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155 Modelling the oxytetracycline fermentation process using multi-layered perceptrons
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.
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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
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