Review of mathematical programming models for energy-based industrial symbiosis networks

La Verne Ramir D.T. Certeza, Aloisius Rabata Purnama, Aniq Ahsan, Jonathan S.C. Low and Wen F. Lu

Renewable and Sustainable Energy Reviews, 2025, vol. 212, issue C

Abstract: Formation of energy-based industrial symbiosis networks (EISNs) is a measure by which industries can address their high energy consumption. EISNs are often designed through mathematical programming (MP) because this method can represent the integration of numerous entities in a compact model while allowing tradeoff analysis of various EISN design objectives. In view thereof, this study presents a systematic review of MP models for EISN optimization. It addresses the research gap on the lack of studies which review the use of MP for optimizing EISNs involving waste heat as the shared resource. The models were analyzed based on five features: the typology of objective functions, the integrated entities in the EISN, the waste heat use options, the effects of considering distance between entities, and the method for modelling parameter uncertainty. This study has uncovered several gaps in EISN modelling. First, there is no consensus about the most relevant environmental and social impacts to include in EISN optimization. Second, novel approaches to simplify nonconvex models are scarce, thereby hindering the incorporation of more pertinent entities into the models due to the concomitant increase in solution time. Third, models analyzing the tradeoff among the various waste heat utilization pathways are limited. Fourth, most models do not include the implications of considering the physical layout of integrated entities in optimizing EISN design. Finally, the best method to incorporate parameter uncertainty in models is still unsettled. By addressing these gaps, more comprehensive MP models can be developed, thereby supporting better-informed decisions about EISN establishment.

Keywords: Industrial symbiosis; Mathematical programming; waste heat recovery; Optimization; Heat integration; Uncertainty modelling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2025.115377

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