Modeling the effect of temperature on ethanol fermentation using Saccharomyces cerevisiae CSI-1 by genetic programming

Abstract

In this study, genetic programming (GP) was the systematic method used to identify the structure and parameters of a mathematical model for ethanol fermentation. The mathematical model simulated the effect of temperature on the kinetics of batch ethanol fermentation and helped to find out the optimum temperature for better performance of the process. Saccharomyces cerevisiae CSI-1 growing in cane molasses-based media was the microorganism used in all the experiments. Achieving the model's precision in describing the experimental observations involved the estimation of its structure (non-linear principally) and its constant parameters. The model found describes the fermentation kinetics and showed a fair prediction for dry cell weight (DCW), colony forming units/mL (CFU/mL), residual sucrose (RS), residual glucose (RG), and ethanol concentration (E). The model was used to optimize the operating conditions of the process. The predictions from the model in terms of mean square error (MSE) and sum squared error (SSE) fitted the experimental data well with fitness values in a range of 𝑅𝑅2 ≥0.92.