An integrated algorithm framework for multi-objective optimization of heat exchanger networks considering temperature-dependent heat capacity

Heat exchanger networks (HEN) synthesis faces challenges in addressing non-isothermal mixing and non-constant thermal properties of streams, giving rise to computationally-hard mixed integer nonlinear programming problems. An integrated algorithm framework including the simple additive weighting method, the two-level hybrid algorithm, and the ε-constraint method is proposed for multi-objective optimization (MOO) of HEN with variable heat capacity. The proposed framework that incorporates a new stream matching set and a new heat exchanger vector to improve optimization and generate feasible solutions, is capable of efficiently handling the trade-off between total annualized costs (TAC) and environmental impacts (EI). Two case studies are conducted to verify the effectiveness and superiority of the proposed framework. Optimization results demonstrate that the proposed framework can find competitive solutions with lower TACs and achieve the trade-off solutions whose EIs are considerably reduced by 16.4% and 7.89% compared to the lowest TAC solutions reported in literature.