MODELLING AND SIMULATION WITH INTELLIGENT METHODS Esko ...

MODELLING AND SIMULATION WITH INTELLIGENT METHODS Esko K. Juuso

Control Engineering Laboratory, Department of Environmental and Process Engineering, University of Oulu, P.O. Box 4300, FIN-90014 University of Oulu, Finland

Abstract: Intelligent methods have been used in modelling and control in various industrial areas. Smart adaptive systems consist of several interactive systems. Integrating intelligent methods with other modelling and simulation methodologies provides many new possibilities for combined process design and automation. Operating areas of existing phenomenological models can be extended and integrated. Difficult processes can be modelled in changing environments, even on-line adaptation of the simulation models is possible. Intelligent approaches combine data and expertise efficiently, and hybrid systems may combine smoothly different submodels developed for process phases, special situations or different variables. Both lumped and distributed parameter can include intelligent modules. A set of practical and interactive intelligent systems can be combined with other modelling and simulation methodologies to build practical simulators for industrial processes. Benefits of the integrated approach increase with increasing complexity. This paper has been prepared for the Sim-Serv ∗ roadmap of continuous and hybrid simulation. ∗

The World’s First Virtual Centre for Simulation, http://www.sim-serv.com

Keywords: intelligent methods, dynamic modelling, nonlinear systems, linguistic equations, fuzzy set systems, neural networks, industrial applications.

1. INTRODUCTION Mathematical modelling and simulation can improve dealing with materials, energy and information through better understanding of underlying mechanisms in the system. The trade-off between the necessary accuracy and resulting complexity becomes increasingly important when the nonlinear and multivariable behaviour must be taken into account.

need to be fairly light and flexible. The prototype simulators could be based more on intelligent methods. Engineering simulator is usually constructed with phenomenological modelling and it contains much more details. The problem is the validity of the parameters, and therefore, investment decisions for building a detailed engineering simulator are sometimes difficult to make. A smooth continuation from light prototype simulators would be much easier.

Simulation has been used in process and automation design for various purposes (Fig. 1). For a new application or process, the process development starts with feasibility study where the simulators

Automation design has traditionally done with separate simplified simulators but the present software and hardware performance provide a good basis for combined use of process and control

Fig. 1. Modelling and simulation in process and automation design.

Fig. 2. Building blocks of smart adaptive systems.

models. Automation projects can be intensified by connecting control systems to simulators.

pool of methodologies. Self-organisation could be a solution if the operating conditions do not fluctuate too much. Novelty and anomaly detection can be used for finding situations where readaptation is needed. However, human contributions should not be overlooked. Combining expertise with data-driven methodologies is essential in practical applications.

Simulators can be storages for the knowledge accumulated during the design process (Fig. 1). Training simulators are increasingly important in well operating processes since it is harder to get knowledge on difficult operating conditions when they are really exceptional. Training simulators support also process operation. Integration of simulators is not adequate in the present simulation practice since the simulators are not consistent. Intelligent models can form links between the simulators as they provide feasible solutions for collecting the experience during the design process. Data-driven modelling techniques will in this framework update the models during the operation as well. Continuous safety analysis and optimisation with simulators may also produce new ideas for process changes.

This paper summarises model approaches based on intelligent methodologies, presents some industrial applications of intelligent modelling and simulation and draws conclusions for the future research and development.

2. MODELLING Modelling traditions depend on the application areas: process design community relies on phenomenological models, automation design is usually based on data-driven methodologies, and computational intelligence has been applied in some special cases (Fig. 3). Computational fluid dynamics (CFD) is based on phenomenological models and is used in process design but its integration to other areas is difficult because of high computational requirements.

The industrial focus is on the smart use of intelligence in the process and production control by handling complexity with practical and interactive small scale systems. Combining functions and features of smart adaptive systems provide basis for adaptation. Adaptation to a changing environment and adaptation to a similar setting have been used for long time in process control. Classical on-line adaptation is not sufficient with strong and fast changes. Fuzzy self-organising approach extends adaptation possibilities but it is also too slow if the operating conditions are continuously changing. Dynamic intelligent simulation is a very fast and reliable method for tuning of the adaptation mechanisms. (Juuso, 2004b)

The amount of detail can increased in selected parts of the process by a decomposition procedure shown in Fig. 4. In an electric furnace application (Juuso, 1990), the process is on the first level decomposed into smaller subsystems, e.g. preheating, feeding ring, and electrical furnace. On the second level, the subsystems are divided into zones. On the third level, the spatial dependence is taken into consideration by using a rectangular grid. On the fourth level, detailed simulation models are applied to a volume element. The size of this element can range from very small to the entire zone area. The level of decomposition can be chosen quite flexibly.

Adaptation to a new/unknown application is a challenging task in complex systems. There are various data-driven methodologies to do this automatically. However, the future potential is in integration of functions and features in hybrid environment (Fig. 2). Connection alternatives are changing much faster than the functions and features. Methods are selected from an expanding 2

but some additional models are needed for selecting appropriate parameters.

2.2 Data-driven modelling Data-driven modelling approaches are based on general function approximators (black-box structures), which should capture correctly the dynamics and nonlinearity of the system. The identification procedure, which consists of estimating the parameters of the model, is quite straightforward and easy if appropriate data is available. Essentially, system identification means adjusting parameters within a given model until its output coincides as well as possible with the measured output. Validation is needed to gain confidence on the model. More details of the algorithms and theories are presented in (Ljung, 1999).

