A multi-objective optimization model for wastewater treatment in eco-industrial park design with employment considerations

• المؤلفون الرئيسيون: Chua, Ralph Anderson P., Magdaong, Cherry Pauline B., Mañalac, Ricardo Emmanuelle D., Puente, Ylesa Erliria B., Torres, Gian Carlo G.
• التنسيق: text
• اللغة: English
• منشور في: Animo Repository 2023
• الموضوعات: Industrial districts—Design and construction; Sewage—Purification; Operations Research, Systems Engineering and Industrial Engineering
• الوصول للمادة أونلاين: https://animorepository.dlsu.edu.ph/etdb_induseng/16; https://animorepository.dlsu.edu.ph/context/etdb_induseng/article/1026/viewcontent/A_Multi_Objective_Optimization_Model_for_Wastewater_Treatment_in_Redacted.pdf
• المؤسسة: De La Salle University

With the continuously increasing consumption of natural resources, more often than not, conservation and sustainability is disregarded. Eco-Industrial Parks (EIPs) in the field of Industrial Symbiosis and Ecology have been studied and implemented in many countries to promote sustainable development, but are yet to consider the social aspect in its optimization. This paper aims to incorporate the creation of job opportunities into a resilient and stochastic planned EIP model utilizing a direct integration scheme from the perspective of an EIP manager. To achieve these, a multi-objective optimization model for EIPs was made that accounted for equal weights on economic costs, environmental impact, resiliency, and employment considerations over a single period, with constraints added to the plants’ utilization and bounds set to each objective taken from individual optimization. Since the goal of the multi-objective model is to minimize deviation from the individual optimal values, the base run prioritizes less deviation on the resiliency and employment considerations since the economic and environmental objectives have a more comprehensive range of possible values; changes in the latter would create smaller impact compared to changing the former. Supported through testing different scenarios, it was found that the model prioritized reaching the optimal value for job opportunities while accounting for utilization requirements, and adjusting different objectives based on the scenarios. If scenarios caused plants to shut down, it would prioritize the economic and environmental objectives. Keeping more plants open kept resiliency low at the expense of environmental impact and economic costs to establish interplant connections.