Beer is one of the most popular alcoholic beverages globally, leading to continuous efforts to enhance its production methods. Raw materials and the production process are crucial in the brewing industry, with fermentation being a vital stage that significantly impacts beer quality. The aim of this study is to optimize the beer fermentation process by maximizing the ethanol concentration while minimizing species that adversely affect the organoleptic properties of beer. A novel optimization approach has been developed to derive an optimal, smooth, and continuous temperature profile that can be directly applied in real-world processes. This method integrates Fourier series and orthogonal polynomials for control action parameterization, in combination with evolutionary algorithms for parameter optimization. A key advantage of this methodology lies in its ability to handle a reduced parameter set efficiently, resulting in temperature profiles that are continuous and differentiable. This feature eliminates the need for post-smoothing and is particularly advantageous in biotechnological applications, where abrupt changes in temperature could negatively affect the viability of microorganisms. The optimized profiles not only enhance fermentation efficiency, but also improve the ethanol yield and reduce undesirable flavor compounds, providing a substantial improvement over current industrial practices. These advancements present significant potential for improving both the quality and consistency of beer production.

This paper presents an improved methodology for optimizing the fed-batch fermentation process of xylitol production, aiming to maximize the final concentration in a bioreactor co-fed with xylose and glucose. Xylitol is a valuable sugar alcohol widely used in the food and pharmaceutical industries, and its microbial production requires precise control over substrate feeding strategies. The proposed technique employs Legendre polynomials to parameterize two control actions (the feeding rates of glucose and xylose), and it uses a hybrid optimization algorithm combining Monte Carlo sampling with genetic algorithms for coefficient selection. Unlike traditional optimization approaches based on piecewise parameterization, which produce discontinuous control profiles and require post-processing, this method generates smooth profiles directly applicable to real systems. Additionally, it significantly reduces mathematical complexity compared to strategies that combine Fourier series with orthonormal polynomials while maintaining similar optimization results. The methodology achieves good results in xylitol production using only eight parameters, compared to at least twenty in other approaches. This dimensionality reduction improves the robustness of the optimization by decreasing the likelihood of convergence to local optima while also reducing the computational cost and enhancing feasibility for implementation. The results highlight the potential of this strategy as a practical and efficient tool for optimizing nonlinear multivariable bioprocesses.