Optimization and Dynamic Adjustment of Tandem Columns for Separating an Ethylbenzene–Styrene Mixture Using a Multi-Objective Particle Swarm Algorithm

This study focuses on optimizing two tandem columns to separate ethylbenzene and styrene. A steady-state model is developed to minimize total energy consumption (TEC) and total annualized cost (TAC) by optimizing the reflux flow rates. An integrated dynamic model is created using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. This model is designed to account for transitions in operating conditions and to identify optimal dynamic strategies for adjusting operations to maintain optimal performance. The optimization considers factors such as fluctuation amplitude, the number of fluctuations, and fluctuation duration. The aim is to reduce fluctuation amplitudes while ensuring higher energy efficiency and stable operation. The results reveal that the optimal reflux flow rates are 41,152.2 kg/h and 1012.7 kg/h, leading to reductions in TEC and TAC by 16.7% and 17.4%, respectively. Compared with the industry standard level, the energy consumption has decreased by 11.25%. Against the backdrop of increasingly strict global carbon emission control, the market competitiveness of ethylbenzene/styrene production has been significantly enhanced. The variable-step adjustment method requires less time to reach a stable state, while the equal-step fluctuation method provides more stability. The Pareto solution set derived from the two optimization techniques can be used to select the most suitable adjustment strategy, ensuring a fast and smooth transition.