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Improved Quality and Productivity in Secondary Metabolite Fermentation Through Estimation, Control and Scheduling
7e-024
Copyright © 19961FAC 13th Triennial World Congress, San Francisco, USA
IMPROVED QUALITY AND PRODUCTIVITY IN SECONDARY METABOLlTE FERMENTATION THROUGH ESTIMATION, CONTROL AND SCHEDULING Zhang B.St, Tang Rt,Leigh J.Rt, Dixon K* and Hinge R.D:~
1
Industrial Control Centre, University of Westminster, London, UK *Pfizer Ltd, Sandwich, Kent, UK
Abstract The paper describes research into modelling, estimation, control and scheduling applied to a large fermentation plant producing secondary metabolites by a fed-batch process, Very extensive modelling and estimation work has been carried out. Only the most significant results are reported here but references to the detailed research are given. Experience of applying a novel advanced supervisory system to the industrial process is described. The software approaches to implementation at a production plant operated by Pfizer UK is also described. Key words: batch control, neural networks, expert systems, estimation, modelling, scheduling, fermentation processes, proccss control.
I. INTRODUCTION Large scale pharmaceutical manufacturers are under increasing commercial and political pressures to reduce costs. The resulting erosion of their profit margins is making them look for cfficicncies at all stages of production, with the particular aims of achieving consistently high product quality whilst maximising plant utilisation, through intelligent scheduling and control. The purpose of this paper is to describe our experience gained h'om a research project on Adaptive Supervision, Co-ordination and Control of Fermentation Processes, involving the secondary metabolite oxytetracycline (OTC) and our current research on the "total control" of this class () r fed- batch process. The main thrust of the project was to develop an estimation and control package to allow better control of nutrient addition to a fed batch fermentation processes. The Pfizer oxytetracycline process was used as the test bed around which to develop the system and efforts concentrated on controlling the carbon and nitrogen concentration throughout each batch. Research effort was concentrated on issues of process modelling, state estimation, predictive control, scheduling and data cross-validation. The central core of the research
6756
was concentrated on the development of an efficient set of neural network based state estimators for use on-line. These make it possible for the first time to continuously predictively control the nutrient values in the production vessels to desired levels with reduced variances compared with previous approaches. The models were developed using industrial data from the Pfizer production plant data base and our clear objective was to develop a system capable of operating on the production plant but of modular generic construction for case of transfer to other plants and products. A key feature is a Data Qualifier designed to validate online data before it is allowed to drive any important realtime function. Since the data coming from the plant sensors were affected by noise and error levels normal on a commercial production plant it was necessary to qualify the data prior to its use in the estimation models. This was achieved by developing an expert system package to identify erroneous data and flag suspected signals for further investigation by the process operators. Signals were automatically checked individually and then in groups to ensure that the various signals were in agreement with each other. The Data Qualifier is important in establishing industrial credibility for the whole supervisory system.
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An carlier approach used successfully by one of the authors for this type of problem in a steelworks application (the scheduling of ingots through twenty parallel batch soaking pits to Iced a single pseudo continuous rolling mill) was based on I~lst what-if? simulation of the outcomes of all possible scheduling decisions followed by semi-automatic choice of the bcst way forward based on the use of a suitable criterion. It is expected that a more systematic approach will be developed for the current problem using ideas from hybrid systems (Dimitris et al 1995), and from autonomous negotiating agents (McFarlane 1995) that have already shown considerable promise in manufacturing applications. In fact we belicve that process control and manufacture are converging inevitably and beneficially from their previous rathcr distinct stances to be seen as merely variations on the same theme; "total cost effective control of complex plants". 8. CONCLUSIONS Fermentation processes are ill defined and highly nonlinear. In this paper a neural network based system has been used to estimate and predict the state variables of the fed batch fermentation process. Very promising results of modelling and state estimation have been achieved in an industrial production plant using this approach due to the ability of neural networks to handle unccrtainties and nonlinearities in the process. The feasibility of exploiting these approaches further in fermentation process control is currently being i"urther cvaluated. Data qualification and fault detection aspects arc also considered. This neural network based system is built in to the portable software package Intelligent Process Management System (JPMS) which is currently running on a full scale production plant. The use of the IPMS system has been demonstrated to lead to improved productivity on an industrial plant. Overall, the system is part of a new generation of systems that will allow much more efficient integration of monitoring, scheduling, process control, quality control and diagnostic functions as well as more responsive day to day management of an cntire plant. Further the system will provide high quality purpose-derived data to assist short term and longer term husiness decisions and aid technical development of the plant to mcet future business challenges. In particular, this development represents a new generation of supervisory control systems for fermentation processes. With its aid, thc process can be run more safely and efficiently without needing the constant attention of process experts. ACKNOWLEDGEMENT Part of the research and development work described here has heen supported by a grant from the LINK Biochemical Engineering Committee and undertaken by Pfizer Ltd, Micro automation Ltd, and the Industrial Control Centre of the University of Westminster.
