The research on scheduling and heat integration of batch process plays an important role in reducing energy consumption

improving production efficiency and enhancing the competitiveness of industries. The complexity and difficulty of the model solving are increased due to the comprehensive consideration of both scheduling and heat integration. In this paper

the mixed integer nonlinear programming (MINLP) mathematical model of multi-product plant heat integration optimization with the goal of energy-saving annual profit (EAP) is established. The simultaneous optimization and sequential optimization are carried out respectively by bi-level programming (BP) based on the genetic algorithm (GA)

and the calculation results are compared. EAP better captures the trade-off relationship between scheduling schemes

energy-saving profits

and equipment costs. The bi-level programming approach based on GA categorizes variables into integer and real types

enabling structural optimization and parameter optimization of the heat exchanger network. This

in turn

enhances solution efficiency and overcomes the limitations of conventional optimization algorithms in terms of solution speed and quality. Two examples show that the EAP of indirect heat integration considering the storage tank are 21% and 2% higher than that of the direct heat integration

and EAP of the simultaneous optimization are 26% and 6% higher than that of the sequential optimization. The example demonstrates that the model and algorithm are applicable to batch multi-product plants

such as those in the chemical

pharmaceutical

and food industries

and possess strong practicality and innovation.