Evaluation of optimization techniques for parameter estimation: Application to ethanol fermentation considering the effect of temperature

Abstract

Optimization techniques are evaluated to estimate the kinetic model parameters of batch fermentation process for ethanol production using Saccharomyces cerevisiae. Batch experimental observations at five temperatures (28, 31, 34, 37 and 40 °C) are used to formulate the parameter estimation problem. The potential of Quasi-Newton (QN) and Real-Coded Genetic Algorithm (RGA) to solve the estimation problem is considered to find out the optimal solution. Subsequently, the optimized parameters ( μ max, X max, P max, Y x and Y p x ) were characterized by correlation functions assuming temperature dependence. The kinetic models optimized by QN and RGA describe satisfactorily the batch fermentation process as demonstrated by the experimental results.