Fig. 3. Methodologies and application types of modelling and simulation (Juuso, 2004a).

Subprocesses HH XX  

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The structure and parameters of these models do not necessarily have any physical or chemical significance, and therefore, these models cannot be adapted to different operating conditions. However, the same identification techniques can also be used with intelligent modelling.

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2.3 Intelligent Modelling Computational intelligence can provide additional tools since humans can handle complex tasks including significant uncertainty on the basis of imprecise and qualitative knowledge. Intelligent methods are based on techniques motivated by biological systems and human intelligence, e.g. natural language, rules, semantic networks and qualitative models. Most of these techniques were already introduced by conventional expert systems.

Rectangular grids

Fig. 4. A decomposition procedure for multilevel modelling (Juuso, 1990). All these methodologies should be combined to get practical solutions for complex industrial processes. Emphasis could be on data-driven modelling techniques and computational intelligence as phenomenological models are difficult to adapt to changing operating conditions. (Juuso, 2004e)

Artificial neural networks (ANN) have been used as behavioural input-output models, which are difficult to connect to other models of the system (Juuso, 1996a). Possibility to a more or less automatic modelling has increased popularity of neural networks, and really a huge amount of software tools is available for neural computing. Neural computation belongs to the data-driven modelling techniques.

2.1 Phenomenological Modelling Physical (mechanistic, first-principle, ”white-box”) modelling is based on thorough understanding of the systems nature and behaviour that is represented by a suitable mathematical treatment. Real systems are usually too complex and poorly understood for a complete mechanistic modelling on an acceptable level of complexity. The obvious risk of unrealistic simulations is very dangerous in the subsequent steps of analysis, e.g. prediction and controller synthesis cannot be successful. The overall operation of these simulators is reasonable

Practical techniques for handling uncertainties and qualitative information have been achieved in the fuzzy modelling. Very complex nonlinear models can be constructed by fuzzy set systems. Data-driven modelling can also use ideas originating from neural networks, data analysis and conventional system identification. Building fuzzy models from prior knowledge involves various knowledge acquisition techniques originating from conventional expert systems. 3

Linguistic equation (LE) approach originates from fuzzy set systems: rule sets are replaced with equations, and effects of membership functions are handled with scaling. For nonlinear models, the scaling technique must be nonlinear as the model equations are linear. The scaling functions are called membership definitions as they have a close connection to the membership functions used in fuzzy set systems (Juuso, 2004e). Also distributed parameter models can be realised with linguistic equations (Juuso, 2004d).

control design (Juuso, 2004d). It extends the operability of the simulator to evaluating the controller performance for drastic changes, e.g. startups and large load disturbances, and local disturbances and malfunctioning. Experimental design techniques were utilised in developing a dynamic model for water treatment process. This model is used in an indicator of the quality of inlet water (Ainali et al., 2002), and a LE based dosing controller has been tuned with the corresponding dynamic simulator (Joensuu et al., 2004).

2.4 Hybrid Systems A mixed approach using both rigorous first principle simulation and black box modelling in an integrated environment seems to be a clear choice for complex systems. Statistical data modelling will recreate the behaviour that is not easily first principle modelled. Fuzzy logic and LE can help to adapt several scenarios to manage the discontinuities of the calculations. In optimisation and process control applications where time frame is important, it may be necessary to substitute first principle models that give fast response versus the slow and convergence problems of the traditional rigorous simulation. (Mac´ıas-Hern´andez, 2004)

2.6 Modelling for Forecasting Ringwood (2002) compared different intelligent systems for forecasting of electricity demand. A conclusion was that the intelligent systems must be applied intelligently. Intelligent systems offer many advantages: the function may be nonlinear, hard quantitative information is not necessarily needed, etc. Still, it has to be remembered that many conventional methods may provide a more parsimonious solution to the modelling problem. Adaptation to the changing operating conditions is included to this system. Various modelling methods have been compared for predicting the cooking result in continuous cooking (Leivisk¨ a et al., 2001).

Fuzzy modelling provides alternatives for hybrid systems, e.g. • Consequents can be dynamic state space models based on data-driven or analytical modelling. • Consequents can be neural models. • Semi-mechanistic models combine phenomenological process models with intelligent models of the model parameters.

Batch processes have additional requirements for forecasting the quality in a quite long horizon. Dynamic LE-modelling and simulation have been used for forecasting the final granule size in a fluidised bed granulator (M¨ aki et al., 2004). Granulation is very important process in pharmaceutical industry. Murtoniemi et al. (1994a, b) have also studied the same problem using neural networks. Dynamic LE-models have also been developed for forecasting the SuperBatch cooking result: residual alkali, lignin and dissolved solids. The simulator is adapted to the changing operating conditions with configurable parameters (Juuso, 2003b). For a fed-batch fermentation, a dynamic simulator is used on-line for predicting the process operation in a time window (Saarela et al., 2003a).

Linguistic equations make the consequent models nonlinear. Genetic algorithms can be used to optimise structures of other computational intelligence methods. 2.5 Modelling for Control Design A dynamic simulator was built for the control of a lime kiln (Juuso, 1999b). Lime kilns are used to convert lime mud to lime in paper industry. The implementation of an intelligent multilayer controller (J¨ arvensivu et al., 2001) was based on the tests with these simulators.

