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150 Enhanced industrial bioprocess monitoring through artificial neural networks
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Abstracts
142 Adaptive Estimation and Control of the Specific Growth Rate of a Nonlinear Fermentation Process via MRAC Method F.Y. Zeng, B. Dahhou, G. Goma, M.T. Nihtli~i, pp 363-366
146 Integration of Expert Systems and Neural Networks for the Control of Fermentation Processes S. Gehlen, H. Tolle, J. Kreuzlg, P. Frledl pp 379-382
The estimation and control of the specific growth rate is a fundamental task for optimizing the fermentation process. This paper presents a new approach based on MRAC (Model Reference Adaptive Control) method for realizing this difficult task.
Expert systems and neural networks are new tools for the control of fermentation processes. With expert systems the fermentation plant and the process itself is modelled via a generalized, qualitative system description based on the experience of human experts. On the other hand
neural networks and interpolating associative memories
143 A Kinetic Model of Mammalian Cell Cultures Xiachang Zhang, A. Vlsala, A. Halme, pp 367-370 A kinetic model of mammalian cell cultures is developed to describe the dynamics of animal cell growth and the formation of waste products and monoclonal antibodies in suspension fed-batch and batch cultures. The models of substrate consumption and product formation are set up on the bases of the metabolic phenomenal comprehension and mass balances. The model's parameters were estimated according to data obtained from a concrete mammalian cell fermentation. The model is compared by simulation with the experimental data.
144 Real-Time Application of Extended Kalman Filtering in Estimation and Optimization of a Recombinant Escherichia Coil Fermentation A.K. Hilaly, M.N. Karim, J.C. Linden, pp 371-374 Optimization of ethanol production from xylose by a recombinant E. coli has been studied in this work. Computer controlled fedbatch fermentations were carried out to investigate the effects of nutrient supply on cell metabolism. Pontryagin's Maximum Principle was used to determine an optimal feed policy to improve ethanol productivity. The extended Kalman Filtering technique, based on measurements of CO2. evolution and alkali addition rate, has been used to esttrnate the states of the system. The proposed fedbatch strategy was able to increase ethanol productivity significantly.
145 A High-Density Cell Cultivation of Escherichia Coil for the Production of Recombinant Human Interferon-B K. Ohtaguchi, H. Sato, M. Hirooka, K. Koide, pp 375-378 The gene coding for human fibroblast interferon-13 (IFN13), which represents antiviral activity and other biological activities attractive in clinical applications, have been expressed in Escherichia coll. To perform an effective production of recombinant IFN-B, a highdensity cultivation technique was developed using a stirred tank bioreactor with supplemental feeding of glucose and recycling of concentrated cell masses. Cells were continuously recovered from a sedimenter. A computer control scheme of the feeding profile was built deductively on the basis of the heuristic knowledge compiled from laboratory experiences. The high-density cultivation resulted in 19 g/l of dry cell masses and 144 mg/l of IFN-I~proteins.
can learn the process behaviour directly by process observation. The paper at hand reports how both control techniques can be combined for purposes like process supervision, modelling and optimization of biological plants.
147 Multiple Steady States in Continuous Cultivation of Yeast J.P. Axelsson, T. Miinch, B. Sonnleitner, pp 383-386 The occurrence of multiple steady states in continuous cultivation of yeast was investigated experimentally and a simplified model is presented that accounts for the behaviour. The model is a modification of the bottleneck model which explains steady state data over a wide range of dilution rates, as well as part of the dynamic behaviour. The model was analysed with respect to multiple steady states and a certain range of dilution rates was found where the culture can show a steady state both with ethanol production and without. Results from experiments in laboratory scale with Saccharomyces cerevisiae are shown and compared with simulations.
148 Knowledge-Based Planning of Mashing Profiles R.J. Aarts, pp 387-390 A prototype system that supports the planning of mashing profiles in breweries is presented. The system uses malt analysis results and the wort specification as inputs. From these parameters it generates a set of constraints in the profile. Constraint-based planning techniques are then used to construct the profile.
