Superstructure Optimization Based on Hierarchical Accelerated Branch and Bound Algorithm and Its Application in Feedstock Scheduling

As modern petrochemical systems tend towards larger scales, process structures become more complex and highly integrated. The interaction and overall complexity between the operations of each unit are constantly increasing, and the solution process is becoming increasingly complex. The superstructure generated by P-graph theory can reduce the generation of redundant structures, and the accelerated branch and bound algorithm can efficiently explore a vast solution space. However, this algorithm can only solve single-objective optimization problems. This article mainly focuses on the multi-objective optimization problem of the chemical process flow, with reduced production costs and the mitigation of environmental pollution being the main considerations. A novel algorithm framework based on an improved accelerated branch and bound method and incorporating the core idea of Analytic Hierarchy Process (AHP) was proposed, aiming to improve the high computational cost of traditional methods and solve the problem of high modeling costs in multi-objective optimization. The feedstock scheduling of ethylene is used to verify the effectiveness and superiority of the proposed method, with low-cost and carbon emission solutions being easily selected. This article provides a reference for the study of superstructures in chemical production processes. The experimental results show that with a small increase in energy consumption costs, the optimal solution obtained by our algorithm can reduce carbon emissions by 1.27% compared to the original solution, and reduce 2/3 of the modeling workload.