Mitigation of Fouling in Refinery Heat Exchanger Networks by Optimal Management of Cleaning

Abstract

A method for optimizing the cleaning schedule in large continuously operating heat exchanger networks (HENs) such as refinery crude preheat trains is presented. The method is based on two heuristics: (i) discretisation of the operating horizon into a number of equally long periods in which cleaning decisions are allocated and (ii) solution of the resulting mixed integer nonlinear programming (MINLP) problem by a multiple starting point strategy and selection of the best locally optimal solution. The network performance equations are written in terms of the binary decision variables and are not linearized further, giving rise to a nonconvex objective function. The formulation includes constraints set by pumparound operation and pressure drop. Different deterministic relationships between pressure drop and fouling resistances are discussed. The form of the objective function is discussed, as a fixed horizon approach has a significant effect on the results obtained. Solution of the MINLP model is demonstrated using a commercial MINLP solver (DICOPT++) for two case studies: (I) an idealized network containing 14 exchangers over three years and (II) an operational plant featuring 27 exchangers for a two-year horizon. The fouling models and parameters for study II were obtained by reconciliation of plant data, and the relatively short horizon reported was due to limitations in the NLP solver rather than to the model. The results for study II are compared with those generated from a simpler approach based on a “greedy” algorithm. The case study II results are also interpreted in terms of selection of appropriate fouling mitigation strategies. The effectiveness of the MINLP approach is discussed, particularly with regard to obtaining globally optimal solutions.