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Reactive distillation is a process that combines chemical reaction and separation in a\udsingle piece of equipment (distillation column). The process has a lot of benefits especially for those\udreactions occurring at conditions suitable for the distillation of the process components, and these\udresult in significant economic advantages. However, owing to the complexities resulting from the\udintegration of reaction and separation, its control is still a challenge to process engineers because it\udrequires a control method that is robust enough to handle its complexities. Therefore, in this work,\udmodel predictive control (MPC) has been applied to a reactive distillation process used for\uddeveloping a renewable energy known as biodiesel. The control algorithm of the MPC was\udformulated with the aid of MPC toolbox of MATLAB/Simulink in which the closed-loop models of\udthe process were developed and simulated. The analysis of the results obtained from the simulations\udcarried out for the optimization of the tuning parameters revealed that, among the tuning parameters\udconsidered, integral absolute error of the control system was less affected by the control horizon\udbecause its p-value was greater than 0.05 based on 95% confidence level. Furthermore, the\udsimulation of the closed-loop system of the process using model predictive control tuned with\udcontrol horizon of 11, prediction horizon of 18, weight on manipulated variable rate of 0.05 and\udweight on output variable of 2.17, which were the optimum parameters obtained using Excel\udSolver, showed that the system was well handled by the controller under servo control because it\udwas able to get settled at desired mole fractions within 60 min. However, the settling time recorded\udin the case of regulatory control system of the process with the same controller was found not to be\udencouraging. Therefore, it is recommended that further work should be carried out on this subject\udmatter in an attempt to obtain tuning parameters that will make the settling time of the closed-loop\udsystem of the process under regulatory control simulation very reasonable.\ud\udNomenclature\udANOVA Analysis of variance\udDMC Dynamic matrix control\udGp Transfer function model of the process\udGPC Generalized predictive control\udIAE Integral absolute error\udISE Integral squared error\udKp Static gain of the transfer function model of the process\udMAC Model algorithmic control\udMPC Model predictive control(ler)\udPI Proportional-integral\udPID Proportional-integral-derivative\udQreb, Q Reboiler heat duty (kJ/s)\udR Reflux ratio\udwurate Weight on manipulated variable rate\udwy Weight on output variable\udxbiod Bottom biodiesel mole fraction\udΤdp Delay time of the transfer function model of the process (min)\udΤp Time constant of the transfer function model of the process (min

Application of Model Predictive Control to Renewable Energy Development via Reactive Distillation Process