Hybrid modelling of yeast production processes – combination of a priori knowledge on different levels of sophistication

Process models are used to formulate knowledge about process behaviour. They are applied, e.g., to predict the process' future behaviour and for state estimation when reliable on-line measuring techniques to monitor the key variables of the process are not available. There are different sources of information available for modelling, which provide process knowledge in different representations. Some elements or aspects may be described by physically based mathematical models and others by heuristically obtained rules of thumb, while some information may still be hidden in the process data recorded during previous runs of the process. Heuristic rules are conveniently processed with fuzzy expert systems, while artificial neural networks present themselves as a powerful tool for uncovering the information within the process data without the need to transform the information into one of the other representations. Artificial neural networks and fuzzy technology are increasingly being employed for modelling biotechnological processes, thus extending the traditional way of process modelling by mathematical equations. However, a sufficiently comprehensive combination of all these techniques has not yet been put forward. Here, we present a simple way of combining all the available knowledge relating to a given process. In a case study, we demonstrate the development of a hybrid model for state estimation and prediction on the example of a yeast production process. The model was validated during a cultivation performed in a standard pilot-scale fermenter.

[1]  Ka-Yiu San,et al.  Process identification using neural networks , 1992 .

[2]  Kazuyuki Shimizu,et al.  Neuro-Fuzzy Control of Bioreactor Systems with Pattern Recognition , 1992 .

[3]  S. Shioya,et al.  Optimization and control in fed-batch bioreactors , 1992 .

[4]  A. Johnson,et al.  The control of fed-batch fermentation processes - A survey , 1987, Autom..

[5]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[6]  John Villadsen,et al.  Modelling of microbial kinetics , 1992 .

[7]  M Kanda,et al.  Profile control scheme in a Bakers' yeast fed‐batch culture , 1985, Biotechnology and bioengineering.

[8]  Rimvydas Simutis,et al.  A fuzzy-supported Extended Kalman Filter: a new approach to state estimation and prediction exemplified by alcohol formation in beer brewing , 1992 .

[9]  Lyle H. Ungar,et al.  A hybrid neural network‐first principles approach to process modeling , 1992 .

[10]  M N Karim,et al.  Artificial neural networks in bioprocess state estimation. , 1992, Advances in biochemical engineering/biotechnology.

[11]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[12]  Dale E. Seborg,et al.  Nonlinear internal model control strategy for neural network models , 1992 .

[13]  Rimvydas Simutis,et al.  Fuzzy-aided neural network for real-time state estimation and process prediction in the alcohol formation step of production-scale beer brewing , 1993 .

[14]  Toshiomi Yoshida,et al.  Expert systems in bioprocess control: Requisite features , 1993 .

[15]  A J Morris,et al.  Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks). , 1993, Advances in biochemical engineering/biotechnology.

[16]  Y H Zhu,et al.  Neural network programming in bioprocess variable estimation and state prediction. , 1991, Journal of biotechnology.