Crude refinery preheat trains (PHTs) are a major part of a refining process as it reduces the quantity of energy required to heat the crude oil to its distillation temperature. Fouling is a long known problem in the PHTs; the ability of a refinery to process different crude blends or to increase its production capacity strongly depends on the thermal and hydraulic performance of the PHT under fouling conditions. This paper describes a data reconciliation approach that utilizes historical plant data, which usually consists of missing information and uncertainty, to generate a so called ‘dynamic fouling model’. Dynamic fouling models are used in a simulation methodology to predict plant performance under variable operating conditions. A discussion in the latest developments in modelling and simulation of PHTs subject to fouling is provided. A novel software tool, SmartPM, was successfully utilized to study heat recovery paths, cleaning schedules and furnace firing capacity on a UK based crude refinery PHT case study.
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