Retrofit of Crude Pre-Heat Train using MINLP Model under Non-Isothermal Mixing Assumption

To enhance the energy recovery through heat integration, heat exchanger network synthesis (HENS) has been introduced for industrial processes in the last several decades. The systematic identification of retrofit designs of the existing complex network in industrial processes is case-dependent problem, especially crude oil atmospheric distillation systems. Therefore, retrofit design is one of the most challenging heat exchanger network (HEN) designs where no standard formulation deals with the fixed cost of topology changes. The revision of an existing HEN is more complex than the grassroots design. This work is to modify a mixed-integer nonlinear programming (MINLP) stage-wise model using commercial optimization software; GAMS, for retrofitting HEN where the main objective is to maximize profit calculated by energy saving subtract total investment cost of additional exchanger area, new exchanger, stream splitting and repiping. The proposed model overcomes the area trade-off restriction caused by the assumption of isothermal mixing and allows several matches located on a branch of each process stream. Dealing with the MINLP case, the initialization strategy is developed to find feasible starting point for the optimization problem resulting in generating more profitable HEN design compared to the published one from the literature.