Real time optimization of distillation columns using data-driven models

This work presents a data-driven model of a two-product distillation tower that is suitable for real-time optimization (RTO) of distillation columns. The proposed model accurately predicts product mass fractions using operating variables and tray temperatures by integrating a linear data-driven inferential composition model (based on two tray temperatures in each section of the tower, reflux/distillate ratio, and reboiler duty/bottoms flow ratio) with a neural network (NN) model that predicts tray temperatures from the value of the manipulated variables. RTO is carried out via an iterative procedure where the sensitivity matrix is initially calculated from the model and updated using plant measurements from subsequent values. A butane splitter column is presented as a case study. Our results show that the implementation of the data-driven model-based RTO results in a solution that is within 0.1% of the optimization solution based on the rigorous tray-to-tray distillation simulation.