Estimation of Fouling Threshold Parameters for Crude Oil Heat Exchanger Networks Using Data Reconciliation and Gross Error Detection

Source: AIChE
  • Type:
    Conference Presentation
  • Conference Type:
    AIChE Spring Meeting and Global Congress on Process Safety
  • Presentation Date:
    April 3, 2019
  • Duration:
    30 minutes
  • Skill Level:
    Intermediate
  • PDHs:
    0.50

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In crude oil refineries, fouling in heat exchangers and heat exchanger networks (HENs) represents a challenging and complicated issue. Its occurrence produces major impacts in thermal, hydraulic and economic performance. Increase in fuel consumption, pressure drop across heat exchangers and CO2 emissions are examples of a wide series of consequences that are attributed to fouling in almost every crude oil refinery. In order to mitigate fouling, different approaches have been developed. Previous studies have discovered correlations among fouling deposition and different operational variables such as wall temperature and flow velocity. This dependency has been further exploited for defining several techniques for either design or retrofit of heat exchangers to mitigate the impact of fouling. Current approaches aim to obtain an accurate simulation model for heat exchanger networks, as well as methodologies for including fouling deposition. If succeeded, these simulation models can potentially improve fouling predictions and decision-making processes related to operational optimisation and cleaning schedules. The most common fouling models available are threshold models. These models are able to define a set of operating conditions in which fouling can be avoided. However, threshold models present a series of parameters that need to be adjusted for each type of crude oil, affecting the design of generalised strategies for fouling modelling in crude oil heat exchanger networks. This set of parameters can be estimated via process measurements as these realistically represent the interactions within a HEN. The main challenges are the presence of measurement error and the amount of available data. Both of these challenges can be accounted for using data reconciliation and gross error detection. The adjustment of measurements in order to satisfy process constraints and the identification of faulty measurements are complementary methods that allow for obtaining reliable data and for a deep analysis of the existing plant-instrumentation. This work proposes a flexible-structured simulation scheme for heat exchanger networks, along with the minimisation of the measurement error, and a parameter estimation based on stochastic optimisation for calculating fouling model parameters. The use of this methodology allows for simultaneous calculation of fouling model parameters for each side of heat exchangers within a HEN. Moreover, the data reconciliation and gross error detection algorithms assess the quality of the measured data, and at the same time analyses the estimation-potential of unmeasured variables, whenever possible. All these insights are shown in a case study, where a crude oil HEN subject to fouling is used to test the proposed methodology. Results show that the use of data reconciliation improves the reliability of measurements and faulty instruments are correctly identified, allowing for an accurate estimation of fouling model parameters. The results from the parameter estimation can be used in assessing the thermal performance of a HEN, as well as in the prediction of fouling behaviour, which is paramount for the design and optimisation of fouling mitigation strategies.
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