A surrogate-based optimization framework for simultaneous synthesis of chemical process and heat exchanger network

Abstract Heat-integrated process synthesis is fundamental to achieve higher energy efficiency. The well-known sequential-conceptual methods have been widely adopted to solve the synthesis problem in a hierarchical manner. However, the natural hierarchy fails to consider complex interactions between the unit operation and the heat integration. To address this issue, a surrogate-based optimization framework is proposed for simultaneous synthesis of chemical process and heat exchanger network. An artificial neural network (ANN)-based surrogate model, derived from the simulation data generated via rigorous mechanism modelling approach, is established for process units to replace their complex realistic models. With surrogate model formulation incorporated into heat integration, an enhanced transshipment-based mixed integer nonlinear programming model is introduced to synthesize heat exchanger network with variable flowrates and temperatures, aiming at the maximized annual profit. Finally, two example studies are investigated to demonstrate the effectiveness of the proposed framework.