removed from the data and the selection of heat data has been performed. The two main criteria for selection of heats into the database have been the following: slabs have not been scarfed and the Parsytec system has been on. The data set consisted originally data from 40000 heats and after pre-processing and heat selection procedure, the amount of heats in the database is around 30000.

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The programming work of the data handling procedures is done in the Matlab environment and the data are saved as Matlab files. Analysis between casting disturbances and Parsytec defects Disturbance parameters cover 41 different cases, which are coded into the database. Disturbance duration is saved as a function of the cast length within a sequence. All disturbances have been correctly attached to slabs and furthermore to slab segments. Figure 55 shows the sum of different casting disturbances within 30.000 heats.

5000

36

4500

Sum of casting disturbances

4000

99

14 3500

42 3000

41

2500 2000

11

1500

22 27

1000

18

500

19

97 98

0 0

10

20

30

40

50

60

70

80

90

100

Disturbance code

11 = oxygen opening of the steel ladle

36 = automatic mould level control not in use

14 = low level at tundish

41 = big single mould level fluctuation

18 = steel grade change by changing tundish and using separate plate

42 = casting speed < 0.7 m/min

19 = change of the tundish

97 = dynamic mould level fluctuation

22 = casting without ladle shroud

98 = increased secondary cooling

27 = pumping with the stopper rod

99 = last slab of the casting sequence

Figure 55: Distribution of casting disturbances in the database.

The typical examples of Parsytec defects, sliver and big sliver, are shown in Figure 56. These defect types were taken into account in the analysis. The inclusion line defect was left aside, because Parsytec did not classify this defect sufficiently reliably.

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Figure 56: Images of defects detected by Parsytec system: sliver and big sliver.

Parsytec defect data should also be correctly attached to the slab segments. For example data of extra cuttings of a slab or a strip are not saved into the automation system. Therefore, the correct locating of defects related to the slab segments is very important when expecting good predicting results of the model (Figure 57).

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segment = 22.06 m 300 m block1 hot strip

block2 block3

segment length = strip length/number of segments = 300/(6.8 / 0.5) =22.06 m BLOCK_ID: block1, block3 = edge area, 10 cm

block2 = centre area

10 cm

SIDE_ID: upper/lower slab to be rolled

10 cm

1

2

3

4

5

6

7

8

6.8 m

9

10

11

12 13

14

6.8 m / 0.5 = 13.6 -> 14 segments

0.5 m

Figure 57: Parsytec segments vs. slab segments.

Figure 58 shows different types of examples of relations between the selected process parameters, casting disturbances and Parsytec defects. In Figure 58a mould level has fluctuated in the beginning of the casting. Thus, casting disturbances are seen at the same time and a little bit delayed, also Parsytec defects has been detected. In Figure 58b casting speed has varied a lot and also mould level has fluctuated during the heat. Casting disturbances are registered, but no Parsytec defects. In Figure 58c the process has been in steady-state condition. Casting disturbances were not registered but Parsytec defects existed.

(a)

(b)

(c)

Figure 58: Three examples showing the relations between selected process parameters, casting disturbances and Parsytec defects: a) Disturbances YES, Defects YES, b) Disturbances YES, Defects NO and c) Disturbances NO, Defects YES.

Delays in process and manually entered disturbances bring an additional difficulty in finding the correlation between casting disturbances and Parsytec defects. Because of this, the disturbance area has been expanded in the analysis (Figure 59). Parsytec defects are not only analysed from the casting disturbance area, but also before and after the actual area (±2 m and ±4 m).

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Expanded area slab disturbance

1

2

3

4

5

53

53

6

7

53

8

9

10

53

11

12 13

14

= automatic control of the mould level not in use 53

slab segment = 0.5 m

= sliver

Figure 59: Expanding the duration of a casting disturbance.

Self-Organising Map (SOM) analysis of casting disturbances/Parsytec defects Casting disturbance data were coded as binary vectors, where every element represents a certain disturbance type. The value of an element is set to one, if a disturbance has been existed within a heat. Otherwise the element value is set to zero. If a defect exists when a disturbance has been active, the last element of the binary coded vector is set to one. The last element in data vector was used as an output (binary class value). All different combinations of disturbances and corresponding defect detections, existing within one heat, have been counted from every heat. However, all duplicate vectors within a heat were removed from the data set. Data were pre-processed in the following way: all those casting disturbance types, whose amount in the database were under 10, were removed. In addition were removed all data rows, which did not contain any detections of casting disturbances. After the pre-processing, the database consisted of 20 different casting disturbance types and the number of total data rows was around 10.000. A Self-Organising Map (SOM) analysis was performed for the binary coded database. The SOM was trained using only the binary values of casting disturbances and the corresponding defect class values were left out. The aim of this analysis was to find out the influence of different combinations of disturbances into the existence of detected Parsytec defects. The possible delays in the process were as well taken into account in the analysis. The influence area of a casting disturbance was expanded ±2 and ±4 metres. SOM analysis was carried out with and without the disturbance area expansion. The results obtained from the SOM analysis were presented as component plane maps (Figure 60). The analysis results in this figure are based on the data, where the disturbance areas have not been expanded. Every component map has the same colour range and the values behind colours are as well equal (blue=low values, red=high values). From this figure, it can be seen which casting disturbance types have been observed to be present simultaneously. For example types 11 and 22 have a clear correlation. It can be find also a region, where disturbances 14, 36, and 42 have been active at the same time.

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Figure 60: All SOM component planes have the same colour and value ranges.

The results of another SOM analysis are presented in the Figure 61. The data used in this analysis were without expansion of casting disturbance area. After training the SOM, the hits in each map unit were calculated using the defect class values, Defect and NoDefect. Map units were coloured according to the Defect/NoDefect ratio of the hits. Blue colour means that most of the hits belong to the NoDefect class while yellow represents the high values of Defect/NoDefect ratio. The map on the right shows the number of data hits in each map unit. The upper number stands for Defect data hits and lower number for NoDefect data hits. The Figure 61 shows that there are no clear regions in the map, where the Defect/NoDefect ratio is high. According to this analysis, a certain combination of casting disturbances, which will have a high probability to cause a defect, cannot be found.

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Defect data hits

NoDefect data hits

= most Defect data = most NoDefect data

Figure 61: SOM hits: disturbance areas not expanded.

A SOM analysis was performed also for the data, where the casting disturbance area was expanded ±2 and ±4 metres. The Figure 62 shows the results obtained from the expansion of ±4 metres. The map on the left shows that there are more non-blue units represented that in the corresponding map in the Figure 61. So the expansion of the disturbance area changed the results in a way that the value of the Defect/NoDefect ratio was increased by average. The map on the right in Figure 62 shows some disturbance type combinations attached to the map units where the corresponding component values are high. 27 36 41 42 27 36 41

27 36 36 41 41 97 98

11 = oxygen opening of the steel ladle (M) 22 = casting without ladle shroud (M) 27 = pumping with the stopper rod (M) 36 = automatic mould level control not in use (A) 41 = big single mould level fluctuation (A) 42 = casting speed < 0.7 m/min (A)

11 22

99

97 = dynamic mould level fluctuation (A) 98 = increased secondary cooling (A) 99 = last slab of the casting sequence (A)

Figure 62: SOM hits: disturbance areas expanded ±4 m.

