Data-Driven Modeling and Control of Semicontinuous Distillation Process

A semicontinuous distillation process is effectively used in the separation of a multi-component mixture with low to medium production rates. This work focuses on building a data-driven model predictive control (MPC) framework to optimize the performance of a semicontinuous process by reducing total annualized cost (TAC) per tonne of feed processed while meeting the specified product quality. A data-driven modeling technique is considered in this work because of the unavailability of a highly complex and accurate first-principle model. An Aspen Plus Dynamics simulation is used as a test bed to collect the data from the process. A multi-model framework developed by modifying the traditional subspace algorithm is adapted in the shrinking horizon MPC (SHMPC) scheme to minimize TAC per tonne of feed processed. Visual Basic for Application (VBA) is used as a third tool to communicate the inputs from MPC developed in MATLAB to the process in Aspen Plus Dynamics. The simulation results illustrate that the MPC reduced the TAC/tonne of feed by 11.4% compared to the existing PI control configuration.