149 Adaptive Optimal Operation of a Bioreactor Based on A Neural Net Model Qi Chen, W.A. Weigand, pp 391-394 This pape.r .presents a novel approach to applying an online opumlzmg control strategy to a continuous fermentation process. The recursive backpropagation neural network (RBPN) identification algorithm with forgetting factor is proposed for the on-line modeling of the process. This technique has been successfully applied for the identification of nonlinear systems. Simulation studies of a continuous culture of baker's yeast were performed to test the RBPN-model-based adaptive optimization approach. The results show that the neural network model describes the nonlinear characteristics of the fermentation process very well. Finally, it appears that this approach is quite general and can be applied to other processes.
150 Enhanced Industrial Bioprocess Monitoring Through Artificial Neural Networks C. Di Masslmo-Peel, G.A. Montngue, M.J. Willis, A.J. Morris, M.T. Tham, pp 395-398 On-line estimation of fermentation process conditions is one area where industrial benefits could be gained by
Abstracts having a representative bioprocess model in the form of an artificial neural network. The need to achieve high levels of estimation robustness in the face of process faults is critical. Rapid model development enables multiple estimation schemes to be formulated in order to enhance robus~ess. Data from the industrial bioprocesses serve to demonstrate robust estimator performance when process instrumentation faults occur. Finally, with an accurate bioprocess model available, the system knowledge can he used for control design, in order to regulate the bioprocess at its 'optimal' conditions.
151 CAMBIO - A Knowledge-Based Software in Modelling and Estimation of Bioprocesses M. Farza, A. Ch~ruy, pp 399-402 CAMBIO (Computer-Aided Modelling of BIOprocesses), the software proposed in this paper, is a dedicated workstation initially designed for easy and interactive modelling and simulation of bioprocesses and presently extended to automatically design bioprocess estimators. Its main features are presented in this brief report.
743
155 Modelling the Oxytetracycline Fermentation Process using Multi-Layered Perceptrons N.A. jalel, D. Tsaptsinorb A.R. Mlrzal, J.R. Leigh, K. Dixon, pp 415-418 This paper illustrates how alternative neural network architectures were developed and applied to the modelling and estimation of the internal variable of the highly nonlinear oxytetracycline process. A wide range of results, together with methodological considerations, are provided.
156 Reasoning Assistant - An AI System Supporting Rational Experimentations of Fermentation Processes R. Oinas, A. Halme, pp 419-424 The Reasoning Assistant is a knowledge-based tool for an expert to help him in organizing and storing the logic of the fermentation process found thus far, and in testing new hypotheses obtained from experiments. In the Reasoning Assistant functions such as minimum, maximum, and mean can be used to describe the observations. The values can be presented with predefined natural language terms like normal, good, bad, etc. Observations can be attached to different phases of an experiment such as the starting phase, the production phase, Or the growth phase.
152 Learning of Rules from Fermentation Data R. Guthke, pp 403-406 For the knowledge acquisition module of an expert system algorithms including classification and statistical tests are developed and applied. Especially the hierarchical classification by average linkage method and the contingency table analysis by FISHER's exact test are applied for rule generation from fermentation data with a recombinant Bacillus subtilis strain.
153 On-Line Control System of Fed-Batch Culture with Culture Phase Recognition using Fuzzy Inference J. Horiuchi, M. Kamasawa, H. Miyakawa, M. Klshimoto, pp 407-410 An on-line control system with culture phase recognition based on fuzzy set theory was developed to realize versatile control of a bioreactor with culture phase transition during cultivation for enzyme production. The system makes it possible to recognize the current culture phase state in a bioreactor and to provide a suitable control policy corresponding to the current process state during fed-batch culture. The system was tested by using a fed-batch culture for a-amylase production. The experimental results showed that it was able to recognize a change of the microbial state as a culture phase transition and to control the feeding rate of the substrate.
