Kuopio, Finland, 1-4 June, 2009
FROM PROCESS DESIGN SUPPORT TO ONLINE OPTIMISATION – CURRENT USE AND FUTURE DEVELOPMENT OF MODELLING AND SIMULATION J. Kappen1, W. Dietz2 1 Corresponding
author. Papiertechnische Stiftung, Simulation and Sensor Technology, Hessstrasse 134, DE-80797 Munich, Germany. E-mail:
[email protected] 2 Papiertechnische Stiftung, Simulation and Sensor Technology, Hessstrasse 134, DE-80797 Munich, Germany. E-mail:
[email protected] .
Abstract. Today cost efficiency improvement is of major importance for all industrial activities. Any cost cutting approach will be favoured against other solutions although they may have an appealing technical approach. Process performance improvement and product cost reduction are key focus. The paper industry tries to gain cost efficiency by building ever larger and faster production lines and this brings along the ever increasing demand for improved stability of the process and the product quality. Better automation and control is one important measure to accomplish this task. At the same time paper industry is evaluating its data, presuming that they are a major source for the discovery of performance improvement options. In many cases, the results of data analysis lend themselves to building up applications for model based control and even model based overall mill wide cost optimisation. The current trend towards online use of models has to be evaluated against its historical background as modelling and simulation have been established since long time within the pulp and paper industry. Right in the beginning there was the vision to model both, the product described by its relevant properties and the respective production process. Deter-mined by complexity, straight forward process models have taken the lead in practical application, mainly in order to perform design tasks. Latest state of the art applications based on dynamic process simulations also allow for DCS checkout and operator training. Still, less attention in practical application is given the modelling of paper properties. Product quality improvement and the development of new paper grades are seen to have a smaller economical impact compared to the most important drivers named above. Another reason being, that from a scientific point of view, the description of paper properties is a far more demanding task. Predominantly data driven soft sensors fill in the gap, providing relevant information regarding product quality on line. Latest developments in modelling techniques and materials testing opening up new options to move from data based black box to first principle models. These developments could finally serve as a basis for online optimisation of mill sites or even supply chains from tree to end user. Keywords: modelling, simulation, optimisation, design, applications, development
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The Situation of the Industry in Europe
1.1 Increased financial pressure The European paper industry is confronted with significant economic pressure. Cash flow and earnings have declined to critical values during the past years as can be seen by the example of Germany (Figure 1). 18 cash flow earnings before tax
16 14 12 10 8 6 4 2 0 2001
2002
2003
2004
2005
2006
2007 (2008)
Figure 1: Development of key figures by the example of the German pulp and paper industry; 2008: preliminary figures [1]
1.2 Focus on growth As a consequence of the economic downturn, cost efficiency is a key concern of all current industrial activities: Any cost cutting approach will be favoured over other solutions that might have an appealing technical approach. The paper industry tries to gain cost efficiency by building ever larger and faster production lines. A comparison of major rebuilds shows that also here cost efficiency is most successfully gained by increasing the available production capacity of the respective line. Projects aiming for paper quality improvement or product change do not offer similar benefits in terms of cost efficiency (Figure 2). Larger lines and capacities bring along the ever increasing demand for improved stability of processes and product quality.
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Improvement of cost competitiveness %
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capacity increase product change quality improvement
Investment m€
Figure 2: Economic evaluation of rebuilds in the paper industry (graphical paper) [2]
1.3 Options for increased cost efficiency (the industry perspective) The industry’s first priority in increasing cost efficiency is to safeguard and maximise process and product stability. In detail this is addressed by setting the following targets: · reduction in downtimes · reduction of off spec production · reduction of grade change times Second priority is placed on reducing proportional cost. This product related cost is determined by the design and can be addressed by altering the recipe of the product to replace more costly components by cheaper substitutes, exchanging recycled pulp for virgin pulp etc. Another approach is to improve process handling and/or control in order to produce the same grade closer to its minimum specifications and thus, save fibres and fillers as well as additives whilst ensuring that no off spec production occurs. Product quality improvements are of lower economic impact and importance. Oftentimes expectations to achieving higher prices for the same (improved) product are not met. For similar reasons this is also true for the development of new paper properties or grades. New grades are of high relevance only to mills under pressure in their respective home market. Decision has to be taken whether or not to move out of the present market since they are not able to compete with other producers cost wise. The placement of capital within investment projects speaks a clear language, despite the fact that paper makers are all in favour of the latter approaches. In short, an improved process performance of ever larger and faster production lines seems to be the overruling paradigm to be followed in order to produce attractive applications. If this is the case, also modelling and simulation applications have to address this goal and find new solutions for these pending demands. So the following question has in fact to be put forward: What can the development of modelling and simulation applications provide in order to support industry today? And furthermore the paradigm itself can be questioned: Is the current focusing the only valid perspective or could J. KAPPEN, W. DIETZ
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modelling and simulation techniques open up new options for the pulp and paper industry? Justification of this question comes along with the fact that new techniques have always been able to support changes in business conditions significantly.
