Multi-Objective Optimization of Reactive Distillation Column for Green Solvent Production Using Response Surface

Abstract

The growing demand for environmentally sustainable chemical processes has increased interest in green solvent production through energy-efficient and integrated reaction-separation technologies. This study presents a multi-objective optimization approach for a reactive distillation column used in green solvent production employing response surface methodology (RSM). The proposed framework integrates reaction kinetics, distillation dynamics, process simulation, and statistical optimization techniques to improve solvent yield, product purity, and energy efficiency under varying operating conditions. A comprehensive analysis was conducted to evaluate the influence of critical process variables, including reflux ratio, feed flow rate, catalyst loading, reaction temperature, and column stage configuration, on the performance of the reactive distillation system. Response surface models were developed to establish mathematical relationships between operational parameters and key performance indicators such as conversion efficiency, solvent selectivity, and energy consumption. Multi-objective optimization techniques were employed to identify optimal operating conditions that simultaneously maximize product yield and minimize utility requirements. Experimental and simulation results demonstrated that optimized reactive distillation operation significantly improves green solvent production efficiency while reducing process energy demand and operational costs compared to conventional sequential reaction and separation systems. The integrated process configuration also enhanced mass transfer efficiency and process intensification through simultaneous reaction and separation within a single unit. Comparative analysis revealed improved sustainability performance, reduced waste generation, and enhanced process economics under optimized conditions. Furthermore, the application of response surface methodology enabled efficient process modeling, sensitivity analysis, and operational decision-making for complex chemical systems.