Optimization of Cyclic Reactive Distillation via a Vapor and Liquid Flow Period Model Coupled with a Stochastic Algorithm

As an effective strategy for process intensification, cyclic reactive distillation offers notable advantages over conventional reactive distillation columns: it can simultaneously enhance processing capacity, reduce energy consumption, and boost reactant conversion. Additionally, this technology minimizes liquid cross-flow resistance on trays packed with solid catalysts while enabling controllable residence time of the reaction mixture. However, the mechanistic model of cyclic reactive distillation is represented by a set of typical nonlinear differential-algebraic equations (DAEs), and its associated optimization problem is inherently a functional extremum problem. This type of problem poses significant challenges for direct solution and has remained an unaddressed gap in previous research. To overcome this obstacle, a genetic algorithm was coupled with the vapor and liquid flow period (VFP/LFP) model, enabling the simultaneous determination of both structural and operating parameters for cyclic reactive distillation systems. The synthesis processes of dimethyl ether and carbonate esters were selected as case studies to validate the performance of the proposed iterative solution strategy and optimization approach. Results indicate that, in comparison to conventional reactive distillation, cyclic reactive distillation achieves a substantial reduction in key economic indicators.