2 Development of knowledge in modelling and simulation The number of publications reflecting the development of knowledge in the field of modelling and simulation has been evolving vividly during the past 35 years (Figure 3). The activities started in the mid ‘70s and development accelerated during the second half of the ‘90s. Within the last 15 years the number of publications on model-based control has risen clearly faster than the number of those dealing with (offline) modelling and simulation. This can be read as a clear trend towards developing applications that apply models and the contained knowledge online. Numbers regarding the period 2005-’09 are still preliminary. number of publications
589
600 528
simulation & paper 500
model based control
400
total pulp&paper (x 1000)
300
245
200
238 165
156 61
87
100
9 00
0
332 276
90
11
22
103
44
1
60-64 65-69 70-74 75-79 80-84 85-89 90-94 95-99 00-04 05-09 year of publication
as of 04/2009
Figure 3: Development over time of the number of publications on simulation concerning pulp and paper [3] Drying or otherwise energy related issues are the most important topics dealt with by publications on modelling and simulation (Figure 4). This focus is quite understandable since the paper industry relies far more on energy than other industries: It spends more than three times as much on energy than other manufacturing industries on average (Sweden) [4]. Significant sources of funding are available to produce new and applied knowledge in this area.
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model based control "paper"
dynamic simulation drying energy coating 1960 - 2004
paper properties
2005 - 2009
waste paper retention pressing fibre properties "simulation" and "paper"
grade change (sheet) formation water circuits fibre sorting
0
50
100 150 200 250 300 350 400 450 500 550 600 650 number of publications
as of 04/2009
Figure 4: Thematic distribution of publications on simulation concerning pulp and paper [3] The number of publications concerning coating and paper properties is not only high compared to other subjects and has seen a high growth in numbers during the past four years. This reflects a clear focus on paper quality. Apparently paper quality is the second most important issue besides energy efficiency (and reduction in operational cost respectively) to be handled within scientific publications. Research orientation also here follows the priorities set by industry. The number of publications in all other topical areas has been very low. One reason might be that topics like waste paper, retention, pressing etc. are still also today in science are dealt without applying modelling and simulation techniques.
3 Applications of modelling and simulation in industry 3.1 Basic considerations To evaluate the current situation and the achievements available to date one has to take into account that any successful application of modelling and simulation is not only dependant on the availability of technical solutions in terms of algorithms, models and optimizers. This is only an imperious precondition. But as can be observed in many cases especially in pulp and paper it has taken us years to take up new ideas and solutions already available in academia or even in other industry branches. The reason for this is to be found when evaluating the other influencing factors present (Figure 5). The process itself has to be in shape to support the paradigm the application is intended to follow. As an example, faster grade change times not only require the availability of a model based controller but also of a most agile process setup that supports the intended trajectory. Otherwise the solution will be sub optimal with a low economic benefit. In addition the availability of data in terms of (online-) sensors and measurements is very important. J. KAPPEN, W. DIETZ
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Paper industry is significantly impeded in optimizing control by the simple fact that many of the key parameters still can not be measured online. In addition, data gained online have to be stored and kept easily available to any technologically driven query. DCS systems have to support this feature. Another aspect of data handling is the availability of tools for the evaluation of the quality of the collected data. Data preprocessing is one of the most critical steps in any modelling and simulation project. It needs a dedicated and skilful individual or team and a sufficient amount of time and money available. Only if all these preconditions are there the economical value itself then is decisive on whether an application will be successful or not. In summarizing this, an application will only be successful when all preconditions have been set in a favourable manner and this is oftentimes not the case. Developers tend to underestimate the necessities laid out above and brilliant technical ideas are in consequence never entering mill practice. Or even more provocatively: not the level of innovation but the synchronous fulfilment of all preconditions still today decides on the success of any application. As the success factors for modelling and simulation activities have been laid out the question is still open on why does a mill start such a project. It is in most cases a manager with foresight and involvement that has trust in this complex technology. Cost reduction and productivity improvement only help to justify the decision taken. This conclusion is based on the personal experience of the authors and upon statements made by participants of plenary discussions held during the conferences of Cost E36 [5]. economical priorities equipment and process related developments