3. INTELLIGENT METHODOLOGIES Smart adaptive applications combine expertise and data (Fig. 5). Expert systems have been long time a feasible channel for introducing expertise to the applications. Fuzzy logic provides additional tools for using expertise in these systems. Neural computing started from data-driven modelling. Both fuzzy set systems and neural networks are coming closer: data-driven techniques have been

A dynamic simulator developed for a solar collector field has been used for controller design (Juuso, 2003a). Fast process disturbances do not allow on-line adaptation procedures. Test campaigns cannot be planned in detail because of changing weather conditions, i.e. operating conditions cannot be reproduced. A dynamic distributed parameter model has been developed for 4

• Linguistic fuzzy model (Driankov et al., 1993), where both the antecedent and consequent are fuzzy propositions, suits very well to qualitative description of the process as it can be interpreted by using natural language, heuristics and common sense knowledge. The input-output mapping is realized by the fuzzy inference mechanism equipped with conversion interfaces, fuzzification and defuzzification. • Takagi-Sugeno (TS) fuzzy model (Takagi and Sugeno, 1985), where the consequent is a crisp function of the antecedent variables, can interpreted in terms of local models. For widely used linear functions, the standard weighted mean inference must be extended with a smoothing technique. TS fuzzy models are suitable for identification of nonlinear systems. • Fuzzy relational model (Pedrycz, 1984), which allows one particular antecedent proposition to be associated with several different consequent propositions, can be regarded as a generalization of the linguistic fuzzy model. Each element of the relation represents the degree of association between the individual reference fuzzy sets defined in the input and output domains, i.e. all the antecedents are tied to all the consequents with different weights. • Singleton model, where the consequent is a crisp value, can be regarded as a special case of both the linguistic fuzzy model and the TS fuzzy model. Defuzzification reduces to the fuzzy-mean method.

Fig. 5. Methodologies of smart adaptive systems. introduced to fuzzy set systems, and utilisation of expertise is also an important topic in neural computing. Various neuro-fuzzy systems are examples of these synergy effects. Top-down and bottomup approaches are combined also in Bayesian networks (BN), i.e. prior knowledge and data. Hyperplane methods also combine data and expertise: linguistic equation (LE) systems originate from fuzzy set systems, and support vector machine (SVM) approach is based on statistical methods. Neural networks and genetic algorithms are integrated to the tuning algorithms. (Juuso, 2004e)

3.1 Expert systems Rule-based programming is commonly used in the development of expert systems but this paradigm leads to serious maintenance and testing problems in practical applications where rule-based systems become really massive. Therefore, linking the rulebased systems to more efficient modelling methods is essential for operability of the practical systems. Using declarative Prolog language (or extensions of it) on relation level can reduce amount of rules to some extent (Juuso, 1992). However, these simulations are still too slow for complex dynamical systems, and therefore, knowledge-based reasoning is embedded to fuzzy logic or linguistic equation applications.

All these models can approximate static and dynamic nonlinear systems. There are several alternatives for representation of the system’s dynamics (section 3.5). Fuzzy models can also be considered as a class of local modelling approaches, which attempts to solve a complex modelling problem by decomposing into number of simpler subproblems (Babuˇska, 1998). Fuzzy models can also be constructed from data, which alleviates the knowledge acquisition problem. Data-driven fuzzy modelling can be based on following methodologies: • Fuzzy clustering (Bezdek, 1981) can be used as a tool to obtain a partitioning of data. A large number of algorithms have been proposed, and applied to a variety of real-world problems. Methodologies, which decompose the problem into a set of locally linear models, are very suitable for constructing TS fuzzy models. • Self-organizing maps (Kohonen, 1995) can be interpreted as a clustering technique suitable for preprocessing before fuzzy rule genera-

3.2 Fuzzy modelling Fuzzy modelling is an extension of the expert system techniques to uncertain and vague systems (Zadeh, 1989). Fuzzy set systems continue the traditions of physical modelling on the basis of understanding the system behaviour. Fuzzy rules and membership functions can represent gradually changing nonlinear mappings together with abrupt changes. Fuzzy modelling is usually based on the following rule-based models (Juuso, 2004e): 5

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tion. Resulting neuron model can be generalized by linguistic equations (Juuso, 1996b). Antecedent membership functions can be generated from the results of fuzzy clustering. The consequent part of a fuzzy TS model is developed as a linearization around the cluster center (Babuˇska, 1998). Rule generation is usually based on membership functions defined in the procedure. Table-lookup scheme (Wang and Mendel, 1992; Wang, 1994) is a one-pass procedure for generating fuzzy rules from numerical I/Odata with capability to combine linguistic information into a common rule base. This methodology does not offer any means to identify the structure of the system. Fuzzy rule generation can also take into account contradictory data (Krone and Kiendl, 1994; Krone and Schwane, 1996). Neuro-fuzzy methods provide various techniques for generating fuzzy set systems, e.g. ANFIS method (Adaptive Network-based Fuzzy Inference Systems) is a well known neuro-fuzzy method which is suitable for tuning of membership functions (Jang, 1993). Fuzzy models on any fuzzy partition can be generated from linguistic equation models (Juuso et al., 1996). Fuzzy–ROSA (Rule Orientated Statistical Analysis) method (FRM) serves for a data– based generation of fuzzy rules which model a given input–output dependency (Kiendl, 1999). The basic idea of the FRM is to apply a relevance test to single fuzzy rules to assess their ability to describe a relevant aspect of the system under consideration. This reduces the problem of finding a good rule base to the problem of finding single relevant rules. Each rule with high relevance is supposed to express an important aspect of the system (Jessen, 2000; Slawinski et al., 1999).