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A2.3.2 COLLECTION OF THE EXPERT’S KNOWLEDGE ILVA On a daily base, at Novi Ligure works, each defective coil detected by Parsytec is sampled in order to assess the nature of the defect. In the presence of inclusions, a SEM analysis is carried out and the elements found in the inclusion are reported in an Excel sheet. The SEM result is then used to assess the genesis of the defect (scale or non metallic inclusion). The database included almost 4000 defective coils, of which 3138 have been detected by Parsytec as “inclusions”. The first investigation, in order to get knowledge useful for the further investigations, was addressed to verify in how many cases the first evaluation of Parsytec has been confirmed as inclusion by SEM analysis. The balance represents the cases where only Fe and O have been detected by SEM (defect originated by entrapped scale). Results of this investigation are reported in Figure 63. IN C L

IN C L co n firm e d

IN C L n o t co n f.

3138 %

1276 40,7

1862 59,3

Le g en d a: IN C L

Inc lu sio n a s d e te cte d b y a uto m atic in sp ection

IN C L co n firm ed

Inc lu sio n c on firm e d b y S E M a n alysis

IN C L n ot co n firm e d

Inc lu sio n n o t con firm e d by S E M a n alysis

Figure 63: % of inclusions confirmed by SEM on defective coils at Novi Ligure works

It is possible to conclude that only 40% of the defects seen by Parsytec as inclusions are confirmed by SEM examination. This is not a very good issue for the reliability of the database, which could be affected by Parsytec’s misclassifications between scale and inclusions. In order to assess the possible origin of inclusions as detected by SEM and the influence due to the manufacturing procedure, the inclusions found have been classified as follows: -

Alumina: when only Al was found

-

Covering powder: when K or Na, was found

-

Calcium aluminate: when Ca and Al, also in combination with other elements was found Silicates (when Si also associated with other elements was found)

-

Spinel: when Mg, Al and no Ca was found

- Other combinations not classifiable in the previous classes For each grade of steel where the number of coils examined was significant (minimum 30) the participation of each type of inclusion has been calculated and the results shown in Figure 64. If we compare the distributions of the steel grades with the general distribution as calculated from the whole database, it is clear that some steel grades show different distributions. In the case of PH10, CH2N and PH14, the main type of inclusion found is alumina, while, for P040, mould powder is the most frequent inclusion found. It can be concluded that some steel grades show typical inclusion distribution as compared with the general distribution and that these differences may be related to the different manufacturing procedures.

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Type of inclusions in defected coils (per steel grade) 80%

RH-OB 70% 60%

AL-CA ALUMINA

50%

x

x

OTHERS MOULD POWDER

40%

SILICATES SPINEL

30% 20% 10% 0% CH3N B040 PH10 BH40 P040 C040 CH2N PH14 AH40 P04S

Tutti

Figure 64: Participation of inclusion families in frequently produced steel grades (Novi Ligure database)

In particular, the highest participation of alumina inclusions is seen for three RH-OB treated grades; to be highlighted that the during the RH-OB treatment it is possible to heat up liquid steel when temperature is not high enough for a proper treatment and subsequent casting. Heat is generated by the reaction between the oxygen blown and the aluminium added and in such conditions alumina is generated which can affect the quality of the coils. Though the amount of heated heats is not high (less than 10%) nevertheless in the case of the grade PH14 (where the influence of alumina defects is higher) the share of reheated heats is typically around 30%. Moreover, in the case of the grade P040, which showed the higher share of mould powder defects, it must be noted that this classification normally covers also slabs with anomalies during casting, which can explain the defects originated by mould powder entrapped. A2.3.3: MODEL DEVELOPMENT ArcelorMittal España & Uniovi Here it is included some information about the models developed for the two types of steel selected. Variables involved in the models are indicated in the report. Methodology (CRISP-DM) is briefly described in Annex 2. ULC model The weighted sensitivity analysis and the results of the model are shown in Figure 65 and Table 27 in WP4 respectively. It can be seen that the most important variables are High variation of volume, volume in superior sheet and Heating.

95

Weighted sensitivity analysis 120 100 80 60 40 20 0

5 2 1 t u p n i

5 4 1 t u p n i

8 t3 u p n i

8 t4 u p n i

5 0 1 t u p n i

4 3 1 t u p n i

1 t9 u p n i

4 t3 u p n i

4 t4 u p n i

9 0 1 t u p n i

1 3 1 t u p n i

0 4 1 t u p n i

6 3 1 t u p n i

1 4 1 t u p n i

Figure 65: Steel B100F33. Weighted sensitivity analysis with the selected variables

In order to analyze the results of the model, several definitions must be done: - The recall or true positive rate is the proportion of positive cases that were correctly identified. - The false positive rate is the proportion of negatives cases that were incorrectly classified as positive. - The true negative rate is defined as the proportion of negatives cases that were classified correctly. - The false negative rate is the proportion of positives cases that were incorrectly classified as negative. - The accuracy is the proportion of the total number of predictions that were correct. Tinplate Model The final model for Tinplate steel is a hybrid model based on a neural model improved through the insertion of expert knowledge (Figure 66).

Figure 66: Hybrid model for Tinplate

The weighted sensitivity analysis and the results of the model are shown in Figure 67 and Table in WP4 respectively. It can be seen that the most important variables are Low speed of casting, high variation of level and Scrap iron.

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Weighted sensitivity analysis 120 100 80 60 40 20 0

4 3 1 t u p n i

5 2 1 t u p n i

8 2 1 t u p n i

6 8 t u p in

9 1 1 t u p n i

3 3 1 t u p n i

3 9 t u p in

8 0 1 t u p n i

6 5 t u p in

4 5 t u p in

9 8 t u p in

Figure 67: Steel B046H33. Weighted sensitivity analysis with the variables selected

SSSA The results obtained after applying Defective-distribution distance calculation on the final dataset (DB3) are given in the next figure. 10

6

0

4

-10

2

7 6 5 4

-20

0

-30

-2

-40

-4

-50

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0

-60

-8

-1

3 2

-70 -80

1

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-2 -3 1

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(a)

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(b)

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1

2

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8

(c)

Figure 68: Distance analysis (a) RH treatment, (b) Reheating treatment, (c) Direct Cycle

Clustering methods Clustering methods (K-mean, Expectation Maximization, Fuzzy C Means ...) have been applied. The best result has been obtained through Fuzzy C-means technique. This algorithm requires the number of clusters to be a priori known. Two tests have been performed: in the first one, the validity measure method [60] has been exploited in order to determine the most suitable number of clusters. In the second test, only two clusters have been distinguished, in order to test if defective casts are divided from other ones. The validity measure method uses the distance between a point and its cluster centre to determine whether the cluster are compact or not. The intra-cluster distance measure is defined as the distance between a point and its cluster centre, is taken the average of all (equation 5)

intra=

1 N

NC

∑∑

i =1 x∈Ci

x − zi

2

(5)

Where N is the number of samples, NC is the number of clusters and zi is the centre of the cluster Ci. This distance has to be minimized. The inter-cluster distance is defined as the distance between clusters and has to be maximized (see eq. (6)): this distance is calculated as the minimum distance between cluster centres:

