HYBRID AND DATA DRIVEN MODELS OF DISTILLATION TOWERS

Abstract

This thesis presents advancements in the development of hybrid and data-driven models of distillation columns. First, it introduces a hybrid model structure that incorporates a novel multiplicative correction term for inferential monitoring. This model architecture outperforms previous hybrid structures, especially in extrapolation conditions, and can be adapted for different conditions. Second, it presents a methodology for selecting temperature measurement for inferential models. This methodology demonstrates that nonlinear columns can be effectively modeled with linear models requiring two temperature measurements per section (previous works state requiring more) when the measurements are selected systematically. Finally, an iterative Real-Time Optimization (RTO) based on an augmented inferential data-driven model is demonstrated. The accuracy of the model enables estimation of the sensitivity matrix of the plant from the model without the need for additional plant measurements. The proposed RTO framework produces results similar to those achieved by optimizing rigorous tray to tray distillation models.