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REFERENCES Bhat, N.V., Minderman, PA, McAvoy, T. and Wang, N.S. (1990), "Modelling chcmical process systems via neural computation", IEEE Control Systems Magazine, April, 24-30. Dimitriadis, V D, Shah, Nand Pantelides CC (1995). 'Towards Systematic Methods for the Design of Controllers for Hybrid Systems". Preprints of IChemE Symposium, Advances in Process Control 4, York 27th, 28th September 1995. Fiacco, M, J aiel N A, Leigh J I, Leigh J R (1994), "Modelling and Control of the Fed-Batch Fermentation Process Using Statistical Techniques". 3rd IEEE Conference on Control Applications, University of Strathclyde, Glasgow Jale!, N.A. and Leigh, J.R. (1993), "Modelling the fed batch fermentation process using an artificial neural network", lCANN, Amsterdam, Holland. Jalel, NA Shui, F. Tang, R. and Leigh, J.R. (1994), "Using expert systems for on-line data qualification and state variable estimation for an industrial fermentation process", Proc.IEE Control'94, pp.1071-1075 J ohnson, A. (1987), "The control of fed batch fermentation process- A survey", Automatica, 23, 6, 691-705. Leigh, J.R. (editor), (1986), Modelling and Control of Fermentation Processes, Peter Peregrinus. McFarlane, D.C (1995), "Holonic Manufacturing in Continuous Processing : Concepts and Control Requirements", Proceedings of ASI '95, Portugal, June. Rumelhart, D.E. and McClelland, J. (1986), Parallel Distributed Processing, vol.l, MIT press. Tsaptsinos, D., Jalel, N.A. and Leigh, J.R (1992), "Estimation of state variables of a fermentation process via Kalman filter and neural network", Colloquium. University of Liverpool, March 11. Tsaptsinos, D. and Leigh, J.R (1993), "Modelling of a fermentation process using multi-layered perceptrons", 1. Microcomputer Applications, 16, pp.125-136 Zhang, B.S, Jalel, N.A and J.R Leigh (1994), "Application of learning control methods to a fed-batch fermentation process", Proc. lEE Int. Con! on Control, March, 21-24, Warwick, UK. Zhang, B.S, Tang, Rand Leigh, J.R (1995), "Modelling and control of a fed bateh fermentation process using neural networks and iterative learning methods", Proc. 6th Int. Con! on Computer Applications in Biotechnology, May 14-17, Garmisch-Partenkirchen, Germany.
Copyright © 19961FAC 13th Triennial World Congress, San Francisco, USA
IMPROVED QUALITY AND PRODUCTIVITY IN SECONDARY METABOLlTE FERMENTATION THROUGH ESTIMATION, CONTROL AND SCHEDULING Zhang B.St, Tang Rt,Leigh J.Rt, Dixon K* and Hinge R.D:~
1
Industrial Control Centre, University of Westminster, London, UK *Pfizer Ltd, Sandwich, Kent, UK
Abstract The paper describes research into modelling, estimation, control and scheduling applied to a large fermentation plant producing secondary metabolites by a fed-batch process, Very extensive modelling and estimation work has been carried out. Only the most significant results are reported here but references to the detailed research are given. Experience of applying a novel advanced supervisory system to the industrial process is described. The software approaches to implementation at a production plant operated by Pfizer UK is also described. Key words: batch control, neural networks, expert systems, estimation, modelling, scheduling, fermentation processes, proccss control.
I. INTRODUCTION Large scale pharmaceutical manufacturers are under increasing commercial and political pressures to reduce costs. The resulting erosion of their profit margins is making them look for cfficicncies at all stages of production, with the particular aims of achieving consistently high product quality whilst maximising plant utilisation, through intelligent scheduling and control. The purpose of this paper is to describe our experience gained h'om a research project on Adaptive Supervision, Co-ordination and Control of Fermentation Processes, involving the secondary metabolite oxytetracycline (OTC) and our current research on the "total control" of this class () r fed- batch process. The main thrust of the project was to develop an estimation and control package to allow better control of nutrient addition to a fed batch fermentation processes. The Pfizer oxytetracycline process was used as the test bed around which to develop the system and efforts concentrated on controlling the carbon and nitrogen concentration throughout each batch. Research effort was concentrated on issues of process modelling, state estimation, predictive control, scheduling and data cross-validation. The central core of the research
6756
was concentrated on the development of an efficient set of neural network based state estimators for use on-line. These make it possible for the first time to continuously predictively control the nutrient values in the production vessels to desired levels with reduced variances compared with previous approaches. The models were developed using industrial data from the Pfizer production plant data base and our clear objective was to develop a system capable of operating on the production plant but of modular generic construction for case of transfer to other plants and products. A key feature is a Data Qualifier designed to validate online data before it is allowed to drive any important realtime function. Since the data coming from the plant sensors were affected by noise and error levels normal on a commercial production plant it was necessary to qualify the data prior to its use in the estimation models. This was achieved by developing an expert system package to identify erroneous data and flag suspected signals for further investigation by the process operators. Signals were automatically checked individually and then in groups to ensure that the various signals were in agreement with each other. The Data Qualifier is important in establishing industrial credibility for the whole supervisory system.