97

(

inter = min zi − z j 1≤i , j ≤ N C

2

)

i≠ j

(6)

In order to have a good clustering the information conveyed by inter-cluster and intra-cluster distance need to be combined. A quite obvious way is to take as a validity index the ratio between the above mentioned quantities, i.e.:

validity=

intra inter

(7)

The validity measure above needs to be minimised. Therefore, the clustering which gives a minimum value for the validity measure gives the ideal value of NC. Self organizing maps The prediction analysis is made by exploiting the Self Organizing Map (SOM) algorithm. The self-organizing map (SOM) is a paradigm of artificial neural networks. It is trained through an unsupervised learning procedure in order to produce a low dimensional representation of the training samples while preserving the topological properties of the input space. HUT Construction of data sets for the development of the models The basic database consist data of around 30000 heats. These data are collected from various sources of the process (converter, stirring station, continuous casting and hot strip mill). Three data sets were constructed for the modelling purposes: - Data set A (data WITH casting disturbances only, 20 binary coded casting disturbances) - Data set B (data WITHOUT casting disturbances only, 4 process and 6 material parameters) - Data set C (all data, 20 binary coded casting disturbances, 4 process and 6 material params) The first data set A contains data from heats, where exist at least one casting disturbance. The objective of using data set B was to find out the influence of process and material parameters on the defect occurrence. The process was stable without disturbance indicators, thus casting disturbances were not affecting the modelling results. The data set C consists of all the data including heats with and without disturbances. The following process, material and disturbance parameters were included in the data sets. Casting disturbance parameters (20) - Oxygen opening of steel ladle (M) - Significant flow in mould (M) - Low level at tundish (A) - Change of mould slag (M) - Steel grade change by changing tundish and using separate plate (A) - Change of tundish (A) - Steel grade change using separating plate (A) - Steel grade change by mixing in tundish (A) - Casting without ladle shroud (M) - Pumping with stopper rod (M) - Leak in ladle shroud (M) - Leak in SEN below mould level (M) - Automatic mould level control not in use (A) - Breakout prevention (A)

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- Big single mould level fluctuation (A) - Casting speed < 0.7 m/min (A) - Change of SEN (M) - Dynamic mould level fluctuation (A) - Increased secondary cooling (A) - Last slab of casting sequence (A) Process parameters (4) - Cooling scrap added - Ladle treatment time - Slab width - Steel temperature in tundish Material parameters (6) - Al content deviation from target value - Al content in non-metallic inclusions - Total Al content - Si content - Ti content - N pickup during casting The output values for different models were defined as follows: - Output - classifier: Defect class value (0=NoDefect/1=Defect) - Output - regression: Fraction of defect segments OR Average number of defects / m2 The Figure 69 shows the distributions of Parsytec defects in the slabs. The upper diagrams represent the total number of defects attached to different slab segments. The middle diagrams show the defect distributions over the sides of slabs. In the lowest diagrams, the distributions are plotted for separate blocks existing in slabs (edge-centre-edge). Figure 69a represents slabs, which are less than 8 m and Figure 69b respectively slabs over 10 m of length. The defect distribution in the slabs less than 8 m shows that the major part of defects, detected by Parsytec system, is attached to the beginning and to the end of slabs. Distributions should be rather uniform, if the defects are mostly originated from the smelting shop. There is a strong doubt that a lot of defects have been formed after the casting process or the Parsytec system is misclassifying the defects. The defect distribution curves on the right side (Figure 69b) have a longer flat part in the centre than the curves on the left side (Figure 69a). Therefore, only the data from slabs, which were over 10 m of length, were selected to the final data set C. Moreover, 5 slab segments were removed from the beginning and from the end of a slab in order to minimise the influence of the rolling process to the formation of defects.

99

(b)

(a)

Figure 69: The distributions of Parsytec defects attached to different slab segments.

Development of the classifiers for predicting surface inclusion defects Three defect classifiers (A, B and C) were developed for the corresponding data sets. The best results were obtained from the classifier C, (explained in Core). Classifier A The first classifier was developed using the data set A. The model input data were binary coded vectors of casting disturbance occurrences within one heat. If a certain disturbance was active, the corresponding vector element was set to 1, otherwise it was left to 0. Separate data rows were created from all the different combinations of disturbance existences. The output was the binary defect class value (Defect/NoDefect). The disturbance influencing areas were expanded ±4 metres, for example, due to the delays occurring in the process. The defect class value was set to 1, if at least one defect was detected in the expanded area of one casting disturbance or disturbance combinations. The Figure 70 shows the results of the classification. On the left, the distributions of the classifier predictions are plotted. Curves represent the density estimates for the prediction distributions of the Defect and the NoDefect classes. A clear overlapping between the distributions can be observed. The Receiver Operating Characteristic (ROC) curve (b) shows the poor prediction results of the classifier A as well. Classifier B The data set A consisted only data where exist the casting disturbances. The creation of data set B had the opposed basis and therefore the data were from the heats where the process has mostly been in the steady state condition (without disturbance indications). In the data set B the proportion of data rows belonging to the Defect class was still around 75 %. The aim in developing the classifier B was to study the possibility to distinguish the Defects from the NoDefects using selected process and material parameters as the model inputs. Classification results are presented in the Figure 71. In Figure 71a, the prediction distributions are strongly overlapping and the ROC curve (Figure 71b) goes rather close to the diagonal line. The classification results are poor and the Defect and the NoDefect classes cannot be separated.

100

(a)

(b)

Figure 70: Classifier A: Distributions of class predictions (a) and the ROC curve (b).

(a)

(b)

Figure 71: Classifier B: Distributions of class predictions (a) and the ROC curve (b).

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APPENDIX 3: SAMPLING CAMPAING AND ANALYSIS (ArcelorMittal España, ILVA, POLIMI, RWTH) A3.1

SAMPLES ANALYSIS

ArcelorMittal España Samples taken from ladle and tundish are lollypop type. A specimen at the centre of the sample was analyzed (Figure 72). Specimen

Figure 72: Localization of the test specimen on the sample

Samples from slabs were taken at the corner. See Figure from RWTH contribution. For SEM analysis, samples were grinded and on the grinded surface an image analysis has been performed by means of the application Leica Qwin, which consists on the determination of the inclusion density in terms of the inclusion average diameter. For each sample analyzed the inclusions density and the inclusions composition analyzed by SEM was registered. ILVA Samples of typical Taranto process routes have been collected for inclusion assessment. Samples consist of typical medal samples currently used to detect the chemical composition of the molten steel during refining, ladle treatments and continuous casting. The three process routes selected were: Conventional Ar bubbling route Ar is blown (by top lance or porous plug) during ladle refining to homogenise temperature and steel composition Steel grades: low C – Al killed steels of commercial grades RH-OB route Vacuum treating in RH unit equipped with Oxygen Blowing lance. Oxygen is used to increase oxygen content in the liquid steel when the conditions of the melt coming from converter are not adequate to reach the required carbon content at the end of the treatment. Oxygen can be blown as well, in combination with aluminium additions, to increase steel temperature. Steel grades: ULC and IF steels, Al killed for severe drawing applications. CAB route Injection of powdered CaSi (sometimes also Ca wire) at CAB station in the aim to modify the composition of sulphide inclusions (MnS to CaS) to improve the isotropy of the material. Steel grades: C steels (Si-Al killed) for plates and tubes in order to modify inclusions and improving the isotropy of the material.

