Optimization and Comparative Analysis of Industrial Polymerization Reactors Using Physics-Inspired Metaheuristics

Abstract A multi-objective optimization (MOO) technique to produce a low-density polyethylene (LDPE) utilizing a tubular reactor is applied to address these two problems: increasing productivity and reducing energy cost (as the first optimization problem, Problem 1) and increasing conversion and reducing energy cost (as the second optimization problem, Problem 2). The ASPEN Plus software was utilized for the model-based optimization by executing the MOO algorithm using the tubular reactor model. Here, the physics-inspired metaheuristic optimization algorithms, namely, Multi-Objective Atomic Orbital Search (MOAOS), Multi-Objective Material Generation Algorithm (MOMGA), and Multi-Objective Thermal Exchange Optimization (MOTEO), are used to address mentioned optimization problems. The performance matrices, including hypervolume, pure diversity, and distance, are used to decide on the best MOO method. An inequality constraint was introduced on the temperature of the reactor to prevent run-away. According to the simulation results, MOMGA is the optimal MOO strategy for Problem 1, while MOAOS is the optimal strategy for Problem 2. The reason is that the solution set found represents the most accurate, diversified, and acceptable distribution points alongside the Pareto front (PF) in terms of homogeneity. The results show that the lowest energy cost, highest productivity, and highest revenue value are 0.670 million RM/year, 5279 million RM/year, and 0.3074 million RM/year, respectively. According to the decision variable plots, the initiator in the reactor’s end zone has a significant influence on the optimal solution.