6757
6758
6759
6760
An carlier approach used successfully by one of the authors for this type of problem in a steelworks application (the scheduling of ingots through twenty parallel batch soaking pits to Iced a single pseudo continuous rolling mill) was based on I~lst what-if? simulation of the outcomes of all possible scheduling decisions followed by semi-automatic choice of the bcst way forward based on the use of a suitable criterion. It is expected that a more systematic approach will be developed for the current problem using ideas from hybrid systems (Dimitris et al 1995), and from autonomous negotiating agents (McFarlane 1995) that have already shown considerable promise in manufacturing applications. In fact we belicve that process control and manufacture are converging inevitably and beneficially from their previous rathcr distinct stances to be seen as merely variations on the same theme; "total cost effective control of complex plants". 8. CONCLUSIONS Fermentation processes are ill defined and highly nonlinear. In this paper a neural network based system has been used to estimate and predict the state variables of the fed batch fermentation process. Very promising results of modelling and state estimation have been achieved in an industrial production plant using this approach due to the ability of neural networks to handle unccrtainties and nonlinearities in the process. The feasibility of exploiting these approaches further in fermentation process control is currently being i"urther cvaluated. Data qualification and fault detection aspects arc also considered. This neural network based system is built in to the portable software package Intelligent Process Management System (JPMS) which is currently running on a full scale production plant. The use of the IPMS system has been demonstrated to lead to improved productivity on an industrial plant. Overall, the system is part of a new generation of systems that will allow much more efficient integration of monitoring, scheduling, process control, quality control and diagnostic functions as well as more responsive day to day management of an cntire plant. Further the system will provide high quality purpose-derived data to assist short term and longer term husiness decisions and aid technical development of the plant to mcet future business challenges. In particular, this development represents a new generation of supervisory control systems for fermentation processes. With its aid, thc process can be run more safely and efficiently without needing the constant attention of process experts. ACKNOWLEDGEMENT Part of the research and development work described here has heen supported by a grant from the LINK Biochemical Engineering Committee and undertaken by Pfizer Ltd, Micro automation Ltd, and the Industrial Control Centre of the University of Westminster.
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REFERENCES Bhat, N.V., Minderman, PA, McAvoy, T. and Wang, N.S. (1990), "Modelling chcmical process systems via neural computation", IEEE Control Systems Magazine, April, 24-30. Dimitriadis, V D, Shah, Nand Pantelides CC (1995). 'Towards Systematic Methods for the Design of Controllers for Hybrid Systems". Preprints of IChemE Symposium, Advances in Process Control 4, York 27th, 28th September 1995. Fiacco, M, J aiel N A, Leigh J I, Leigh J R (1994), "Modelling and Control of the Fed-Batch Fermentation Process Using Statistical Techniques". 3rd IEEE Conference on Control Applications, University of Strathclyde, Glasgow Jale!, N.A. and Leigh, J.R. (1993), "Modelling the fed batch fermentation process using an artificial neural network", lCANN, Amsterdam, Holland. Jalel, NA Shui, F. Tang, R. and Leigh, J.R. (1994), "Using expert systems for on-line data qualification and state variable estimation for an industrial fermentation process", Proc.IEE Control'94, pp.1071-1075 J ohnson, A. (1987), "The control of fed batch fermentation process- A survey", Automatica, 23, 6, 691-705. Leigh, J.R. (editor), (1986), Modelling and Control of Fermentation Processes, Peter Peregrinus. McFarlane, D.C (1995), "Holonic Manufacturing in Continuous Processing : Concepts and Control Requirements", Proceedings of ASI '95, Portugal, June. Rumelhart, D.E. and McClelland, J. (1986), Parallel Distributed Processing, vol.l, MIT press. Tsaptsinos, D., Jalel, N.A. and Leigh, J.R (1992), "Estimation of state variables of a fermentation process via Kalman filter and neural network", Colloquium. University of Liverpool, March 11. Tsaptsinos, D. and Leigh, J.R (1993), "Modelling of a fermentation process using multi-layered perceptrons", 1. Microcomputer Applications, 16, pp.125-136 Zhang, B.S, Jalel, N.A and J.R Leigh (1994), "Application of learning control methods to a fed-batch fermentation process", Proc. lEE Int. Con! on Control, March, 21-24, Warwick, UK. Zhang, B.S, Tang, Rand Leigh, J.R (1995), "Modelling and control of a fed bateh fermentation process using neural networks and iterative learning methods", Proc. 6th Int. Con! on Computer Applications in Biotechnology, May 14-17, Garmisch-Partenkirchen, Germany.