data handling improvement
Applications training/education of staff on modelling and simulation techniques
priorities regarding the allocation of time and money developments achieved in online measurement
DCS accessibility and versatility currently available solutions in modelling and simulation
Figure 5: Influences and/or success factors on modelling and simulation applications
3.2 Current industrial applications The relevance of the influencing factors, named in the above section as being prerequisites for any industrial application, have been well understood by industry during the past few years indicating a clear change in attitudes in an industry that has for a long time carried the stigma of being a very conservative one. In consequence the pulp and paper industry is heavily engaged in providing data collection and storage capabilities and online measurements. As a consequence industry is J. KAPPEN, W. DIETZ
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installing data storage systems and online sensors. But, industry is still making very little use of the data available. Technologists and managers seem to be puzzled by the opportunities available through their investments without having a clear concept on how to use them. Taking for granted that if data are available the applications will be so as well. As this is not the case, they start asking application developers to suggest new applications. 3.2.1 Application cases In applications building two major streams of activities can be observed in mills. The strongest trend is to build up applications to improve online process performance and to optimise online product quality by using model based techniques. These are typically installed in larger mills and built up with the support of suppliers. In any case these are site specific solutions. Soft sensors are one result of these process optimisations projects. The soft sensors cover strength parameters, optical parameters and some more fundamental parameters like ash etc. [Figure 6]. These soft sensors fill in gaps in process information as they provide parameters that can not be measured on line. The expected performance in terms of accuracy is high. Data driven models suffer from low robustness and transferability to a new grade. Typically completely independent of the soft sensor applications and far more commonly installed are various solutions based upon the technique of model predictive control (MPC). Control action is improved by a model providing the capability to predict the measured value, well before this value of the real sensor is available within the process control system. These techniques provide an improved performance within any transition period (grade change, start up etc.) of the process, shortening it significantly. The models identified are normally data driven and do not need to reach a high level of accuracy. Only very few MPC applications include “real” soft sensors although any soft sensor built is an ideal basis for use within process control. It is obvious [Figure 6] that within the recent years, very fundamental parameters like basis weight, ash and moisture seem to take the lead. Only major publicised application projects are shown within the overview. Model based CD optimisation projects are not included.
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• Pulp&Paper • Application in a mill known/stated • In most cases long term benefit is not evaluated • CD-control excluded
Control
…
Aylesford/GB Retention 1173
Abitibi Stephensville/CA Fibre stock/paper quality 1157
…
Tembec/CA 1133 Pulp, wet end
Softsensors Appleton/WI, USA 1134 Opacity, strength, …
MD Ettringen/D Deinking 1569
NS Saugbrugg/NO BW, Ash 1156 (TNO)/NL Drying 891
SE Eilenburg/D Deinking 1460
(Metso) 1327 Grade changes
(Siemens)/D 1570 Drying
Hamburger Pitten/A BW, moisture 1452
Iggesund/GB pH, bend. stiffness 1379 Weig Mayen/D SCT 1568
2004
UPM Nordland/D Ash 1360
2006
2008
2010
Figure 6: Soft sensors and model based control applications in paper mills [6, 7, 8, 9, 10, 11, 12, 13, 14] A second very strong line of activities is to build up offline paper property prediction models based upon physical models and/or statistical data. These projects have no third party involvement and are operated on a high level of confidentiality. These projects are based on long term operational data and a very grade specific deep product property prediction related experience. Successful projects typically rely on more than ten years of data collected and expertise in application available. 3.2.2 Solutions offered by suppliers Besides proof of application in individual mill cases that is reported in literature the solutions offered by the various suppliers have been reviewed. Most of the offered solutions of model based optimisation are intended to stabilise a selection of one or a few target parameters (Figure 7). Only some of the solutions aim at the optimisation of sub plants. No clear proof could be found of any offered solution having a operating reference in paper production that is built and run for the purpose of mill wide optimisation. Typical optimisation objectives could be a minimisation of energy use or operational cost respectively and quality on demand. When looking at the offered solutions on model based optimisation to more detail it becomes clear that most of them target at providing control of basis weight, ash and moisture (BAM), reflecting the current trend in installed applications as named above.