154 Physiological State Control of Recombinant Amino Acid Production using a Micro Expert System with Modular, Embedded Architecture K.B. Konstantinov, R.M. Matanguihan, T. Yoshida, pp 411-414 The physiological state (PS) control concept provides a general framework for control of complex bioprocesses, based on on-line, knowledge-based identification of the PS of the cell culture. This approach is realized in a software containing a 'micro' expert system with an embedded architecture and applied to the control of fedbatch phenylalanine production.
157 Using Neural Networks for the Interpretation of Bioprocess Data G.K. Raju, C.L. Cooney, pp 425-428 This paper describes a neural network approach which "learns" to recognize patterns in fermentation data. Neural networks, trained using data from previous runs, are used to interpret data from a new fermentation. A task decomposition approach to the problem is proposed. Separate neural networks are trained to perform each task, including fault diagnosis, growth phase determination and metabolic condition evaluation. These trained networks comprise a multiple neural network hierarchy for the diagnosis of bioprocess data. The methodology is evaluated using experimental data from fed-batch, Saccharomyces cerevisiae fermentations. Each network can develop a task-specific representation which can lead to network activations and connection weights that are more clearly interpretable.
158 Application of Neural Networks to Variables Estimation and Stage Identification in Phenylalanine Production W. Ruenglertpanyakul, K.B. Konstantinov, T. Yoshida, pp 429-432 This report describes the application of a multi-module neural network structure to the estimation of unknown bioprocess variables, time-profile analysis and stage identification in recombinant phenylalanine production.
159 Fuzzy Control of Ethanol Concentration and its Application to Glutathione Production in Yeast Fed-Batch Culture H. Shimizu, K. Miura, C.G. AIfafara, S. Shioya, K. Suga, K. Suzuki, pp 433-436 The aim of this paper is to describe a fuzzy controller for ethanol concentration control in a yeast fed-batch culture. The proposed fuzzy controller can diagnose the state of fermentation and avoid the overfeeding and underfeeding of the substrate. Experimental evidence shows that
Abstracts
142 Adaptive Estimation and Control of the Specific Growth Rate of a Nonlinear Fermentation Process via MRAC Method F.Y. Zeng, B. Dahhou, G. Goma, M.T. Nihtli~i, pp 363-366
146 Integration of Expert Systems and Neural Networks for the Control of Fermentation Processes S. Gehlen, H. Tolle, J. Kreuzlg, P. Frledl pp 379-382
The estimation and control of the specific growth rate is a fundamental task for optimizing the fermentation process. This paper presents a new approach based on MRAC (Model Reference Adaptive Control) method for realizing this difficult task.
Expert systems and neural networks are new tools for the control of fermentation processes. With expert systems the fermentation plant and the process itself is modelled via a generalized, qualitative system description based on the experience of human experts. On the other hand
neural networks and interpolating associative memories
143 A Kinetic Model of Mammalian Cell Cultures Xiachang Zhang, A. Vlsala, A. Halme, pp 367-370 A kinetic model of mammalian cell cultures is developed to describe the dynamics of animal cell growth and the formation of waste products and monoclonal antibodies in suspension fed-batch and batch cultures. The models of substrate consumption and product formation are set up on the bases of the metabolic phenomenal comprehension and mass balances. The model's parameters were estimated according to data obtained from a concrete mammalian cell fermentation. The model is compared by simulation with the experimental data.
144 Real-Time Application of Extended Kalman Filtering in Estimation and Optimization of a Recombinant Escherichia Coil Fermentation A.K. Hilaly, M.N. Karim, J.C. Linden, pp 371-374 Optimization of ethanol production from xylose by a recombinant E. coli has been studied in this work. Computer controlled fedbatch fermentations were carried out to investigate the effects of nutrient supply on cell metabolism. Pontryagin's Maximum Principle was used to determine an optimal feed policy to improve ethanol productivity. The extended Kalman Filtering technique, based on measurements of CO2. evolution and alkali addition rate, has been used to esttrnate the states of the system. The proposed fedbatch strategy was able to increase ethanol productivity significantly.