Fig. 6. Classification of fuzzy set systems (Juuso, 2004e). membership functions and complicated rule sets are not solutions for a smart adaptive system. Self-organising fuzzy approach is a good technique if the key issue is to find the parameter values for a new operating point. • Fuzzy models can be considered as a class of local modelling approaches, which attempts to solve a complex modelling problem by decomposing into number of simpler subproblems. Changing operating conditions can be handled with a multimodel approach, and different clustering method can be used for finding suitable areas for modelling. Prior knowledge can used in constructing rulebased fuzzy models: qualitative knowledge can be incorporated in linguistic fuzzy models, or in fuzzy relational models if there are several alternative rules; locally valid linear models can be collected by TS fuzzy models. Linguistic fuzzy models are mostly used in the knowledge-based approach, and TS fuzzy models and fuzzy relational models for data-driven methods.

Different types of fuzzy set systems have been compared in (Juuso, 2004e). Classification is shown in Figure 6:

3.3 Artificial neural networks

• In the knowledge-based approach, understanding of the rules is important, and the number of rules is tried to keep in minimum. Nonlinear behaviour of the system should be included as much as possible in the distribution of the membership functions. The actual functions can be constructed from linear or nonlinear parts. The knowledge-based approach is based on nonlinear sets of membership functions and a simple set of rules. • The data-driven approach leads to a blackbox model if the interpretation of results is not addressed sufficiently. As understanding of the system is a very important benefit, approaches relying on linear sets of symmetrical

The most popular neural network architecture is the multilayer perceptron (MLP) with very close connection to the backpropagation learning (Rummelhart et al., 1986). Various optimisation methods have been used in these networks to speedup learning. In addition to the widely popular feed forward networks, various useful methodologies, e.g. self-organising maps and radial basis networks, are used in applications. Self-organising maps (SOM) (Kohonen, 1995) can be used for finding operating conditions or simply for clustering. Radial basis networks (Chen et al., 1991) provide an interesting alternative as they can be used both as a clustering tool and a modelling tool. 6

Overfitting is the main problem, the generalisation is tried to improve for example with ensemble learning with early stop (Lampinen, 1997) and regularisation techniques (Foresee and Hagan, 1997). Selecting the optimisation method is an important issue as very fast tuning may even prevent the validation stop. Precision is fairly good for small well-defined systems but problems arise in complex systems. Actually only small fragments of the overall system can be modelled at a time. Since fully representative data is very hard to get, ANNs can be recommended primarily to system development.

cients are extracted from data or defined on the basis of expert knowledge. The FuzzEqu toolbox contains tools for all the development and tuning stages described above (Juuso, 2000). It also contains routines for modifying membership definitions interactively to adapt the models to changing operating conditions and routines for building LE systems from large fuzzy systems including various rule blocks implemented in FuzzyCon or Matlab FuzzyLogic Toolbox. Other fuzzy modelling approaches can be used as channels for combining different sources of information.

A feasible approach is to generate a fuzzy set system from several specialised neural models. The ANFIS method based on the backpropagation method is an example of this generic methodology.

3.5 Dynamic modelling Dynamic fuzzy models can be constructed on the basis of state–space models, input–output models or semi–mechanistic models. In the state–space models, fuzzy antecedent propositions are combined with a deterministic mathematical presentation of the consequent. The most common structure for the input–output models is the NARX (Nonlinear AutoRegressive with eXogenous input) model which establishes a relation between the collection of past input–output data and the predicted output.

There is also a clear link to fuzzy set systems, i.e. both SOM and radial basis neurons can be represented as fuzzy rules. Radial basis models fill well the whole operating area, and fairly good results can be obtained if an appropriate number of neurons are used. Models based normalised data use symmetric activation functions and a linear layer combining the neurons. Connecting ANNs to other modelling techniques is vitally important as far as complex systems are concerned. A solution might be a neuro-fuzzy approach, which makes the model more understandable. Neural computing provides a suitable identification method for working point adaptation if the generalisation aspect is taken into account.

Multiple input, multiple output (MIMO) systems can be built as a set of coupled multiple input, single output MISO models. The antecedent parts of these subsystems should be consistent, and in some cases even completely identical. Delays can be taken into account by moving the values of input variables correspondingly, i.e. in the same way as in system identification.

3.4 Linguistic equations

where Xj is a linguistic level for the variable j, j = 1...m. The direction of the interaction is represented by interaction coefficients Aij . The bias term Bi was introduced for fault diagnosis systems.

The basic form of the LE model is a static mapping, and therefore dynamic LE models could include several inputs and outputs originating from a single variable. However, rather simple input-output models, e.g. the old value of the simulated variable and the current value of the control variable as inputs and the new value of the simulated variable as an output, can be used since nonlinearities are taken into account by membership definitions. Comparisons with different parametric models, e.g. autoregressive moving average (ARMAX), autoregressive with exogeneous inputs (ARX), Box-Jenkins and OutputError (OE), show that the performance improvement with additional values is negligible.