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minimized cost / energy use; quality on demand
Mill-wide optimisation
Optimisation of sub plants
Siemens/D Deinking 1569
EposC/D Deinking 1460 Siemens 1570 Drying
Stabilisation of selected target params
…
ABB BAM, optical prop‘s EposC Honeywell BAM, whiteness, BAM, optical prop‘s Perceptive Engineering retention BAM, retention Metso Automation BAM, optical prop‘s, retention Voith Paper Invensys Paper prop‘s, retention BAM, retention
Pacific Simulation Fibre stock/paper quality 1157
Brainwave Whiteness
2004
• • • •
Pulp&Paper Real-time Commercially available CD-control excluded
BAM = Basis weight, Ash, Moisture. This list is not exhaustive.
2006
2008
2010
Figure 7: Model based optimisation solutions offered by suppliers that have already been applied in paper mills [15, 16, 17, 18]
3.3 Success factors In addition to the external factors as laid out in the above chapter there are success factors for the inner built of any industrial application and in the application building itself that are either to be directly addressed in current projects or even open up future options for a more successful application of modelling and simulation in the pulp and paper industry. These success factors as suggested by the authors are described and discussed in the following chapters. 3.3.1 Cost reduction as primary objective Any cost cutting approach will be favoured against other solutions that might have an appealing technical approach. As a consequence the old rule is still valid: Achieved benefit and cost of installation and the cost of operation are to be quantified in advance of and again after any project performed or service delivered. 3.3.2 Excellent project performance High product quality (reliability) and excellent service quality (response time etc.) have to be addressed and documented already in the conceptual phase of any application and have to be followed up in the following steps of design, built and installation. 3.3.3 Streamlining the development of modelling and simulation with process development Still today most of the applications are add on to existing production processes. Over time this should change. Already in the early conceptual phase of any new process, these techniques should be considered. Process layout should take into account performance improvement options available in terms of model based process control and optimisation. This will lead to reduced investment and operational cost. J. KAPPEN, W. DIETZ
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On the other hand models can be used to evaluate process design in the conceptual phase to minimise risk of and new project. Simulation models can be used to train personnel well before commissioning to smoothen and improve the start up curve. 3.3.4 Is there a communication gap to close? In their quest for innovation developers in R&D on a broad scale seem to take a development direction that differs quite significantly from that of the activities of industry. As a consequence it appears as if there is a communication gap open between both players: Key R&D development work has been going on for a long time in offline process design optimisation focussed on generic, first principle solutions, on offline quality improvement and the development of new paper properties or grades. This line of developments is triggered by the allocation of research funding authorities that put emphasis on the achievement of generic results with a highly scientific ambition. Within the area of modelling and simulation paper companies have focussed their attention on online process performance optimisation (model based or knowledge based solutions), on offline product cost reduction related property optimisation (data based or first principle) and on online product property optimisation. Could the closure of the gap bring along better solutions for industry? What could be the driver for a better understanding? Which role does the source of financing play? 3.3.5 Online use of existing models Many of the algorithms developed for offline use could be “taken online” and in this way help to produce a far higher value for the respective paper mills. Following this idea it can be suspected that currently a lot of value is kept on the shelf and spoiled as in many cases these opportunities have not been used. 3.3.6 Synchronous model based process optimisation Various models have been built up to serve individual optimisation purposes. In linking these, far more complex models would become available that allow to perform a global optimisation approach. In this way all relevant process performance and product quality related aspects could be included. The most important challenge here is to manage the resulting complexity. Key to it will be the proper sizing of the level of abstraction. Several suppliers already have expressed there views towards global optimisation, based upon mill or even company wide control systems. Feasibility seems to be there; the acceptance by the market is still to be proven. Little to no scientific proof regarding this development work is being delivered to date.
4 Outlook As has been described, paper industry seems to have reached an economic deadlock. At the same time many innovative solutions based on modeling and simulation techniques are offered to industry and several of these have already been realized. In addition to this, what is needed to support industry in its future development?