102

Lolly-pop samples used for process control have been collected at the ladle and tundish, while the slab samples of the second campaign have been collected according to the procedure described in Figure 73.

Casting direction

Surface to be examined

Figure 73: Scheme of the sampling procedure adopted for slabs

Inclusion assessment by PDA A preliminary assessment of the inclusion content was performed by the use of Optical Emission Spectrometry (OES) supported by Pulse Discrimination Analysis (PDA) in order to assess the potential capability of this technology to give reliable information as concerns the inclusion content. The advantage of this methodology is to give a fast response - only few seconds longer than the time requested for the detection of steel composition - as concerns inclusion content; the only present limitation is that results need to be interpreted. An OES of the last generation, equipped with PDA, is available in Caronno works (Riva Acciaio, a sister company of ILVA). This was an opportunity to investigate the applicability of this methodology in the frame of the objectives of the present project. The commercial name of the PDA application in Caronno works is SSE (Single Spark Evaluation) developed by Spectro. The instrument used is a Spectrolab, M9 model. A number of 4000 micro sparks is performed on the surface of the sample. The radiation of each spark is analysed for wavelength and intensity. When one micro spark hits one inclusion, the intensity of the radiation emitted is higher than that resulting from the iron matrix, due to the higher concentration of elements in the inclusions. Therefore, when the intensity for a given wavelength is higher than a given threshold, it can be associated to an inclusion. In Figure 74 the results of the same detection for two different wavelengths are reported. In the X axis the 4000 micro-sparks are represented with the relevant intensities (Y axis). Ca1 393.4 (2) / 1

A l1 3 9 6 . 2 (2 ) / 1 8,0E+02

4, 0 E + 0 2

7,0E+02 6,0E+02

3, 0 E + 0 2 5,0E+02 4,0E+02

2, 0 E + 0 2 3,0E+02 2,0E+02

1, 0 E + 0 2

1,0E+02 0

0

-1,0E+02

-1, 0 E + 0 2

-2,0E+02

1 00 0

2 0 00

3000

40 0 0

1000

2000

3000

4000

Figure 74: Typical output of a PDA detection analyzed on two different wavelength, Al (left) and Ca (right)

103

First 500 sparks are not considered (blue region) to allow the stabilization of the output. Only the intensities above a given threshold (red lines) are considered. This threshold is calculated by means of statistic criteria, but can be modified by the operator. The intensities for each wavelength (which is typical for each element) are compared with other elements and the corresponding peaks of intensities are accounted for given combinations of elements. The result is given as number of peaks counted for combination of two, three or more elements. The combination of elements can be selected by the operator, but it has to be chosen inside the concrete possibilities of inclusion composition, which depends mainly on de-oxidation procedure, steel grade, Ca treatment and so on. The main topic of this technique is the appropriate selection of the most representative combinations of elements which may properly describe the inclusion population of the different routes and give also reliable indications on the amount of inclusions inside the sample. Inclusion assessment by SEM A Zeiss EVO 40XVP SEM coupled with INCA Feature System for analysis of inclusions in steels is available in ILVA (Genoa Works) and has been used for the characterisation of the inclusion population in the samples collected in the two sampling campaigns. The principals of SEM technique are well known. Inclusion composition is detected by means of the micro-analizer based on EDS technique (Energy Dispersive Xray Spectrometry). The peculiarity of the SEM available in Genoa works is the coupled software for inclusion classification (patented name INCA Feature) which can automatically classify each inclusion detected in a given area according to pre-defined criteria (windows of composition for each element). The magnification applied in the detection determines the minimum size of inclusions detectable; by increasing the magnification, the detection time increases as well. In order to reach a compromise between the time for a complete analysis and the accuracy, a magnification of 100x has been used, which allows to detect inclusions with diameter higher than 2,72 µm. In these conditions, the average detection time was a few hours, depending on the number of inclusions as well. Next the SEM images obtained for every route are shown. They correspond to the results present at the Scientific and Technical description of the results. Ar bubbling route Stirring 1 Spectrum

In stats.

O

Al

Total

1

Yes

54,53

45,47

100

2

Yes

54,43

45,57

100

3

Yes

53,48

46,52

100

4

Yes

53,34

46,66

100

Figure 75: Typical Al2O3 inclusions (2000x) and relevant micro-analyses on Ar bubbling route (before treatment)

104

Stirring 2 Spectrum

In stats.

O

Al

Ca

Total

1

Yes

48,45

40,19

11,36

100

2

Yes

63,66

16,02

20,32

100

Figure 76: CaO-Al2O3 inclusion (500x) and relevant micro-analyses on Ar bubbling route (after treatment)

Stirring 2 Spectrum

In stats.

O

Mg

Al

Total

1

Yes

48,83

4,43

46,74

100

2

Yes

50,31

2,68

47,00

100

Figure 77: Al2O3 inclusions (2000x) with MgO traces and micro-analyses on Ar bubbling route (after treatment)

Stirring 2 Spectrum

In stats.

O

Mg

Al

P

1

Yes

44,14

2,11

26,72

1,46

2

Yes

45,01

1,7

29,22

24,07

3

Yes

62,38

11,3

26,32

Ca

25,57

Figure 78: Complex inclusion (200x) and relevant micro-analyses on Ar bubbling route (after treatment) CC Spectrum

In stats.

O

Al

Total

1

Yes

52,05

47,95

100

2

Yes

51,07

48,93

100

Figure 79: Al2O3 inclusion (1000x) and relevant micro-analyses on caster process sample

105

CC Spectrum

In stats.

O

Mg

Al

1

Yes

55,51

2

Yes

47,39

2,29

50,32

3

Yes

50,15

2,86

46,99

44,49

Figure 80: Al2O3 inclusions with MgO traces (2000x) and relevant micro-analyses on caster process sample

CAB route Spectrum

In stats.

O

Al

Total

1 2 3 4

Yes Yes Yes Yes

47,5 49,17 42,93 47,07

52,5 50,83 57,07 52,93

100 100 100 100

Figure 81: Al2O3 inclusions (750x) in process samples before CAB treatment and relevant micro-analysis Spectrum

In stats.

O

Al

S

Ca

1 2

Yes Yes

23,35 7,71

2,62

21,00 41,63

53,03 50,65

3

Yes

83,55

4 5

Yes Yes

13,45 41,20

1,04 15,65

36,79

48,73 43,15

16,45

Figure 82: Inclusions (500 x) in process samples after CAB treatment and relevant micro-analysis CAB 2 Spectrum

O

1

88,06

2

37,09

3

75,59

4

46,94

Mg

Al

S

13,7

15,29

8,88

11,94 25,04 24,41 1,07

15,26

3,05

Figure 83: Inclusions (500 x) in process samples after CAB treatment and relevant micro-analysis

106

Ca

33,69

RH route Vacuum treatment by RH process is applied to Ultra Low Carbon steels (Interstitial Free). In this case, the most frequent type of inclusions found is Al2O3. Nevertheless, in some case also large, round shaped inclusions have been found, as shown in Figure 84, containing Ca, Al and some Mg. Since no Ca addition is performed in this route, the presence of this type of inclusion may be due to slag entrapment.

