Bioethanol production optimization by direct numerical methods and evolutionary algorithms

Abstract

This paper develops a dynamic optimization methodology based on direct numerical methods, for the bioethanol fed-batch production from glucose and fructose as a substrate. The mathematical model that governs the process consists of six differential equations and is highly nonlinear. The proposed strategy uses the Fourier trigonometric basis and normalized orthogonal polynomials for substrate feeding rate parameterization. Then, evolutionary algorithms and gradient methods are combined to search parameters that generate the best control action. This parameterization methodology requires a minimum number of parameters to optimize. Also, the continuous and differentiable nature of the optimal profile enables its direct implementation in the physical process, eliminating the necessity for filtering or smoothing it. In addition, they are ideal for bioprocesses, in which it is preferable to avoid abrupt changes in the operating modes of the process to promote cell growth. As a result, using only 3 parameters, a 3.5% increase in ethanol production was achieved, while the reference uses at least 10 parameters and provides a stepped feed profile. The simulations have yielded promising results, making this proposal an alternative with excellent potential for process optimization.