The membership definition is a nonlinear mapping of the variable values inside its range to a certain linguistic range, usually [−2, 2]. The mapping is represented with two monotonous, increasing functions, which must overlap in the centre at the linguistic value 0. In the present system, these functions are second order polynomials. Coeffi-

In the single model approach, also variables affecting to the working point of the model are included to the model. In small models, all the interactions are in a single equation. For larger models, the equation system is a set of equations where each equation describes an interaction between two to four variables.

Linguistic equation models consist of two parts: interactions are handled with linear equations, and nonlinearities are taken into account by membership definitions (Juuso, 2004e). The basic element is a compact equation m


Aij Xj + Bi = 0,

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Fig. 7. Results of a dynamic LE multimodel in a solar collector field (Juuso, 1999c).

Fig. 8. Degrees of membership for submodels (Juuso, 1999c).

3.6 Multimodel approach A multimodel approach based on fuzzy LE models has been developed for combining specialised submodels. The approach is aimed for systems that cannot be sufficiently described with a single set of membership definitions because of very strong nonlinearities. Additional properties can be achieved since also equations and delays can be different in different submodels. In the multimodel approach, the working area defined by a separate working point model. The submodels are developed by the case–based modelling approach.

Fig. 9. Cascaded modelling (left) and resulting model (right) (Juuso et al., 2000). .

The dynamic model of the solar collector field consists of four specialised LE models (Juuso, 1999c). The simulation result is very accurate for the average loop temperature (Fig. 7). The degrees of membership for submodels are obtained from the operating conditions by the working point model (Fig. 8). Smooth transitions between the submodels are based on fuzzy logic. This solution can be considered as an extension of Takagi-Sugeno type fuzzy systems.

Fig. 10. Dynamic simulator of a fed-batch fermentation process.

The cascaded modelling approach shown in Figure 3.6 is another approach developing multimodels. The Fuzzy–ROSA method (FRM) has been used in this way in a data–based generation of fuzzy rules for special situations in a solar plant application (Juuso et al., 2000).

3.7 Evolutionary computation Genetic algorithms (GA) can be used in optimisation of very large search space with noisy data and uncertain models. The GAs might not find an optimal solution, but they can find a satisfactory solution in challenging environment. Genetic algorithms can be used to optimise structures of other computational intelligence methods. They can be used for example in optimisation of an expert system rule base, optimisation of membership functions for a fuzzy set system, optimisation of neural network structures, and optimisation of membership definitions for a linguistic equation systems (Juuso, 2004e).

For fed-batch enzyme fermentation, three dynamic models were combined to form a simulator that predicts the dissolved oxygen (DO) concentration, the concentration of carbon dioxide in the exhaust gas, and the oxygen transfer rate (OTR) has been constructed (Fig. 3.6). All three dynamic models include three specialized submodels for different phases in the process. Similar structures have been used in models for cooking (Juuso, 2003b) and granulation (M¨ aki et al., 2004). 8

4. FUNCTIONS AND FEATURES Smart adaptive systems are based on intelligent subsystems (Fig. 11) which are designed for specific functions and features (Juuso, 2004e). Each function or feature can be realised with several methodologies and various connecting possibilities provide possibilities for building large interactive systems with can be considered smart (Fig. 2). Intelligent analysers (or software sensors) are used in making the existing measurements more efficient or in replacing the non-existing measurements with software systems that form the measurements signals e.g. from other, existing measurements, laboratory analyses and a priori expert knowledge. Modelling is an essential part of developing intelligent analysers. An intelligent analyser can be directly based on a dynamic model as presented in (Ainali et al., 2002).

Fig. 11. Intelligent subsystems of smart adaptive systems (Juuso, 2004e). Development of intelligent subsystems is widely based on modelling (Fig. 12): steady state modelling provides techniques for software sensors and even for trend analysis; dynamic modelling is needed for prediction of process operation, especially for control design; detection of operating conditions and diagnosis requires case-based models. Data-driven development of intelligent models can be done in a same way for dynamic modelling as it is done for steady-state modelling. Prediction of trends is an interesting new possibility in dynamic simulation. Advisory systems can be used even when the implementation of an on-line control is not possible. (Juuso, 2004e)

Detection of the operating conditions is closely related to the intelligent analysers. The origin of this approach is in fault diagnosis but the detected condition does not always mean a failure or fault, i.e. each case can simply mean different model or different control strategy. Fuzzy logic facilitates smooth transitions between operating conditions. The detection feature is important in batch processes (Juuso, 2004d; M¨ aki et al., 2004) and in semi-batch processes (Saarela et al., 2003a). In industrial applications, operating conditions are often changing so strongly that the changes in nonlinearities must be taken into account by intelligent control. Adaptation can be based predefined or on-line mechanisms and models (Juuso, 2004b). Also on-line identification has been used for adaptive control. Model-based predictive control is a general methodology for solving control problems in the time domain with following concepts: (1) a model is used for predicting the process output over a prediction horizon; (2) control actions are calculated over a control horizon in such a way that the predicted process output is as close as possible to a desired reference signal; (3) the first control action in sequence is applied in each step. Intelligent methods can be used at the modelling level, in optimisation and in the specification of the control objectives. Model-based intelligent actuators can improve the operation. The model development principles are similar to those used in intelligent analysers.

Smart adaptive systems need several of these intelligent subsystems, and the smartness of the systems depends strongly on interactions of the subsystems. A smart simulator would then be a hybrid simulator which also contains modules for analysing process measurements, modules for detecting changes, and modules for adapting the simulator to the changing operating conditions, e.g. the dynamic simulator of the batch cooking is adapted to the quality of wood chips (Juuso, 2003b).

