Data reconciliation and gross error detection in crude oil pre-heat trains undergoing shell-side and tube-side fouling deposition

Abstract

Fouling is a problem in crude oil refineries. The effect of fouling deposition is particularly significant in the heat exchanger network (or pre-heat train) upstream of the crude oil distillation unit. A wide variety of semi-empirical models are available for predicting the fouling behaviour. These models can be obtained by fitting experimental or industrial operating data to a specific fouling model. When industrial data are used, the effect of measurement error and presence of faulty instruments (or gross errors) should be accounted for. This work presents a new methodology that allows for data reconciliation and gross error detection, together with the estimation of fouling model parameters for a pre-heat train undergoing different fouling mechanisms on the shell and tube-sides. The methodology is tested in a simulated case study. It is shown that the data reconciliation and gross error detection algorithms are able to minimise the measurement errors and to identify the presence of single or multiple faulty instruments. The fouling models for each heat exchanger are estimated using the reconciled data, and the fouling behaviour and thermal performance of the network are predicted and analysed.