Intelligent Optimization of Industrial Heat Exchanger Networks

Abstract

Industrial heat exchanger networks recover thermal energy between process streams and reduce dependence on external heating and cooling utilities. Their optimization is challenging because discrete equipment decisions interact with nonlinear heat-transfer relationships, operating variability, fouling, hydraulic constraints, and competing economic and environmental objectives. This article presents a narrative review and conceptual framework for intelligent optimization of industrial heat exchanger networks. Nine dimensions are examined: process-data preparation, pinch-based energy targeting, mathematical network formulation, evolutionary search, surrogate-assisted optimization, multiobjective decision-making, uncertainty and fouling management, retrofit feasibility, and implementation with operational verification. The proposed framework combines thermodynamic targeting with mixed-integer models, hybrid search algorithms, and independent engineering validation. Intelligence is understood as the coordinated use of physical knowledge, optimization, and validated data-driven models, rather than a substitute for conservation laws. An analytical graph illustrates the competing effects of heat recovery on utility expenditure and annualized recovery cost using explicitly assumed values. The graph is a pedagogical calculation and provides no measured industrial savings or algorithm-performance evidence. The synthesis emphasizes that feasible stream matching, realistic exchanger sizing, controllability, maintainability, and consistent economic boundaries determine whether a mathematically attractive solution can deliver practical value. Reliable implementation requires transparent assumptions, repeated optimization trials, uncertainty analysis, and verification against detailed equipment models and operating constraints.