(624f) Towards Sustainable Resource Integration Networks in Industrial Clusters

Industrial clusters have been pursuing designs and policies that fulfil recent sustainability goals. These policies encourage the implication of improved systems for waste reduction, thus decrease the environmental impact with minimum cost requirement. This simultaneous consideration of the two design aspects is challenging since, mostly, there exist trade-offs between economic and environmental performance. Therefore, there has been a focus on developing methods for multi-objective optimization (MOO) combined with material and energy integration approaches that tackle this issue. Two of the most prominent MOO models are the weighted average method, which converts multi-objective problems into single objective ones, as well as the e-constraint method. However, integration MOO models developed previously rely focus on specific material resources. They only allowed a certain number of resources to be integrated. This work applies holistic resource integration in industrial cluster (material and energy) under multiple objectives. The approach allows the integration of all type of resources, and uses both the augmented e-constraint (an enhanced e-constraint model) and the weighted average method to optimize an industrial cluster. The MOO models allow exploring the trade-offs between economic and environmental performance and represent them in Pareto Optimal sets. A case study of a carbon-neutral industrial cluster is explored and optimized using the MOO model, and an optimal set of solutions is obtained. This method is suitable to generate multiple scenarios to assess policymakers in decision making.