Optimization of scheduled cleaning of fouled heat exchanger network under ageing using genetic algorithm

The problem of heat exchanger network (HEN) fouling and ageing has been a costly feature of chemical and petrochemical industries, particularly, the oil refineries. In this paper, a strategy to determine the optimal cleaning schedule of the crude oil refinery's preheat train (PHT) under fouling and different ageing scenarios (i.e., slow and fast ageing) is addressed by a specialized Genetic Algorithm (GA) which uses an array of discrete and positive integers as chromosome representation. Specifically, the effect of key cleaning decision factors (i.e., which, when and how to clean), dictated by the GA, on optimal cleaning schedules based on both thermal and economic behavior of the PHT was studied. The results highlight the efficacy of the GA and a novel chromosome application to the optimization of complex cleaning scheduling. Considerable savings are achieved by the implementation of optimum cleaning schedules for all the ageing scenarios compared with un-optimized cleaning or no cleaning performed in a case study involving a 14-unit crude oil refinery HEN. Fast ageing led to significant reduction in energy losses, and hence, improved heat recovery and fewer number of cleanings due to the higher formation rate of a more thermally conductive coke, compared with slow ageing.