Modeling of a Fermentation Process with Multiple Support Vector Machines

A fermentation process suffers from a lack of good mathematical models, because of its complexity, since it is highly non-linear, has time varying parameters. It also has many important variables such as concentrations that cannot be measured on-line, which makes this set of variables of limited use for control purposes. However, these variables can be on-line estimated using soft sensing. This paper describes the identification of fermentation process with multiple least square support vector machine models. And the parameters in least square support vector machine were optimized by Genetic Arithmetic. The soft sensing model was trained on data coming form Nosiheptide fermentation process, and evaluated the production. The estimated results obtained were in good agreement with those found with the off-line laboratory analysis. It is also shown that software based state estimation was a powerful technique in biochemical products.