NEURAL NETWORK BASED BIOMASS AND GROWTH RATE ESTIMATION AIMED TO CONTROL OF A CHEMOSTAT MICROBIAL CULTIVATION

摘要

Two approaches for neural network estimation of biomass concentration X and the specific growth rate μ, aimed to control of chemostat microbial cultivation, are proposed. Continuous growth of a strain Saccharomyces cerevisiae on glucose limited medium is considered as an example in order to evaluate the capability of the designed estimators to work independently, as well as in the framework of linearizing adaptive control systems of biomass and substrate concentration. The first approach assumes that two different estimators NNX and NNμ are designed for biomass and growth rate, respectively. The second one is based on the assumption that one estimator, NNXμ, is designed for both, biomass and growth rate. In both cases the substrate concentration is assumed to be online measurable and the dilution rate, the main control variable, to be known. In the process of the estimator design, the necessity of substrate concentration past values incorporation is studied. The influence of the estimation error on the control performance is also investigated. Conclusions with respect to the practical applicability of the proposed estimators are made.