145 A High-Density Cell Cultivation of Escherichia Coil for the Production of Recombinant Human Interferon-B K. Ohtaguchi, H. Sato, M. Hirooka, K. Koide, pp 375-378 The gene coding for human fibroblast interferon-13 (IFN13), which represents antiviral activity and other biological activities attractive in clinical applications, have been expressed in Escherichia coll. To perform an effective production of recombinant IFN-B, a highdensity cultivation technique was developed using a stirred tank bioreactor with supplemental feeding of glucose and recycling of concentrated cell masses. Cells were continuously recovered from a sedimenter. A computer control scheme of the feeding profile was built deductively on the basis of the heuristic knowledge compiled from laboratory experiences. The high-density cultivation resulted in 19 g/l of dry cell masses and 144 mg/l of IFN-I~proteins.
can learn the process behaviour directly by process observation. The paper at hand reports how both control techniques can be combined for purposes like process supervision, modelling and optimization of biological plants.
147 Multiple Steady States in Continuous Cultivation of Yeast J.P. Axelsson, T. Miinch, B. Sonnleitner, pp 383-386 The occurrence of multiple steady states in continuous cultivation of yeast was investigated experimentally and a simplified model is presented that accounts for the behaviour. The model is a modification of the bottleneck model which explains steady state data over a wide range of dilution rates, as well as part of the dynamic behaviour. The model was analysed with respect to multiple steady states and a certain range of dilution rates was found where the culture can show a steady state both with ethanol production and without. Results from experiments in laboratory scale with Saccharomyces cerevisiae are shown and compared with simulations.
148 Knowledge-Based Planning of Mashing Profiles R.J. Aarts, pp 387-390 A prototype system that supports the planning of mashing profiles in breweries is presented. The system uses malt analysis results and the wort specification as inputs. From these parameters it generates a set of constraints in the profile. Constraint-based planning techniques are then used to construct the profile.
149 Adaptive Optimal Operation of a Bioreactor Based on A Neural Net Model Qi Chen, W.A. Weigand, pp 391-394 This pape.r .presents a novel approach to applying an online opumlzmg control strategy to a continuous fermentation process. The recursive backpropagation neural network (RBPN) identification algorithm with forgetting factor is proposed for the on-line modeling of the process. This technique has been successfully applied for the identification of nonlinear systems. Simulation studies of a continuous culture of baker's yeast were performed to test the RBPN-model-based adaptive optimization approach. The results show that the neural network model describes the nonlinear characteristics of the fermentation process very well. Finally, it appears that this approach is quite general and can be applied to other processes.
150 Enhanced Industrial Bioprocess Monitoring Through Artificial Neural Networks C. Di Masslmo-Peel, G.A. Montngue, M.J. Willis, A.J. Morris, M.T. Tham, pp 395-398 On-line estimation of fermentation process conditions is one area where industrial benefits could be gained by
Abstracts having a representative bioprocess model in the form of an artificial neural network. The need to achieve high levels of estimation robustness in the face of process faults is critical. Rapid model development enables multiple estimation schemes to be formulated in order to enhance robus~ess. Data from the industrial bioprocesses serve to demonstrate robust estimator performance when process instrumentation faults occur. Finally, with an accurate bioprocess model available, the system knowledge can he used for control design, in order to regulate the bioprocess at its 'optimal' conditions.
151 CAMBIO - A Knowledge-Based Software in Modelling and Estimation of Bioprocesses M. Farza, A. Ch~ruy, pp 399-402 CAMBIO (Computer-Aided Modelling of BIOprocesses), the software proposed in this paper, is a dedicated workstation initially designed for easy and interactive modelling and simulation of bioprocesses and presently extended to automatically design bioprocess estimators. Its main features are presented in this brief report.
