Artificial intelligence in the control of a class of fermentation processes

Copyright © IFAC Artificial Intelligence in Real-Time Control, Delft, The Netherlands, 1992

ARTIFICIAL INTELLIGENCE IN THE CONTROL OF A CLASS OF FERMENTATION PROCESSES N.A. Jalel, F. Shul, D. Tsaptslnos, R. Tang, W. Vanichsrlratana, J.R. Leigh Industrial ControlCentre, Polytechnic of Central London, London, UK

Abstract. The control problems that arise in a fed- batch process are caused by the poorly understood nature of the process, its nonlinearity, the wide range of operating states passed through during a batch and the unmeasurability of key process variables. In this paper alternative approaches to modelling and state estimation of the process will be outlined and a summary given of the comparative performance of numerically based and neural net derived models. A Self organising system based on fuzzy logic controller has been adapted to control the state variables of the process. The feasibility of using pattern recognition for modelling and state estimation of the process will be illustrated. Finally a brief treatment on the software structure, including expert system shells, that will allow these emerging AI techniques to be applied in real time to the fermentation process will be described. Keywords. Expert Systems; Fuzzy Control; Modeling; Neural Nets; Pattern Recognition non-measurable variables and the model parameters.

INTRODUCTION In the fermentation process there are three types of variables, the controlling inputs to the fermenter such as feeds, pH, temperature, etc., the measured outputs in the gas components leaving the fermenters such as oxygen, carbon dioxide, etc. and the state variables, such as biomass, glucose and product concentration.

In this contribution, the feasibility of using different techniques for modelling the Oxytetracycline (OTC) fermentation process are investigated. An identification technique is applied where autoregressive models have been developed from the process data. Neural networks have been developed to model the process. To control the state variables around a desired trajectory selforganising fuzzy logic controller has been used.

The problem with the industrial fermentation process is to find a model to represent the unmeasurable state variables and to design a suitable controller for the process. In controlling a fermentation process the most difficult problem is to determine its current state. This problem can be overcome if a suitable process model can be found for then an estimator can be used to determine the

THE AUTOREGRESSIVE (AR) MODEL FOR THE FERMENTATION PROCESS The identification approach, Autoregressive (AR), is adopted to develop a model for the batch process from the available data [1]. For 441

been tested on one batch of data and Figure 4 illustrates the off-line measurement and the model output for the potency.

the OTC fermentation process two models have been derived to describe the process. In the first a model has been derived to express the relation between the inputs, the carbon dioxide evaluation (RCO~ and the carbon fed, and the unmeasurable state variables, potency, residual carbon, and residual nitrate. The second model is used to express the relation between the state variables and the measured output oxygen uptake rate (RO~ Figure 1. The Kalman filter has been applied to estimate the unmeasurable state variables, in which the A, B, and C matrices and the model output (ROJ have been used to derive the Kalman filter [2]. Figure 2 illustrates the model output, Kalman filter estimation and the offline line measurement for the potency. Since fermentation is a time varying process, sequential modelling approach has been adopted. It has been decided to divide the fermentation process into three phases. Each phase has been treated separately in that a model for each has been derived. The unmeasurable state variables of the process have been taken to be the combination of the three phases. Of course, an important point is to find the change-over between the three phases. The change-over point between phases was chosen by inspection of the data. Figure 3 illustrates the offline measurement, the model output and the Kalman filter estimation for potency.

In the other approach, a varying window technique is used to model the process. In this approach an AR model has been derived over a data window which represent one phase of the process, The window is moving by a fixed step along the time axis of the process data. After each overlapping a new AR model has been derived. At each moment of time, several overlapping AR models have been derived and the average of them is used to represent the process. Figure 5 illustrates the offline measurement and the model output for potency. SELF ORGANIZING FUZZY LOGIC CONTROLLER The basic design of self organizing fuzzy logic controllers has been described in many papers [3,4]. It is composed of two levels, the first containing a simple fuzzy logic controller and the second containing the self organizing mechanism. There are many parameters involved in the design of SOFLC such as fuzzification, the choice of the input and the output variables, the type of the fuzzy control rules, defining the implication and inference procedure, and the defuzzification procedure, Figure 6.