5. INDUSTRIAL APPLICATIONS Modelling with intelligent methods is a successful approach for processes which are difficult to model from first principles. Biotechnical processes and processes in pharmaceutical industries, sustainable energy production and water treatment belong to this category. Also heavy industries, e.g. pulp and paper, steel, oil and gas, have subprocesses where intelligent modelling is beneficial but there is also another level: a large number of processes simulators must be integrated in such a way that a meaningful set of parameters are used in each simulator.

Dynamic simulation is primarily needed for comparing alternatives in controller design (Juuso, 1999a) but it can be also used in intelligent analysers(Ainali et al., 2002). Forecasting of the process operation is important in detecting operating conditions which provides information for model-predictive control and quality control. 9

Continuous cooking. During continuous cooking chips and chemicals are continuously fed from the top and removed from the bottom of the digester. Digester is divided to zones, in which different phases take place. The widely used quality variable, the Kappa number, can be predicted on the basis of these measurements before the end of the cook. As the digester process is far from linear and simple input-output system, the analysis must be nonlinear. Different approaches have been used for mathematical modelling of the cooking result (Isokangas and Juuso, 2000; Leivisk¨ a et al., 2001): fuzzy logic, partial least squares method (PLS), artificial neural networks (ANN) and linguistic equations (LE). In modelling the Kappa number in a continuous digester, all these methods seem to learn the process behaviour in a similar manner, but the LE models are the best in process environment since they can be adapted to various operating conditions in an understandable way. ANN and PLS models are sensitive for changes in process conditions, and fuzzy models need a large number of membership functions and rules that are too time-consuming to adapt.

Fig. 12. Development of intelligent applications (Juuso, 2004e).

Batch cooking. The linguistic equation (LE) model for a SuperBatch coking process is based on the measurements of the cooking liquor analyser CLA 2000. The simulation is done to the end of cooking sequence several times during cooking process. As the measurements are not continuously updated, the data set must be checked before starting the simulation. The on-line simulator calculates every time the values of alkali, lignin and dissolved solids to the end of the cooking sequence. The simulation is started on appropriate time intervals. Figure 14 shows an example of the simulation results in the end of the cooking sequence: simulations and in the first column, predictions of the end result in the second, and actual measurements in the third. Predictions are shown after each simulation run. (Juuso, 2003b)

Fig. 13. Chemical pulping (Juuso, 2004c). 5.1 Pulp and paper industry Pulp and paper industry has been in the pioneering role in the development of process automation. Digital automation systems provided tools for combining distributed control functions with centralised process monitoring. Introduction of intelligent systems to this kind of application environment has produced various integrated tools. Smooth operation of whole plant is essential because of several circulation flows between and within the subprocesses (Fig. 13). Intelligent functions and features have been used in various applications in fibre line, chemical recovery, paper mill, and water treatment (Juuso, 2004c).

The off-line tests are very fast: one cooking sequence takes some minutes and most of the time goes for showing the intermediate results similar to the on-line operation. According to the test results, the speed of the change depends strongly on the operating conditions. The dynamic simulator of the batch cooking is in on-line testing at an industrial digester house. According to extensive on-line tests in an industrial pulp mill, dynamic LE models suit very well for forecasting the cooking result, and the models are adapted to the changing operating conditions with configurable parameters. The on-line simulator of the cooking process is planned to be used in the control of alkali additions during the cooking sequence on the basis of the estimates of the residual alkali. The other predictions are planned to be used forecasting the quality of the pulp.

Applications from two pulp processes are described in this section: cooking is the major process in the pulp mill, and a proper operation of the lime kiln is a basis for smooth chemical recovery. Third important area is an efficient water treatment (section 5.7), which guarantees the operation of the important water circulation. All these processes are very difficult to model from first principles. The cooking model is used for forecasting, the lime kiln model for controller tuning, and the water treatment model for detecting different operating conditions. (Juuso, 2004a) 10

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Fig. 15. Simulation test for a lime kiln (Juuso, 1999b).

Fig. 14. On-line simulation of a SuperBatch cooking process (Juuso, 2003b).

5.2 Mineral and metal industry Steel making is an especially complex and wide process composed of very different subprocesses. Intelligent methods and data mining can help data analysis, modelling and optimisation virtually in any subprocess. The potential of intelligent systems is very high because of a huge amount of data to analyse. A considerable quality improvement of plates has been achieved in a rolling process by using a system which combines a prediction model of the mechanical properties and a design model. The prediction model is a model based on neural networks and SOM clustering, and the design model is an inverse model based on genetic algorithms (Ortega et al., 2004).

Lime kiln. Sodium compounds used in the pulp cooking are a significant cost item. Even in the early days of pulping, the sodium compounds were recovered from the black liquor and recycled back into the process. Black liquor is separated from pulp during the post-cook washing stages and then pumped to the evaporation plant for the removal of excess water and then onwards to the recovery boiler to be burned. The chemical smelt is dissolved into weak wash, forming green liquor. The green liquor is fed into the causticising plant to be processed further into white liquor for the cooking process. Lime circulation is needed in the chemical recovery cycle. The complex dynamics and multi-variable nature of the calcination process, with its nonlinear reaction kinetics, long time delays and variable lime mud feed characteristics, make the lime kiln process inherently difficult to operate efficiently.

