Use of Recurrent Neural Networks for Bioprocess Identification in On-line Optimization by Micro-Genetic Algorithms

The use of recurrent neural networks in bioprocess identification and optimization is investigated. A recurrent neural network is trained on a set of fermentation data, and there-after used as a nonlinear process model to estimate nonmeasurable process states at different conditions. With the bioprocess state variable information available, an optimization technique can be used to generate optimum controls settings to improve the process performance. This paper explores the use of Micro-Genetic Algorithms as a technique for bioreactor optimization. Simulation results will be discussed based in the fermentative ethanol production by the anaerobic bacteria Zymomonas mobilis.