Fuzzy modeling of enzymatic penicillin-G conversion

Mathematical modeling is essential for the design, analysis and optimizatio n of biotechnological processes, as well as for the development of their co ntrol systems. Because of the complexity and uncertainty associated with th ese processes, rigorous mathematical modeling often becomes a major bottlen eck. This article presents a modeling approach that is based on a combinati on of first-principle modeling of known relationships, with fuzzy modeling of the unknown parts of a process. A Penicillin-G conversion process is use d as an application example to demonstrate the methodology. A linguistic fu zzy model, which represents the kinetic term of the conversion, is develope d from experimental data by means of fuzzy clustering. The model is then in corporated in macroscopic balance equations describing the overall conversi on process. It is shown that the approach leads to an accurate prediction m odel, and, at the same time, allows for a qualitative interpretation of the unknown relationships learnt from data. The extracted knowledge base was p resented to experts, who confirmed the overall correctness of the rules, an d also the relevance of the membership functions for the particular process under study. (C) 1999 Elsevier Science Ltd. All rights reserved.