Nonlinear predictive control of an evaporator for bioethanol production

Production of biofuel has a positive environmental and economic impact; therefore, the interest for accurate modeling and more advanced control techniques has grown considerably over the last years. The reason is that it allows optimizing the productivity while the energy consumption is minimized. This paper presents the modeling, simulation and nonlinear control of a double-effect evaporation process to obtain bioethanol from sugarcane juice. This is achieved by controlling the juice concentration at the output of the last evaporator stage. Nonlinear Model Predictive Control (NMPC) has been successfully implemented, following the Extended Prediction Self-Adaptive Control (EPSAC) approach. This algorithm has been chosen among the different methods as it requires less computational load. The effectiveness of the proposed EPSAC controller is presented and compared against PI and Generalized Predictive Control (GPC), through simulation.Production of biofuel has a positive environmental and economic impact; therefore, the interest for accurate modeling and more advanced control techniques has grown considerably over the last years. The reason is that it allows optimizing the productivity while the energy consumption is minimized. This paper presents the modeling, simulation and nonlinear control of a double-effect evaporation process to obtain bioethanol from sugarcane juice. This is achieved by controlling the juice concentration at the output of the last evaporator stage. Nonlinear Model Predictive Control (NMPC) has been successfully implemented, following the Extended Prediction Self-Adaptive Control (EPSAC) approach. This algorithm has been chosen among the different methods as it requires less computational load. The effectiveness of the proposed EPSAC controller is presented and compared against PI and Generalized Predictive Control (GPC), through simulation.P