Spectrum

In stats.

O

Mg

Al

Ca

1

Yes

48,67

2,91

24,41

24,01

2

Yes

44,52

2,61

27,2

25,67

3

Yes

52,57

38,4

Figure 84: Large inclusion (1000 x) found in process sample before RH treatment

In all the other cases detected, only Al2O3 has been found, both in samples after RH treatment and in continuous casting samples (see Figure 85, Figure 86) Spectrum

In stats.

O

Al

Total

Spectrum 1

Yes

46,76

53,24

100

Figure 85: Al2O3 inclusion (1500 x) found in process sample after RH treatment Spectrum

In stats.

O

Al

Total

1

Yes

52,27

47,73

100

2

Yes

51,66

48,34

100

3

Yes

51,02

48,98

100

Figure 86: Al2O3 inclusion (1500 x) found in continuous casting process sample

RWTH Nine lollipop samples from ladle and tundish have been sent to Aachen by ArcelorMittal España. Table 33 shows the chemical composition of the steel grade which has been analysed. C 0,025

Mn 0,23

Si 0,02

S 0,015

P 0,015

Al 0,06

Cu 0,06

Ni 0,04

Cr 0,03

Nb 0,001

Mo 0,01

V 0,005

Ti 0,01

Table 33: Chemical composition of analysed steel grade (wt.-%)

These samples have been investigated in metallographic way on non-metallic inclusions by lightmicroscope. Pictures have been made from non-metallic inclusions and size distributions have been

107

calculated by statistical analysis with IBAS-system. The samples have been embedded in TECHNOVIT, grinded and polished. Four areas of respectively 1mm2 have been investigated. Figure 88 to Figure 107 show the results for the metallographic photos and the size distribution of the samples. Furthermore, samples from liquid steel at different stages (ladle and tundish), two heats; samples from slabs, four heats; samples from hot rolled steel, one heat were sent to Aachen corresponding to PQ slabs. The following figures show the position of samples which have been cut and analysed.

Figure 87: Photos of slabs samples (red strips and red boxes: position of sampling) 700

600

400

300

200

100

51

26

22

20

18

16

15

48

50 -

47 -

25 -

21 -

19 -

17 -

15 -

14

14 -

13 -

0

11 10 -

89

91

78

67

56

45

34

23

12

0 01

Number density [mm-2]

500

Mean diameter [µm]

Figure 88: Size distribution of inclusions of ladle sample 1a-991-33

108

Figure 89: Metallographic micrographs of ladle sample 1a-991-33 1600

1400

Number density [mm-2]

1200

1000

800

600

400

200

4

5

6

7

8

9

3-

4-

5-

6-

7-

8-

910 10 -1 1 11 -1 2 12 -1 3 13 -1 4 14 -1 5 15 -1 6 16 -1 7 17 -1 8 18 -1 9 19 -2 0 20 -2 1 21 -2 2 22 -2 3 25 -2 6 26 -2 7 27 -2 8 30 -3 1 33 -3 4

2

3 2-

1

1-

0-

0

Mean diameter [µm]

Figure 90: Size distribution of inclusions of ladle sample 2a-991-33

Figure 91: Metallographic micrographs of ladle sample 2a-991-33 45 40

30 25 20 15 10 5

51 -5 2

43 -4 4

23 -2 4

16 -1 7

0

11 -1 2

91

89

78

67

56

45

34

23

12

0 01

-2

Number density [mm ]

35

Mean diameter [µm]

Figure 92: Size distribution of inclusions of ladle sample 32-690-189

109

Figure 93: Metallographic micrographs of ladle sample 32-690-189 700

600

400

300

200

100

-3 1 30

-2 3

-2 2

-2 0

-1 8

-1 7

-2 5 24

22

21

19

17

16

-1 4

-1 3

-1 2

-1 1

-1 6 15

13

12

11

10

89

910

78

67

56

45

34

23

12

0 01

Number density [mm-2]

500

Mean diameter [µm]

Figure 94: Size distribution of inclusions of ladle sample 32-690-190

Figure 95: Metallographic micrographs of ladle sample 32-690-190

110

3500

3000

-2

Number density [mm ]

2500

2000

1500

1000

500

68

60 59 -

67 -

46

38

34

54 53 -

45 -

37 -

29

32

33 -

31 -

25

23

27

28 -

26 -

24 -

19

21

22 -

20 -

18 -

15

13

17 16 -

14 -

11

12 -

89

10 -

67

45

23

01

0

Mean diameter [µm]

Figure 96: Size distribution of inclusions of ladle sample 32-690-191

Figure 97: Metallographic micrographs of ladle sample 32-690-191 50 45

35 30 25 20 15 10 5

-6 8 67

-6 1 60

-4 9 48

-2 7 26

-1 8 17

-1 3 12

-1 2 11

78

67

34

23

12

0 01

Number density [mm-2]

40

Mean diameter [µm]

Figure 98: Size distribution of inclusions of ladle sample 33-690-190

111

Figure 99: Metallographic micrographs of ladle sample 33-690-190 1400

1200

-2

Number density [mm ]

1000

800

600

400

200

2

3

4

7

8

1-

2-

3-

6-

7-

89 910 10 -1 1 11 -1 2 12 -1 3 13 -1 4 14 -1 5 15 -1 6 16 -1 7 17 -1 8 18 -1 9 19 -2 0 20 -2 1 21 -2 2 22 -2 3 23 -2 4 24 -2 5 25 -2 6 26 -2 7 30 -3 1 32 -3 3 33 -3 4

1 0-

0

Mean diameter [µm]

Figure 100: Size distribution of inclusions of ladle sample 51-690-189

Figure 101: Metallographic micrographs of ladle sample 51-690-189 350

300

-2

Number density [mm ]

250

200

150

100

50

54

46

27

24

22

21

15

55 54 -

53 -

45 -

26 -

23 -

21 -

20 -

14

14 -

13 -

0

11 10 -

89

91

78

67

56

45

34

23

12

01

0

Mean diameter [µm]

Figure 102: Size distribution of inclusions of ladle sample 52-690-189

112

Figure 103: Metallographic micrographs of ladle sample 52-690-189 250

Number density [mm-2]

200

150

100

50

10 -1 1

78

67

34

23

12

01

0

Mean diameter [µm]

Figure 104: Size distribution of inclusions of tundish sample 92-690-190

Figure 105: Metallographic micrographs of ladle sample 92-690-190 120

80

60

40

20

22 21 -

20 19 -

15 14 -

12 11 -

11 10 -

0 91

89

78

67

34

23

12

0 01

Number density [mm-2]

100

Mean diameter [µm]

Figure 106: Size distribution of inclusions of tundish sample 92-690-191

113

Figure 107: Metallographic micrographs of ladle sample 92-690-191

A3.2

IDENTIFYING THE TYPE AND QUANTITY OF INCLUSIONS

ArcelorMittal España Figure 108 shows the results found for the first heat analyzed (C1) of the first Tinplate sampling campaign and Figure 109 the evolution of the inclusion content through that sample. Similar graphs were produced for the six heats (C1 – C6) but are shown in the present report.

