An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery

The sugarcane crushing stage is one of the most important technologies being developed at the moment. In this paper, the control of the multiple-stage evaporation system was addressed, as it is a crucial stage in the first- and second-generation ethanol production from sugarcane. A neural network model was proposed based on a dynamic phenomenological model developed in EMSO (Environment for Modeling, Simulation and Optimization). The phenomenological model was used to build a neural network prediction model for an MPC (Model Predictive Control) scheme using a DMC (Dynamic Matrix Control) algorithm. Simulations were carried out to evaluate the performance for tracking the set-point. Also, disturbance rejection tests were performed, considering different step disturbances. The analysis demonstrated that the MPC scheme performed well in the tests and showed superiority when compared to classical PID controllers.