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The input signals to the controller taken at each sampling instant are, the error signal calculated by subtracting the process output from the set-point, and the change in error calculated .~y subtracting the error of the last sample from the present one. The signal is then mapped to the corresponding discrete level by using the error and change in the error scaling factors and prior to rule evaluation. The output signal is scaled to a real value using the output scaling factor and fed to the process being controlled.

The identification technique is a linear approach while fermentation is a highly non-linear, time varying and uncertain dynamic process. In this work, different techniques are investigated to create a general non-linear model to represent the process over the whole operating range. The approaches are based on transforming the linear model derived using the AR technique into a non-linear model. In the first, the constants of the A and B matrices for the three models derived using the sequential modelling has been expressed in a time series polynomial. So instead of having three linear constants for the coefficients of the A and B matrices a time variant polynomial has been derived which represent the constants. The derived polynomial is used to represent and cover the time varying of the process over the whole operation. The described approach has

The control rules are usually viewed as linguistic conditional statements and symbolised in the form of a relational matrix R given by the Cartesian product. R =

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obtained from its inputs, the error (E) and the change in the error (CE) using Zadeh's compositional rules of inference U = (Ek

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evaluate the process performance, and recognize fermentation phases using on-line measurements.

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The control strategy could be accomplished using the integration of adaptive control and pattern recognition techniques. The basic framework is essentially a generic self-tuning controller containing three modules: A plant identification module; a controller design module; and a control implementation and validation module.

Then the output is defuzzified using defuzzification procedure to obtain a control action. SOFLC has been applied to control a desired trajectory of the OTC product, by controlling the amount of Carbon fed inside the fermenter and using the derived AR model, described previously, to estimate the process. Figure 7 shows the desired trajectory and the process output of the product. From this figure it is possible to indicate that SOFLC has achieved a good control strategyin following the desired trajectory.

The identification module uses the on-line measurements to select a non-parametric model based on historical data. This is followed by a controller design module to design a control rule, and finally a control implementation and validation module will be used to implement the control ruleand evaluate the control action. This information would feedback to the first module to conform the pattern recognition process as well as the control rules. Hence, this controller would have learning and selforganising capability with the advantage of effectiveness, high insensitivity to noise and operational simplicity for fermentation process control.

PATTERN RECOGNITION FOR MODELLING AND CONTROL OF FERMENTATION PROCESS Pattern recognition is an alternative approach for ill-defined process modelling, control and diagnosis. It can be regarded as a procedure for mapping a pattern correctly from pattern space into class membership space. Usually it comprises of two distinct steps as described by Pao [5] and illustrated in Figure 8.

ARTIFICIAL NEURAL NETWORKS The objectives of the research are twofold. First, the suitability of neural networks for the modelling and control of the OTC fed-batch fermentation process isbeing investigated. This part of the work has resulted in a number of publications where the use of multi-layered perceptrons (MLP) employing the backpropagation learning algorithm has been reported. The employment of different architectures, such as recurrent ANN's, will be explored in the future. The second objective is the foundation of a generic methodology for the development of ANN's. Obviously thetwo objectives are related. The need for a methodology arises when one considers the vast number of options confronting a user.. For instance, the topology of the network and the selection of the appropriate inputs and training set are two aspects that need addressing.

There are two principal ways by which one can design a pattern recognition system. Firstly, the supervised approach consists of gathering representative patterns from each acceptable category and using these patterns to adaptively 'train' the machine to recognize the sample sets. Secondly, the unsupervised approach deals with techniques thataccomplish learning without a prior knowledge of the categories present in the sample sets. Pattern recognition technique could be used to obtain an accurate and robust identification model of the state variables for the fermentation process. The unsupervised approach is applied to analysis process data to get a prior knowledge of the process, for example, classify the process into normal or abnormal classes, or subclassify normal processes into several other classes. Eventually, the supervised approach would be used to diagnose the abnormal process,

In our research the use of correlation analysis between inputs and weights hasbeentested and proved to be a useful tool for post-pruning a neural network [6]. Correlation analysis assists 443

towards: Matching of the topology of a neural network to a particular problem; the identification of networks which learn faster; the recognition that a network does not improve its performance and that changes are required; and the elimination of hidden nodes, input nodes and connections.

suggest desired control values to the process operator. The function of the DQ knowledge source is to ensure that all the data collected from the batch are reasonable for use by the subsequent knowledge sources. This could also result in providing early fault detection about measurement devices.