The linguistic equation approach was originally developed for process design in metal industry, and the first models were developed for submerged arc furnaces used in the production of ferroalloys (Juuso and Leivisk¨a, 1992).

Lime kilns are used in paper industry to convert lime mud to lime in the recovery cycle. Dynamic simulators based on linguistic equations have been used in development and tuning of a multilayer linguistic equation controller for the control of the burning end of the lime kiln (Juuso, 1999b). A demanding test is to use the calculated values always as inputs on the following simulation step. The results are very good (Fig. 15): the only considerable differences are on the same time periods as in the previous test. According to analysis with additional simulation tests, these differences can be caused by fairly short disturbances in fuel quality. Another reason for this kind of behaviour could be an oscillation of kiln temperature caused by earlier control actions. The dynamic LE simulator was an essential tool in the tuning of the lime kiln control system, and also an intelligent analyser was developed for the quality of the bio fuel (J¨ arvensivu et al., 2001).

5.3 Oil and gas industry Oil and gas refining industry has a variety of characteristics that make it suitable for the application of smart adaptive systems. Mac´ıas-Hern´andez (2004) deals with application requirements, most suitable tools and opportunities for the application of intelligent technologies. Smart adaptive systems can be applied to several fields ranging from operations or process engineering to maintenance, accounting and supply chain. The measurements are highly collinear and noisy. Typically a high number of variables in a small range of variation must be taken into account. Connections between systems have widely tested in oil and gas industry (Mac´ıas-Hern´andez, 2003). Process control systems use standard buses and protocols. OPC has become the most interesting 11

client - server data connection for control systems and most of them are compatible with this technology. Information systems like centralised databases have interfaces with OPC as well.

[−0.19012 0.52315 −0.76128 0.10073]

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5.4 Biotechnical industry

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For fed-batch enzyme fermentation, all the subsystems shown in Fig. 3.6 are based on dynamic linguistic equation (LE) models. In these submodels, a fuzzy inference system chooses the best submodel to be used in each situation. The forecasting is based on on-line measurements, and the calculations are performed at appropriate time intervals. The model can predict the values in required hours in advance, so the operator or the control system has time to react to the changes in the system. In this application, a prediction horizon of one hour is used. The forecasting horizon can be specified freely on the basis of the control horizon. The dynamic LE models operate accurately even when the horizon is the whole fermentation time.

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Fig. 16. A dynamic LE model for temperature difference (Juuso, 2003a).

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Different data based modelling methods were compared earlier but only the models made by the linguistic equations approach succeeded in dynamic modelling of this process (Saarela et al., 2003a). The other methods tested were the Takagi-Sugeno fuzzy models and artificial neural networks (linear, perceptron, feedforward, and radial basic function networks, plus self-organizing maps). The results of the static modelling are reported more precisely in (Saarela et al., 2003b).

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Fig. 17. Temperatures of the collector loops during a load disturbance (Juuso, 2004d).

5.5 Pharmaceutical industry

error type controller tuning does not work since the operating conditions cannot be reproduced. Therefore, dynamic simulators are needed in controller design and tuning. Conventional mechanistic models do not work: there have problems with oscillations and irradiation disturbances.

Final granule size in a fluidised bed granulator has been forecasted by using dynamic LE modelling (M¨ aki et al., 2004) and neural networks (Murtoniemi et al., 1994a). Granulation is a very common sub-process in the tablet manufacture used to increase the particle size and to improve the flowability of pharmaceutical powders. Powder particles are agglomerated in the granulation process due to interparticle bonds caused by the addition of the granulation liquid.

The dynamic models of the solar collector field provide a smooth and accurate overall behaviour achieved with linguistic equations combined special situation handling with fuzzy systems (Juuso et al., 2000). The dynamic LE model of the normal operating area is represented in Fig. 16. The new adaptive control technique tuned with this simulator has reduced considerably temperature differences between collector loops. The new distributed parameter model is also aimed for control design (Juuso, 2004d). It extends the operability of the simulator to evaluating the controller performance for drastic changes, e.g. startup and large load disturbances, and local disturbances and malfunctioning. The distributed parameter model represents well differences between the loop temperatures (Fig. 17).

5.6 Sustainable energy Solar power plants should collect any available thermal energy is in a usable form at the desired temperature range, which improves the overall system efficiency and reduces the demands placed on auxiliary equipments. In cloudy conditions, the collector field is maintained in a state of readiness for the resumption of full-scale operation when the intensity of the sunlight rises once again. Trial and 12

Fig. 18. Calculation of the Water Quality Index (Juuso, 2004a). Fig. 19. Connecting different types of simulators with intelligent systems.

The operation of the multilevel LE controller is very robust in difficult conditions: startup and set point tracking are fast and accurate in variable radiation conditions; the controller can handle efficiently even multiple disturbances. Adaptive set point procedure and feed forward features are essential for avoiding overheating. The new adaptive technique has reduced considerably temperature differences between collector loops. Efficient energy collection was achieved even in variable operating condition (Juuso and Valenzuela, 2003).

6. FUTURE POTENTIAL Combined intelligent and mechanistic modelling and simulation is a practical solution for industrial applications. Benefits will increase with increasing complexity: • Intelligent methods are efficient in combining expertise and data. • Modelling of difficult processes in changing operating environments is the main area of intelligent methods. • Intelligent handling of parameters can improve the operating area of phenomenological models considerably. • Intelligent models are useful for making compromises which makes transitions between different model cases smoother and easier. • Integration of several subprocess models can be done on an aggregated level with intelligent models. • On-line adaptation of simulators can be done with intelligent methods.

