Control of alcoholic fermentation is intensively studied in last decades as it is used in biofuel production. Two advanced control approaches for the yeast alcoholic fermentation running in a continuous-time biochemical reactor are studied in this paper with focus on maximizing product yield and minimizing energy consumption. The neural network predictive control uses a neural network (NN) process model in the optimizing model-based control strategy. A new approach to control of a biochemical reactor for yeast fermentation presented in the paper is a robust model-based predictive control with integral action (RMPC-IA). The RMPC-IA uses a discrete time state-space model for prediction of future outputs of the process with parametric uncertainty. The calculated control inputs are the results of an optimization strategy. The optimization problem to be solved is formulated as a convex optimization problem resolved via linear matrix inequalities. The RMPC-IA outperformed the NN predictive control of alcoholic fermentation as it improved performance indices, preserved the product yield, and ensured energy saving.