Other post-pruning techniques have been attempted as well, for example weight analysis and activation analysis [7].

The PE unowledge source is required to determine the unmeasurable state variables as well as providing information about the state of the batch. This can be accomplished by using the modelling techniques described above to estimate the state variables on-line. Further, information about the productivity of the batch at this moment in time or how healthy is the batch could also be determined by this knowledge source. Alarms would be generated if unusual activities (for example, the batch is biological contaminated) inside the fermenter is concluded.

Alternative input schemes have been tried for the modelling of the product concentration of the OTC process. The present values of the feeds, the present and previous values of the feeds, and the present values of the two feeds plus a third input which received the input were three such schemes [8]. Regarding the representation of the dynamic we are considering three alternative approaches: recursive nets, time staggered multiple inputs and dynamicaly enhanced neural nets. Our evaluation of these approaches is continuing.

The third knowledge source PC, is used to determine the desired control values in order to maximize the production of OTC. Additionally, the system requires a SC to manage the sequence of knowledge sources execution.

Comparisons between ANN and Kalman filters demonstrated that with an appropriate learning set the ANN's will outperform the identification approach. This is mainly due to the ability of ANN's to handle the nonlinearities of the data set. On the other hand the use of an identification technique within an overall control strategy is well defined whereas the integration of ANN models into control architectures need to be further investigated [9].

With the system architecture designed in such a way that it could be implemented by one software tool or another. COGSYS Real-Time Expert System Shell has been selected for this project. CONCLUSION

The use of machine learning algorithms as tools for identifying relevant inputs and for modelling are also investigated. In particular the AIM tool which synthesizes abductive networks is currently evaluated.

This paper examined various approaches for modelling and estimation of the state variables in the process. These included numericallybased and neural-net derived models. SOFLC has been used to control the state variables of the OTC fermentation process. The feasibility of using pattern recognition in the fed-batch process has been described. A brief description of the intelligent software structure that will allow the emerging AI techniques to be applied in real time has also been illustrated.

THE INTELLlGENT SYSTEM APPROACH With the above modelling and control methodology described, the software structure for the Intelligent Supervisory Monitoring And Control System is illustrated in Figure 9. Three knowledge sources (Data Qualifier (DQ), Process Estimator (PE) and Process Controller (PC» and a System Controller (SC) are required to accomplish the task of data screening, state variable estimation and to 444

REFERENCES

1. Mirzai A.R., Dixon K., Hinge R.D., Leigh J .R. (1991). Approaches to the Modelling of Biochemical Processes. lEE International Control Conference. Edinburgh. U.K.

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2. Leigh J .R. (1985). Applied Digital Control. Prentice-Hall.

2. Zadeh L.A. (1973). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems. Man. and Cybernetics, Vol. SMC-3, No. 1.

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4. Procky TJ., Mamdani E.H. (1979). A Linguistic Self-Organizing Process Controller. Automatica, Vol. 15, pp. 15-30.

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5. Pao Y.H. (1989). Adaptive Pattern Recognition and Neural Network, AddisonWesley. 6. Tsaptsinos D., Mirzai A.R., Leigh J.R. (1992). Matching theopology of a neural net to a particular problem: Preliminary results using correlation analysis as a pruning tool, accepted for publication in ICANN'92 conference. Brighton. England.

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7. Tsaptsinos D., Jalel N.A., Leigh J.R., Mirzai A.R. (1992). Neural networks for estimating fermentation state variables, accepted for publication in AIENG'92 conference. University of Waterloo. Canada.

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8. Jalel N.A., Tsaptsinos D., Mirzai A.R., Leigh J.R., Dixon K. (1992). Modelling the Oxytetracycline fermentation process using multi-layered perceptrons, accepted for publication in ICC AFT 5/IFAC.BIO 2 conference. Keystone. Colorado.

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9. Tsaptsinos D., Jalel N.A., Leigh J.R.(l992). Estimation of state variables of a fermentation process via kalman filter and neural network, Colloguium on the application of neural networks to modelling and control, Liverpool University/Polytechnic.

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