Model based optimization of in silico fed batch ethanol production process using genetic algorithm
In the present work a Non Linear Model Predictive Control (NLMPC) procedure for in silico ethanol production maximization will be presented for a fed batch process, configuration which is widespread found in the industrial scope, using an Genetic Algorithm (GA) routine. The dynamic optimization problem was subdivided in a series of sequential intervals for optimization, and the influence of the number of intervals was also studied in terms of the final yield and productivity obtained in the NLMPC. In order to ensure for smooth feed profiles, a exponential bioreactor feeding profile was evaluated. The results obtained in this work are coherent to the other presented in the literature, and superior to some published values. The utilization of the in house GA routine for the NLMPC control has proven to be efficient in terms of maximization of the final process productivity.
2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)