Evolutionary optimization of an industrial batch fermentation process

Our research deals with the dynamical optimization of chemical batch processes. An application field, of special interest, is fermentation-based industrial activities. To introduce ourselves into experimental work to verify the results obtained by algorithmic procedures, we selected beer fermentation, and built a pilot plant to reproduce the industrial process. We needed a mathematical model to describe this process, and had to develop a new one. During the beer fermentation, a temperature profile is applied to drive the process so as to obey to certain constraints. There is an optimization problem, to minimize time without quality loss. We adapted Genetic Algorithms to solve the problem, and achieved satisfactory results. The paper describes our experimental framework, the new model, and the application of Genetic Algorithms to optimize the process.