An integrated multi-criteria decision making framework for industrial excess heat recovery and utilization

Industrial excess heat recovery offers substantial potential for energy efficiency and a circular economy; however, selecting optimal utilization strategies presents a complex decision problem involving diverse and often conflicting criteria. This paper presents a novel integrated decision-making framework that addresses this challenge by combining a Decision Tree for rapid feasibility screening with a Multi-Criteria Decision-Making (MCDM) model. This model leverages the Analytic Hierarchy Process (AHP) to weight stakeholder priorities and employs a multi-method approach for ranking potential solutions. Unlike traditional models, this framework ensures both computational efficiency and adaptability to diverse industrial contexts, integrating the merits of heuristic and analytical methods. Applied to six diverse case studies from a real-world project database, our framework demonstrates its efficacy in identifying promising heat recovery pathways while highlighting the critical influence of criteria weighting on final recommendations. A sensitivity analysis further confirmed the robustness of our approach. Rigorous statistical analysis also reveals no significant differences between the MCDM methods, showing consistency of the results. This integrated framework contributes a practical and robust tool for sustainable excess heat management, informing more effective decision-making and offering a pathway for improved industrial energy efficiency.