Multilevel optimization framework to support self-sustainability of industrial processes for energy/material recovery using circular integration concept

Abstract

Climate change, resource scarcity, waste reduction, and the lack of sustainability in process industries are vital concerns that civilization confronts. The process integration methodology may aid such sustainability. In this study, a novel chemical exergy resource recovery network is proposed for optimal energy and waste recovery in high-salinity-gradient chemical industries using a pressure-retarded osmosis membrane while indicating a self-sustainability and allocation in complex industrial networks. The mathematical programming paradigm is expressed as a multilevel optimization model to introduce novel ideas for explicitly modeling the trade-offs between waste and energy flows in circular integration while demonstrating industrial network’s environmental impact assessment. This problem is decomposed into two subproblems (SPs) that must be addressed sequentially. The first SP is designed to decrease the total cost of the network while reducing external resource use. The second SP formulates a mixed-integer nonlinear programming model with the objective of minimizing the environmental effects and exergy consumption rate of the network. A case study of a naphthalene-methaforming plant demonstrates the efficacy of the proposed methodology. The results showed that using a tri-level optimization technique, a considerable improvement in flowrate, total annualized cost, and energy recovery is obtained while limiting the network's environmental impact. The operating phase accounts for approximately 75% of the global warming potential output. The proposed tri-level approach based on Benders’ decomposition approach reduced the overall cost of the network by 19.29%, and 47.42 MW net power is recovered in the case study. In addition, the circular exergy use rate and environmental factor were reduced from 400 to 0.037 MW and from 2.569 to 1.481 kgCO2/year, respectively, using the tri-level decomposition approach.