Develops a linear optimization model to improve water supply efficiency in industrial facilities, suggesting integrated planning implications.
This study presents a linear optimization framework for the design and operation of multi-source industrial water supply systems, applied to a large-scale iron smelting facility in Shiraz, Iran. The proposed model determines the optimal allocation of available water resources and the required capacities of storage, pretreatment, and treatment units while incorporating operational and energy-related constraints. Owing to its linear structure, the framework is computationally efficient and suitable for large-scale industrial applications. Multiple supply scenarios were developed to evaluate alternative combinations of conventional and unconventional water sources, including drainage water, wastewater, groundwater, rainwater, and internal industrial streams. The results indicate that water supply performance is strongly influenced by both resource availability and water quality. Strategies heavily dependent on treated wastewater enhance supply reliability but lead to higher electricity demand due to intensified treatment requirements. In contrast, diversified portfolios prioritizing drainage water and supplemented by limited groundwater and rainwater improve operational stability while reducing reliance on energy-intensive processes. The findings highlight the critical interaction between water quality and energy demand in industrial supply systems and emphasize the importance of integrated water–energy planning under resource-constrained conditions. The proposed framework provides a practical and transferable decision-support tool for industrial facilities seeking to strengthen water security, improve operational resilience, and advance sustainability, particularly in arid and semi-arid regions. • Developed a novel linear optimization model for industrial water supply systems, integrating storage, pretreatment, and treatment unit capacities with multi-source water allocation. • Utilized linear programming to minimize water and energy costs, incorporating real data from Pasargad Steel Plant and evaluating eight resource-mix scenarios. • Scenario Two (drainage water, well water, rainwater) achieved the lowest energy use (∼14,700 kWh/day), while wastewater-only scenarios demanded up to 20,750 kWh/day.
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Amirizanjirani et al. (2026) studied this question.
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