Multi-objective resource integration for sustainable industrial clusters

Abstract

With the current global climate change and resource depletion concerns, industrial clusters are challenged to focus on implementing sustainability designs and policies. Sustainable designs mainly consider the economic and environmental impact of the cluster. A trade-off normally exists between profit and reducing environmental impact. Therefore, there is a need for optimization techniques that consider these factors simultaneously to achieve sustainable designs. Multi-Objective Optimization (MOO) allows the consideration of multiple objectives to generate Pareto optimal solutions. This work optimizes resource integration (both material and energy) networks for different sustainability objectives to develop sustainable cluster designs using the augmented ε-constraint method. The method was applied to a cluster that uses green technologies and renewable energy for maximum economic return, minimum CO2 and water footprint. The approach generates a 3D Pareto optimal surface, where each point corresponds to a unique resource integration network. The case study optimized a carbon converting cluster that generates a profit up to $8.5–17 MM/y at electricity prices of $0.03 and 0.02/kWh, respectively, by producing methanol, ammonia, and urea.