Optimization of Energy Efficiency in Mechanical Engineering Systems: A Comprehensive Multi-Scale Review and Engineering Framework

Abstract

Optimization of energy efficiency in mechanical engineering systems has progressed from isolated component tuning to a multi-scale engineering problem that links equipment design, operating-condition selection, and computational decision-making. This review synthesizes literature across pumps, compressors, turbines, heat exchangers, boilers, and motors, emphasizing practical interventions such as variable speed drives, advanced materials, surface modification, and improved lubrication. It further evaluates classical mathematical optimization methods, including Linear Programming, Nonlinear Programming, and Dynamic Programming, alongside metaheuristic strategies such as Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, and Ant Colony Optimization. Recent review literature shows that optimization is increasingly treated as an integrated process-systems problem, with AI-assisted and hybrid modeling approaches expanding the scope of feasible solutions. 1 Representative application studies demonstrate that nonlinear surrogate-based optimization can successfully minimize cost or emissions in industrial energy systems, that thermal–mechanical compression can outperform single-cycle alternatives in high-temperature heat upgrading, and that modified PSO variants can improve solution quality in constrained mechanical design problems. 254 Recent multiscale optimization work also shows that fast-converging PSO frameworks can improve both energy consumption and output efficiency in complex engineering systems. 6 The evidence indicates that optimization gains depend on problem structure, constraint severity, and implementation realism rather than on algorithmic novelty alone. The main lacunae are fragmentation between physical and computational interventions, limited benchmarking across domains, weak reporting of deployment feasibility, and insufficient integration of robustness and lifecycle metrics. To address these gaps, the review proposes a unified engineering framework for selecting optimization strategies according to system complexity, data availability, objective structure, and industrial applicability.