Abstract
The paper presents the method of scheduling of heat exchangers cleaning from fouling in heat exchanger networks (HEN). The objective function (F) was formulated, taking into account the savings associated with the increase of heat recovery in HEN (as a result of on-line cleaning of the heat exchangers from fouling), reduced by costs of cleaning operation of the exchangers. The decision variables are the set of p integers {n1, n2,..,nj,..np}, expressing the number (nj) of cleaning interventions from fouling of any jth heat exchanger. F maximization belongs to the category of INLP (integer nonlinear programming) programming. F optimization methods are commonly based on random search methods.
The authors proposed a new approach to the F optimization issue, enabling plant staff to make cleaning decisions for selected heat exchangers. Namely, the authors propose to lay down the HEN schedule based on an analysis of the F 's sensitivity to the number (nj) of cleaning interventions of any jth heat exchanger.
The proposed method was used to arrange the HEN cleaning schedule for the Crude Distillation Unit (CDU), processing 800 t/h of crude oil. The analyzed HEN is composed of 26 heat exchangers serving for the heat recovery. The non-random method of the HEN scheduling was used and the resulting savings amounted to 2.33 M USD/y. For comparison, a random method (Monte Carlo method) was used to arrange the HEN cleaning schedule. and the resulting savings amounted to 2.19 M USD/y.
The proposed by the authors the non-random method of HEN scheduling enables reduction the number of cleaning interventions comparing to other methods. For the presented in the article case of CDU plant, the number of cleaning interventions amounted to 36 - for the proposed method whereas for the Monte Carlo method amounted to 49.