BOF sample

RH sample

T2 sample

T3 sample

Figure 108: Inclusion content for Heat 1 -C1

114

Inclusion Density (mm-2)

HEAT 1 – C1 Figure 109: Evolution of inclusion density along Heat 1 – C1.

In Figure 110 to Figure 112 is shown the comparison between inclusion content at sample TD2 for the six heats analyzed.(Scientific and Technical description of the results).

Figure 110: a) Heat 1, sample TD2; b)Heat 2, sample TD2

Figure 111: a) Heat 3, sample TD2; b) Heat 4, sample TD2

115

Figure 112: a) Heat 5, sample TD2; b) Heat 6, sample TD2

For inclusion composition analysis, micrographics of each sample were taken. Some results are present in the next figures. Figure 113 shows the most common inclusion found: Al2O3. Figure 114 shows an example of MnS inclusions, found also very often in all sampling campaigns made while Figure 115 shows a more complex one with origin probably in refractory.

Figure 113: Alumina inclusion

Figure 114: MnS inclusion

116

Figure 115: Inclusion containing Ca and Zr

ILVA Preliminary work had to be done in order to get acquaintance with INCA software for inclusion classification. Sample polishing Though a good sample preparation praxis was already available, some improvements has been done to fit better with SEM detection needs: in order to avoid any corrosion problem, water utilisation has been limited to the first steps of preparation (SiC grinding papers), in which the sample is still rough and not so reactive. A few samples showed some pits formation during the latest phases of preparation (cleaning cloths for diamond polishing). These critical preparation steps have been performed using strictly ethanol (C2H5OH) to clean samples from one cleaning phase to another. Each sample has been processed with two consecutive ultrasonic cleanings in ethanol and additionally cleaned with a fine cloth promptly before the introduction in the SEM chamber. Dust particles, which deposit on the sample surface during the application of the conductive graphite paint, were thus totally removed. Inclusions classification In order to assess a reliable methodology for an automatic inclusion classification, several attempts have been carried out in order to give to each inclusion typology the proper thresholds for each element, in the aim to decrease unclassified inclusions and avoid ambiguities (the same inclusion with two or more classifications). Features can be classified either during or after data collection and may be reclassified quickly without re-acquiring the data, saving valuable microscope time. In addition, once a suitable classification scheme has been defined, it can be saved for use on future data sets [3]. Using this possibility, classes have been reviewed many times in order to find the best layout. Each class has been optimised in order to be more complying to the real inclusions chemical composition. Unclassified inclusions decreased: their presence is now strictly related to zirconium oxide (whose presence is due to the addition of zirconium in order to deoxidise the liquid steel samples taken from the steel ladle) and to particular complex inclusions, such as aggregates of oxides, sulphides or oxy-sulphides. The so adjusted thresholds used to classify the inclusions are shown in Table 34 (to be noted that the wide ranges given for most of the elements considered is due to the presence of Fe in most detections, particularly in the smallest inclusions).

117

Class Al2O3 CaAl2O4 MgAlCaO TiO2 MnSiO3 Other Oxides Ca-Silicates CaAl-Silicates MgAlO CaMnS CaS MnS Other Sulphides AlCaSO Other Oxysulphides TiN High Fe

Al 4 - 100 3 - 100 3 - 100 0-2 3 - 100 0-2 2 - 100 3 - 100

3 - 100

Automatic classification of inclusions: allowable thresholds for element Ca Mg S Si Mn Ti O 0-5 0-2 0 - 2.5 0-2 0-3 2 - 100 0-2 0-2 0-1 0-1 2 - 100 2 - 100 0-1 0-1 0-1 0-1 5 - 100 3 - 100 0-1 0-1 3 - 100 0-1 10 - 100 2 - 100 2 - 100 2 - 100 2 - 100 2 - 100 5 - 100 5 - 100 5 - 100 0-1 5 - 100 2 - 100 0-2 2 - 100 3 - 100 0-2 2 - 100 0-1 2 - 100 2 - 100 2 - 100 3 - 100 3 - 100

Fe

0 - 80

90 - 100

Table 34: Classification of inclusions: thresholds for each element adopted for each inclusion class

ii. Image acquiring, inclusion detection and x-ray EDS analysis The detection system of the computer software used for the EDS x-ray microprobe analysis, INCA Feature, is based on image analysis; scanning electron microscope is set in backscattered electrons in order to be get images with a strong contrast related to the atomic number: inclusions, which are formed by light elements, appear darker than the matrix. Acquired images are processed via image analysis based on greyscale. At the operating magnification of 100x, lower limit for inclusions detection has been set to an equivalent diameter of 2.72 µm (equal to 16 pixels area on the screen). X-ray acquisition time has been set to 4 seconds with a dead time of approximately 50%, for a total time of 6 seconds per inclusion. Tests performed at 200x and 20 seconds per inclusion have not shown any significant improvement in chemical analysis, despite of a great increasing in the analysis duration. Next the composition of steel analyzed to support the results of each sampling campaign presented at the Core section of the present report is given.

Results of the first campaign HEAT no. 634789 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0477

0.0087

0.2193

0.0111

0.0178

0.0001

0.0453

0.0010

0.0049

0.0001

HEAT no. 635022 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0610

0.0050

0.4662

0.0164

0.0240

0.0002

0.0371

0.0008

0.0035

0.0000

HEAT no. 635035 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0432

0.0059

0.2373

0.0162

0.0148

0.0001

0.0508

0.0006

0.0045

0.0001

HEAT no. 635039 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.1687

0.0074

0.5099

0.0101

0.0046

0.0001

0.0524

0.0075

0.0033

0.0001

Table 35: Chemical composition of analysed steels for Ar bubbling route

118

HEAT no. 625056 C

Si

Mn

P

S

Nb

V

Al

Ti

N2

Ca

0.0044

0.0021

0.1592

0.0117

0.0066

0.0003

0.0023

0.0308

0.0620

0.0040

0.0000

HEAT no. 625062 C

Si

Mn

P

S

Nb

V

Al

Ti

N2

Ca

0.0039

0.0011

0.1917

0.0147

0.0063

0.0361

0.0007

0.0343

0.0187

0.0040

0.0000

HEAT no. 634962 C

Si

Mn

P

S

Nb

V

Al

Ti

N2

Ca

0.0049

0.0027

0.2010

0.0179

0.0087

0.0364

0.0008

0.0286

0.0193

0.0040

0.0000

Table 36: Chemical composition of analysed steels for RH route HEAT no. 673398 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0760

0.2896

1.5304

0.0210

0.0031

0.0428

0.0276

0.0176

0.0059

0.0060

HEAT no. 673409 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0810

0.2587

1.5242

0.0190

0.0040

0.0390

0.0307

0.0215

0.0060

0.0041

HEAT no. 673412 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0701

0.2829

1.5373

0.0208

0.0023

0.0417

0.0280

0.0173

0.0058

0.0021

HEAT no. 673399 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0850

0.2515

1.5271

0.0143

0.0044

0.0448

0.0325

0.0210

0.0069

0.0053

Table 37: Chemical composition of analysed steels for CAB route

Results of the second campaign HEAT no. 831628 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.027