5.7 Water treatment Internal water circulation is essential for an integrated pulp and paper mill as washing is done in many stages of the process. Water in an internal water circulation is usually treated chemically in a flotation basin. The amount and quality of water fluctuates greatly depending on the process conditions. The water can be kept in the circulation if the treatment is capable to clean it. All disturbance of the purification result will later cause new disturbances in the pulping or paper making processes.

The system integration leads to a hybrid system: fuzzy set systems move gradually to higher levels, neural networks and evolutionary computing are used for tuning, and the whole system reinforced with efficient statistical analysis, signal processing and mechanistic modelling and simulation. Detection of fluctuations is very useful in fault diagnosis and adaptive control.

The technology of control systems for chemical dosage in the water treatment processes is in a relatively low level. In general, methods of dosage control can be far from ideal, leading occasionally to inefficient plant operation, occurrence of unnecessary costs and in some cases decreasing water quality. An intelligent water quality indicator described in (Ainali et al., 2002) results an impurity factor of inlet water in the range of [-2 2], which corresponds to the properties from extremely pure, pure, slightly pure, normal, slightly impure, impure to extremely impure. Experimental design technique was utilised in developing the models. Goodness of the models was analysed carefully by independent data. The results show that the indicator describes well the quality of inlet water (Fig. 18). This indicator replaces expensive analysers and makes the controller adaptation very fast. A LE controller has been tuned with this simulator (Joensuu et al., 2004).

Intelligent methods provide new building blocks to be used together with other methods of modelling and simulation. Decomposition and smooth (or abrupt) transitions between the detailed models are the main tasks of the intelligent models. Models can be based on ordinary or partial differential equations (Fig. 19). Linking steady state flowsheeting with dynamic simulation would improve handling of processes consisting of large number of subprocesses. Integration of intelligent models with distributed parameter models may bring the computational fluid dynamics close to real time applications. 13

ACKNOWLEDGEMENTS The author wishes to thank the European SimServ project for the opportunity to write this paper.

REFERENCES Ainali, I., M. Piironen and E. Juuso (2002). Intelligent water quality indicator for chemical water treatment unit. In: Proceedings of SIMS 2002 - the 43rd Scandinavian Conference on Simulation and Modelling, September , 2002, Oulu, Finland. pp. 247–252. Aachen. Babuˇska, Robert (1998). Fuzzy Modeling and Identification. Kluwer Academic Publisher. Boston. Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function. Plenum Press. New York. Chen, S., C.F.N. Cowan and P. M. Grant (1991). Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2, 302–309. Driankov, D., H. Hellendoorn and M. Reinfrank (1993). An Introduction to Fuzzy Control. Springer. Berlin, Germany. Foresee, F. D. and M. T. Hagan (1997). GaussNewton approximation to Bayesian regularization. In: Proceedings of the 1997 International Joint Conference on Neural Networks. pp. 1930–1935. Isokangas, A. and E. K. Juuso (2000). Development of fuzzy systems from linguistic equations for kappa number prediction in continuous cooking. In: Proceedings of TOOLMET 2000 Symposium - Tool Environments and Development Methods for Intelligent Systems, Oulu, April 13-14, 2000 (L. Yliniemi and E. Juuso, Eds.). Oulun yliopistopaino. Oulu. pp. 257–270. Jang, J.-S. R. (1993). ANFIS: Adaptive-Networkbased Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685. J¨ arvensivu, M., E. Juuso and O. Ahava (2001). Intelligent control of a rotary kiln fired with producer gas generated from biomass. Engineering Applications of Artificial Intelligence 14(5), 629–653. Jessen, H. (2000). Test and rating strategies for automatic fuzzy rule generation and application to load prediction. In: New Frontiers in Computational Intelligence and its Applications. pp. 11–21. Joensuu, I., M. Piironen and E. Juuso (2004). Adaptive feedback controller for dosage of water treatment chemicals. In: Proceedings of AFNC’04 - the 2nd IFAC Workshop on Advanced Fuzzy/Neural, September 16-17, 2004,

Fig. 20. Connecting process design, automation and information systems with intelligent systems. Extensions to multidomain simulation will increase the importance of intelligent modelling methodologies since the parameters of the models do interact, and these interactions are not easy to model. Modelling and simulation approaches in process design and automation are becoming more and more united. Automation systems use standard buses and protocols, and OPC has become in this are the most interesting client - server data connection. Information systems like centralised databases have interfaces with OPC as well. We will see in the future business applications of intelligent systems using OPC as its standard of communication. Intelligent systems are possible links between all these different domains: process design, automation and management (Fig. 20). Exchange of information is not possible in practice without data-driven modelling and simulation. A set of practical and interactive intelligent systems can be combined with other modelling and simulation methodologies to build practical simulators for industrial processes.

7. CONCLUSIONS Intelligent methods have been used in modelling and control in various industrial areas. These methodologies provide many new possibilities for combined process design and automation. Difficult processes can be modelled in changing environments, even on-line adaptation of the simulation models is possible. Intelligent approaches combine data and expertise efficiently, and hybrid systems may combine smoothly different submodels developed for process phases, special situations or different variables. Both lumped and distributed parameter can include intelligent modules. Benefits of the integrated approach increase with increasing complexity. 14

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