AI-Based Geometry Recognition for Evaluating the Feasibility of Intensified Reaction and Separation Systems

Abstract

Reactive distillation (RD) provides notable advantages over conventional processes, regarding reduced energy requirements and CO2 emissions. However, as the benefits of RD may not be universally applicable, a comprehensive feasibility assessment is necessary. This study introduced an automated feasibility evaluation procedure for an RD column using an AI-based region recognition approach, reducing the reliance on expert knowledge and heuristics in graphical methods. Through k-means clustering-based image segmentation, topological information on the reaction and separation reachable region was extracted from ternary diagram landscapes. Subsequently, the extracted information was integrated into tray-by-tray calculations to automate the evaluation. This geometric calculation procedure was applied to assess the feasibility of RD columns with different types of reactions. The feasibility results were obtained within seconds, demonstrating the efficiency of the proposed approach. Furthermore, case studies validated the feasibility of the evaluation results for three practical examples using rigorous simulations, confirming its reliability and applicability.