0.009

0.26

0.009

0.017

0.002

0.043

0.015

0.0049

0

HEAT no. 811648 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.035

0.013

0.26

0.017

0.019

0.001

0.055

0.010

0.0050

0

HEAT no. 811669 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.040

0.010

0.28

0.012

0.018

0.001

0.043

0.012

0.0075

0

HEAT no. 821937 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.060

0.012

0.37

0.026

0.023

0.001

0.053

0.002

0.0050

0

Table 38: Chemical composition of analysed steels for Ar bubbling route

119

HEAT no. 831699 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0036

0.003

0.17

0.014

0.008

0

0.035

0.064

0.0035

0

HEAT no. 811646 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0030

0.006

0.20

0.013

0.008

0.030

0.030

0.030

0.0036

0

HEAT no. 831697 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0036

0.004

0.18

0.015

0.007

0

0.039

0.062

0.0036

0

HEAT no. 831698 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.0032

0.004

0.16

0.014

0.014

0

0.037

0.070

0.0037

0

Table 39: Chemical composition of analysed steels for RH route HEAT no. 880980 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.065

0.24

1.33

0.020

0.004

0.046

0.035

0.021

0.0075

0.0046

HEAT no. 880962 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.070

0.30

1.40

0.017

0.005

0.048

0.061

0.027

0.0080

0.0036

HEAT no. 891658 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.16

0.28

0.97

0.015

0.007

0.039

0.031

0.022

0.0105

0.0053

HEAT no. 891655 C

Si

Mn

P

S

Nb

Al

Ti

N2

Ca

0.16

0.28

1.07

0.019

0.005

0.039

0.033

0.018

0.0100

0.0046

Table 40: Chemical composition of analysed steels for CAB route

RWTH Next tables shown targets at different stages: C 0,025

Mn 0,23

Si

S

0,02

P

0,015

Al

0,015

Cu

0,06

0,06

Ni

Cr

Nb

0,04

0,03

0,001

Mo 0,01

V 0,005

Ti 0,01

Table 41: Target chemical composition of steel grade (wt.-%)

The chemical compositions at each process step from ladle and tundish are shown in Table 42,Table 43 and Table 44. C

Mn

0,017

Si

0,18

S

0,005

0,014

P

Al

0,008

Cu

0,06

Ni

0,02

0,03

Cr

Mo

0,017

0,004

V 0,003

Ti 0,0007

Table 42: Chemical composition of steel sample 601 603 32 1 (wt.-%), ladle, during RH C

Mn

Si

S

P

Al

Cu

Ni

Cr

Mo

V

Ti

0,016

0,2

0,008

0,012

0,009

0,05

0,018

0,03

0,02

0,004

0,0001

0,0004

Table 43: Chemical composition of steel sample 601 603 33 1, (wt.-%), ladle, end of RH

120

C 0,02

Mn

Si

0,20

0,007

S 0,007

P 0,01

Al

Cu

0,01

0,018

Ni 0,03

Cr

Mo

0,020

0,004

V

Ti

0,0001

0,0003

Table 44: Chemical composition of steel sample 601 603 91 1, (wt.-%), tundish

The samples have been investigated metallographically on non-metallic inclusions by light-microscope. To gain better knowledge about the shape of inclusions in the specific case, a sample from the steel making process during ladle treatment was etched with HNO3 to assay inclusions morphology. Pictures of different enlargements 1000x, 2000x and 3000x have been made from various non-metallic inclusion areas. Also the etching time was varied to obtain the best results in visibility. Two areas of the sample have been investigated. The results are shown at the Scientific and technical description of the results section of this report.

POLIMI

Figure 116: An example of pure Al2O3 non-metallic inclusions revealed just after the first de-oxidation operated through the Al addition in grade-1.

Figure 117: An example of pure SiO2 non-metallic inclusions revealed just after the first de-oxidation operated through the Si addition in grade-3 steel at an average oxygen level of 950ppm after EAF tapping.

121

Figure 118: Anorthite inclusion found in the steel grade-3 at the end of the LF treatment.

Figure 119: An example of some inclusions rich in SiO2.2CaO.Al2O3 (gehlenite) found in grade-1 after the tapping at an average oxygen level of 750ppm, after de-oxidation through Al.

65mins after LF Tundish Rolled product

Figure 120: Size distribution of the non-metallic inclusions in the grade without delaying time of the LF treatment stirred by Ar (a) 140 l/min and (b) 250 l/min.

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65mins after LF

65mins after LF

Tundish

Tundish

Rolled product

Rolled product

(a)

(b)

Figure 121: Size distribution of the non-metallic inclusions in the grade with a delaying time of the LF treatment of 5 min and stirred by Ar (a) 140 l/min and (b) 250 l/min. 65mins after LF

65mins after LF

Tundish

Tundish

Rolled product

Rolled product

(a)

(b)

Figure 122: Size distribution of the non-metallic inclusions in the grade with a delaying time of the LF treatment of 8 min and stirred by Ar (a) 140 l/min and (b) 250 l/min.

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APPENDIX 4: IDENTIFICATION OF THE PROBLEMS OF REPRESENTATIVENESS OF PARSYTEC – (SSSA)

New database description A new database has been built to explain the problems related to the application of AI techniques (in particular Neural Networks) relying on the defect classification performed by the Parsytec systems. The aim of this work is to demonstrate that when the target data referring to the defect classification are reliable and the distinction is made between inclusions related to continuous casting and hot-rolling and other kind of inclusions, the proposed SOM model give satisfactory results. A sample of images coming from 132 coils has been analyzed in order to verify and eventually correct the defect classification. The coil number has been linked to the associated heat and the database has been built by taking into account the associated chemical and process variables. The set of potential inputs contains the same variables that had been considered in the approach pursued during the PREDINC project, but in this case the attention is focused on the defects coming from inclusions with a good deal of accuracy based on both the number and the size of the defects. A pre-processing stage has been required in order to eliminate outliers form the database. The limited size of the database does not allow clustering casts in three different groups on the basis of the adopted treatment cycle. As during the project it has been demonstrated that treatment type affects the presence of inclusions, it is reasonable to expect an improvement of the results when enough data are available, which allow the design of one predictor for each treatment type.

Inputs selection and model development In the previous work, more than 2000 combinations of the different parameters have been evaluated. In particular the SOM-based neural network was trained by varying the network dimensions, threshold and input variables, by also considering that the number of data used for training should be at least four times greater with respect to the number of free parameters of the network. Then the best results for each treatment were selected. The previously obtained results are shown in Table 45. Performance indexes Treatment

Selected Input Variables

RH

True negative

False positive

True positive

False negative

Accuracy

Al, OxBlow, OxppmReheat, 88.2 treatment duration

11.8

23.3

76.7

78.0

Reheat

Al, OxBlow, OxppmReheat, 87.0 treatment duration

13.0

20.0

80.0

79.5

Direct Cycle

PkP, PkN, Oxcov, casting speed

10.0

17.0

83.0

77

mean 90.0

Table 45: Results of the best predictors for the three routes (results evaluated on a validation dataset, i.e. on 25% of the available data, which have not been used for training)

