Classification and estimation of unmeasured process variables in crude oil pre-heat trains subject to fouling deposition

• Published in: Computers & chemical engineering Vol. 137; p. 106779
• Main Authors: Loyola-Fuentes, José, Smith, Robin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 09.06.2020
• Subjects: Data reconciliation; Energy recovery; Gross error detection; Heat exchanger networks; Data reconciliation; Energy recovery; Heat exchanger networks; Gross error detection
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2020.106779

•Matrix-based linear model for heat exchanger network simulation.•Consideration of fouling contributions from shell-side and tube-side.•Time-dependent updating procedure for fouling resistance.•Data reconciliation considering random and gross errors.•Classification of measured and unmeasured variables based on their feasibility of estimation.•Fitted fouling models used for monitoring and predicting purposes. [Display omitted] Crude oil refineries use a series of measurement instruments to monitor process units. One of these units is the pre-heat train. Flow rate and temperature measurements are taken from specific locations for monitoring purposes, especially fouling deposition. The availability of these measurements is related to the instrumentation cost, which limits the number of instruments installed on each unit. Dealing with missing measurements requires specific techniques for allowing the estimation of such variables. Data reconciliation and gross error detection can be integrated to improve the accuracy of process measurements. This work presents an integrated approach that considers the estimation of missing measurements, identification and estimation of measurement bias and reconciliation of measured data. The set of reconciled data is used for determining fouling models to predict the thermal performance of a crude oil pre-heat train. The fouling models can also be used for fouling mitigation and optimisation of cleaning schedules.