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One major step could come along with the “single unit” profitability approach: Models can determine product properties and the required raw material and process conditions beforehand via soft sensors. Thus downtimes and off spec production can be minimized by using these predictions in order to control the process. At the same time based on recent developments more agile process layouts are available. Model based control can make use of them and enable “grade/quality on demand” production. In consequence this means a change from growth ruled development to built-in intelligent cost reduction at the least possible production size - with an utmost flexible process as a basis. Secondly modelling and simulation could play a major role in bringing along a paradigm change in the pulp and paper industry: A change from incremental amelioration to revolutionary steps in product quality and process performance improvement could be possible. Justification may be provided by stating that modelling and simulation offer unique prediction capabilities. But still to date models have primarily been industrially used in optimisation rather than in design. In optimisation, key focus is the current process performance that falls short of expectation. In design the paradigm changes. Initial focus is given the product properties. From that a (paper) product design is derived, that defines its inner built. This information on the required physical structure of the product is then translated into an adequate process, capable of producing the (new paper) structure in an optimal way. By doing this, information on how grade specific properties are constructed is handed down into process design. All information regarding model based process control is therefore available right at the start. New products and the required new processes can be designed in parallel. The development times of new grades and new fibre based composite materials could significantly be reduced, mastering of the respective new processes could greatly be improved. This is an approach that is in use in other industry branches since long time. As tribute to the complexity of the fibrous material this is not yet the case for pulp and paper and significant research activity is required to achieve this goal. References [1] N. N.: Verband deutscher Papierfabriken; www.vdp-online.de [2] Weise U.: Die Herausforderung, Investitionsprojekte erfolgreich durchzuführen, PTS- Seminar Optimierung von Papiermaschinen, Munich, 14.-15.2.2007 [3] Combined search results of the literature databases Papertech (www.ptspaper.de) and Pirabase (www.pira.co.uk ) [4] Bergman MA, Johansson P.: Large investments in the pulp and paper industry: a count data regression analysis. Forest Economics 2002;8(1): 29-52. [5] www.coste36.org [6] Austin P., Mack J., McEwen M.: Increased production and improved quality on paper machines using advanced process control: some European case studies
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Control Systems/Pan-Pacific Conference, June 16-18, Vancouver, PAPTAC Canada (2008) [7] Hofmann F., Kummer B.: Multivariable Füllstoffregelung mit komplexem Bilanzmodell bei UPM Nordland. 17. Internationales Münchener PapierSymposium, 2.-4. April, München (2008) [8] Dietz W., Taylor M.: Practical requirements for data driven modelling Modelling and Simulation in the Pulp and Paper Industry, COST E36 final conference, 12-14 May, UCM Madrid 2008 [9] Steiner G.: Einsatz von Softsensoren zur Optimierung an einer Testlinermaschine 17. Internationales Münchener Papier-Symposium, 2.-4. April, München (2008) [10] Nuyan S.: Finally a sensible and agile solution for grade change. ipw - Das Papier 11, 28-30 (2007) [11] N.N.: Mit „Neuropredict“ Kartoneigenschaften zuverlässig ermitteln Corporate publication, PPI - Informatik, Sindelfingen, Germany, 2005 www.ppi-informatik.de/pdfs/weig.pdf [12] Austin P., Mack J., Bauer A., Marotte F.: Improved wet end stability and performance using multivariable model predictive control and optimisation at Papeteries de Clairefontaine. ATIP Conference, Bordeaux, France (2004) [13] Sinon A.M.J., Lo Cascio D.M.R.: On-line monitoring of the papermachine performance. PTS Symposium: Simulation and Process Control, Munich 2004 [14] Austin P., Mack J., Lovett D., Wright M., Terry M.: Improved wet end stability of a paper machine using model predictive control. Control Systems 2002, Swedish Pulp and Paper Research Institute, Stockholm, 80-84 (2002) [15] Fries E., Reinschke J., Sieber A.: Modellbasierte Optimierung zur Steigerung des Durchsatzes und Minimierung des Energieeinsatzes in Trockenpartien. 18. Internationales Münchner Paper-Symposium, 18-20. März, München (2009) [16] Mayer M., Kallich C.: Status quo and perspective of eMPC – an intelligent control tool for production excellence. Modelling and Simulation in the Pulp and Paper Industry, COST E36 final conference, 12-14 May, UCM, Madrid (2008) [17] Hauge T.A., Slora R., Lie B.: Application and roll-out of infinite horizon MPC employing a nonlinear mechanistic model to paper machines. Journal of Process Control 15:2, 201-213 (2005) [18] Sieber A., Mickal V.: Modellprediktive Regelkonzepte zur Optimierung in der Altpapieraufbereitung. PTS-Faserstoff-Symposium FS503, Erhard K., Meinl G. (Hg.), München (2005)
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