The aim of the model was to investigate the true positive rate i.e. to predict the presence of inclusions and the results around 20% were obviously not satisfactory. While in the previous elaborations the variables were pre-selected on the basis of experts’ knowledge and imposed in the elaboration, in the new database the input variables have been selected by using a Genetic Algorithms-based variables selection procedure called GIVE A GAP (General purpose Input Variables Extraction: A Genetic Algorithm (GA) -based Procedure) which has been developed at SSSA in 2009 [64] outside the PREDINC project. This method investigates different combinations of the input variables of a generic model (not necessarily based on SOM) and exploits the GA to select the best combinations of variables. The considered models can have any purpose, included classification, such as in the considered case: the fitness function should be formulated as a consequence of the model purpose. On the basis of the maximum value of input variables that need to be selected and of the

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number of data which are available for training, the procedure is capable of self-dimensioning the neural model. In the considered application, the GIVEAGAP procedure applied to the new database has selected as input variables which mostly affect the target (i.e. the presence/absence of inclusions) the following variables:

-

Blown Oxygen

-

Tapping Temperature

-

Ladle temperature

The results of the SOM-based classifier, whose dimension is 3x3, which exploits the selected input variables are shown in the following table. Performance indexes Treatment

Selected Input Variables

True negative

False positive

True positive

False negative

Accuracy

All

OxBlow, Tapping Temperature, Ladle Temperature

83.3

16.7

87.5

12.5

85.0

Table 46: Results of the new predictor designed on the reduced but more reliable dataset (results evaluated on a validation dataset, i.e. on 25% of the available data, which have not been used for training)

As it appears form the obtained results, the accuracy is not dramatically increased, but the developed system achieves the purposes of the project because the true positive rate overcomes 85%, which demonstrates the efficiency of the system in recognizing the actual occurrence of the inclusions. Obviously if more data were available, the results would be expected to improve because the dimension of the network can be greater and the different treatments could be taken into account separately. The application of the GIVEAGAP procedure, although important in order to select the correct set of input variables, is not the key of the successful development of the classifier. If the above listed three input variables selected with the GIVE A GAP method for the new database were used to train the old database, the results are still poor, as shown in Table 47 (NOTE: for the Direct Cycle, Blown Oxygen is not available and it has been substituted with Oxygen value in the ladle). Performance indexes Treatment

Selected Input Variables

RH

True negative

False positive

True positive

False negative

Accuracy

OxBlow, Tapping Temperature, 72.2 Ladle Temperature

27.8

16.7

83.3

63.3

Reheat

OxBlow, Tapping Temperature, 71.7 Ladle Temperature

28.3

23.0

77.0

66.2

Direct Cycle

Oxygen in the ladle, Tapping 78.4 Temperature, Ladle Temperature

21.6

22.5

77.5

69.5

Table 47: Results of the best predictors for the three routes exploiting only three input variables (results evaluated on a validation dataset, i.e. on 25% of the available data, which have not been used for training)

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If the GIVEAGAP selection procedure is applied to select the best input to the previous database, the results slightly improve but are still not satisfactory, as depicted in Table 48, which also lists the input variables selected by the GIVEAGAP procedure for each treatment:

Performance indexes Treatment

Selected Input Variables

RH

True negative

False positive

True positive

False negative

Accuracy

Al, OxppmReheat, mean casting 87.36% speed, PkSi

12.64%

42.50%

57.50%

80.21%

Reheat

Al, PkP, PkSi

84.84%

15.16%

42.11%

57.89%

80.09%

Direct Cycle

PkP, Tapping Temperature, 83.16% Ladle Temperature, mean casting speed

16.84%

56.47%

43.53%

78.38%

Table 48: Results of the best predictors for the three routes exploiting the input variables separately selected through the GIVEAGAP procedure (results evaluated on a validation dataset, i.e. on 25% of the available data, which have not been used for training)

Conclusions The results obtained with the new database, where the classification of inclusions by Parsytec was more reliable as compared with the database previously used in Predinc, are better, though the limited number of data. As a conclusion, it can be argued that, as the successful application of any AI technique depends on the goodness of the database which is exploited for the learning phase, they cannot be applied in inclusions detection if one wants to exploits directly the PARSYTEC data for training the SOM-based predictor, at least at this stage of development of these systems. In the present situation, clearly a model based on physical reasoning, which takes into account the chemical and physical phenomena which are at the basis of the inclusions formation, will surely overcome any AI technique. On the other hand, if some kind of post-processing of the Parsytec data is performed, in order to build a reliable database, where defects considered are only inclusions-related defects depending on steelmaking and continuous casting procedures, AI techniques can be successfully applied to the prediction of inclusions-related defects on the basis of process parameters. The very limited database that has been exploited for the experiments presented here has been manually elaborated, but the same operation could not be performed on the huge database that has been exploited in PREDINC. On the other hand, the development of image processing techniques which allow an automatic post-processing of the images and other data collected by the PARSYTEC was out of the scope of PREDINC. However an intensive work is being carried out in this sense both by the producer of Automatic Surface Inspection Systems and by the steel producer and also some RFCS projects have been and are being carried out on this subject. When the results of these efforts will be available, AI techniques could have a strong potential in the prediction of the occurrence of inclusions-related defects.

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European Commission EUR 24992 — Prediction of inclusions in the slabs from the process characteristics (PREDINC) L.F. Sancho, V. Colla, S. Cateni, S. Fera, J. Laine, C. Mapelli, D. Senk, F. Ortega, F .Rodríguez Luxembourg: Publications Office of the European Union 2011 — 126 pp. — 21 × 29.7 cm Research Fund for Coal and Steel series ISBN 978-92-79-21718-0 doi:10.2777/86035 ISSN 1831-9424

HOW TO OBTAIN EU PUBLICATIONS Free publications: • via EU Bookshop (http://bookshop.europa.eu); • at the European Union’s representations or delegations. You can obtain their contact details on the Internet (http://ec.europa.eu) or by sending a fax to +352 2929-42758. Priced publications: • via EU Bookshop (http://bookshop.europa.eu). Priced subscriptions (e.g. annual series of the Official Journal of the European Union and reports of cases before the Court of Justice of the European Union): • via one of the sales agents of the Publications Office of the European Union (http://publications.europa.eu/others/agents/index_en.htm).

The aim of this project is to develop a system capable to determine the quality in the field of inclusions of steel before and during its production, in order to change the setups to improve it. Data from four Steel shops, two partners expert in metallurgical modelling and three partners with proven expertise in data-based models worked together, including crossed evaluation in order to produce, validate and conclude the cleanliness model. Two ways of model development were carried out: classical thermodynamic calculation and data-based analysis. Thermodynamical models provide good results for being the first approach to cleanliness models for selected cases. Data Mining models are capable to imitate classical models, improving their performance for more cases although it was demonstrated that it is not possible to manage all kinds of steel with a single model. Moreover, the need of improving reliability of inspection system output to be used as model input was identified. In order to validate the models and get deeper knowledge on inclusions formation in selected steel grades, several sampling campaigns and inclusions analysis have been done. As expected, for similar steel grades the results obtained were similar, as expected. Finally a user interface and requirements for integration of the developed models within the steel plant were also developed in the promising cases although it was not completely extended as the range of steels where models are applicable is limited. Potential areas of exploitation for the results from this project have been highlighted.

KI-NA-24992-EN-N

This project is a collaboration between ArcelorMittal España, SA, SSSA, ILVA, Helsinki University of Technology, Politecnico di Milano, RWTH IEKH Aachen University and University of Oviedo and is coordinated by ArcelorMittal España.