Metaheuristic-Based Design and Optimization of Chemical Reactors: A Comprehensive Survey

Abstract

This paper presents a comprehensive review of metaheuristic optimization in chemical process engineering over the period 2000-2025. We systematically analyze the literature on metaheuristic algorithms and their applications to process design, reactor optimization, distillation, separation, contro. The review covers approximately 179 papers, providing a structured taxonomy of algorithms, applications, and evaluation methodologies. We identify key trends including the shift toward hybrid approaches, integration of machine learning, and growing emphasis on explainability. The survey reveals that nonlinear dynamics, safety constraints, multi-objectivity, real-time r remain significant open problems. We provide detailed analysis of evaluation protocols, benchmark suites, and statistical methodologies. Future research directions include hybrid algorithm design, quantum-inspired methods, and standardized benchmarking frameworks.