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Hybrid modelling of yeast production processes – combination of a priori knowledge on different levels of sophistication
Author(s) -
Schubert Jörg,
Simutis Rimvydas,
Dors Michael,
Havlík Ivo,
Lübbert Andreas
Publication year - 1994
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.270170103
Subject(s) - process (computing) , computer science , heuristic , process modeling , artificial neural network , a priori and a posteriori , artificial intelligence , machine learning , data mining , heuristics , rule of thumb , fuzzy logic , process state , key (lock) , industrial engineering , engineering , work in process , algorithm , philosophy , operations management , computer security , epistemology , operating system
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.