Fouling modelling and mitigation for crude oil heat exchanger networks using reconciled operating data

In crude oil refineries, fouling in heat exchangers and heat exchanger networks represents one the most complex and challenging issues. Its occurrence produces major impacts in thermal, hydraulic and economic performance on every heat transfer process. Increasing in fuel consumption, pressure drop and CO2 emissions across heat exchangers are examples of a wide series of consequences that are attributed to fouling in different petro-chemical process industries. Predictive models for fouling deposition have become a useful tool for analysing thermal performance on heat exchanger networks. A wide range of semi-empirical models have been reported in the literature considering different types of crude oil. Most of these models present a series of parameters that need to be fitted using experimental data. However, there is an uncertain relation between field and laboratory conditions, compromising the quality of predictions and greatly affecting further simulation and optimisation of heat exchanger networks. The proposed approach presents an alternative method for determining fouling models using reconciled operating data. Data reconciliation is included in order to account for the effect of measurement errors and presence of faulty instruments within a given set of measurements. Depending on the available redundancy, further analysis can take place regarding feasibility of the data reconciliation problem, along with process variable classification. Once the data have been reconciled, the specific set of fouling model parameters can be fitted and implemented into the framework for operational optimisation for fouling mitigation, or optimisation of cleaning schedules. Different sets of cleaning actions can be analysed and further optimised by minimising the total operating cost, once the specific fouling models are successfully determined, resulting and economic and environmental savings.