743
155 Modelling the Oxytetracycline Fermentation Process using Multi-Layered Perceptrons N.A. jalel, D. Tsaptsinorb A.R. Mlrzal, J.R. Leigh, K. Dixon, pp 415-418 This paper illustrates how alternative neural network architectures were developed and applied to the modelling and estimation of the internal variable of the highly nonlinear oxytetracycline process. A wide range of results, together with methodological considerations, are provided.
156 Reasoning Assistant - An AI System Supporting Rational Experimentations of Fermentation Processes R. Oinas, A. Halme, pp 419-424 The Reasoning Assistant is a knowledge-based tool for an expert to help him in organizing and storing the logic of the fermentation process found thus far, and in testing new hypotheses obtained from experiments. In the Reasoning Assistant functions such as minimum, maximum, and mean can be used to describe the observations. The values can be presented with predefined natural language terms like normal, good, bad, etc. Observations can be attached to different phases of an experiment such as the starting phase, the production phase, Or the growth phase.
152 Learning of Rules from Fermentation Data R. Guthke, pp 403-406 For the knowledge acquisition module of an expert system algorithms including classification and statistical tests are developed and applied. Especially the hierarchical classification by average linkage method and the contingency table analysis by FISHER's exact test are applied for rule generation from fermentation data with a recombinant Bacillus subtilis strain.
153 On-Line Control System of Fed-Batch Culture with Culture Phase Recognition using Fuzzy Inference J. Horiuchi, M. Kamasawa, H. Miyakawa, M. Klshimoto, pp 407-410 An on-line control system with culture phase recognition based on fuzzy set theory was developed to realize versatile control of a bioreactor with culture phase transition during cultivation for enzyme production. The system makes it possible to recognize the current culture phase state in a bioreactor and to provide a suitable control policy corresponding to the current process state during fed-batch culture. The system was tested by using a fed-batch culture for a-amylase production. The experimental results showed that it was able to recognize a change of the microbial state as a culture phase transition and to control the feeding rate of the substrate.
154 Physiological State Control of Recombinant Amino Acid Production using a Micro Expert System with Modular, Embedded Architecture K.B. Konstantinov, R.M. Matanguihan, T. Yoshida, pp 411-414 The physiological state (PS) control concept provides a general framework for control of complex bioprocesses, based on on-line, knowledge-based identification of the PS of the cell culture. This approach is realized in a software containing a 'micro' expert system with an embedded architecture and applied to the control of fedbatch phenylalanine production.
157 Using Neural Networks for the Interpretation of Bioprocess Data G.K. Raju, C.L. Cooney, pp 425-428 This paper describes a neural network approach which "learns" to recognize patterns in fermentation data. Neural networks, trained using data from previous runs, are used to interpret data from a new fermentation. A task decomposition approach to the problem is proposed. Separate neural networks are trained to perform each task, including fault diagnosis, growth phase determination and metabolic condition evaluation. These trained networks comprise a multiple neural network hierarchy for the diagnosis of bioprocess data. The methodology is evaluated using experimental data from fed-batch, Saccharomyces cerevisiae fermentations. Each network can develop a task-specific representation which can lead to network activations and connection weights that are more clearly interpretable.
158 Application of Neural Networks to Variables Estimation and Stage Identification in Phenylalanine Production W. Ruenglertpanyakul, K.B. Konstantinov, T. Yoshida, pp 429-432 This report describes the application of a multi-module neural network structure to the estimation of unknown bioprocess variables, time-profile analysis and stage identification in recombinant phenylalanine production.
159 Fuzzy Control of Ethanol Concentration and its Application to Glutathione Production in Yeast Fed-Batch Culture H. Shimizu, K. Miura, C.G. AIfafara, S. Shioya, K. Suga, K. Suzuki, pp 433-436 The aim of this paper is to describe a fuzzy controller for ethanol concentration control in a yeast fed-batch culture. The proposed fuzzy controller can diagnose the state of fermentation and avoid the overfeeding and underfeeding of the substrate. Experimental evidence shows that