• What heat exchanger fouling prediction frameworks do you see benefiting refinery and petrochemical operations?



  • Tina Owodunni, KBC (A Yokogawa Company), tina.owodunni@kbc.global

    Heat exchanger fouling can significantly limit operations. It reduces the rate of heat transfer in the preheat train, which increases furnace duty and sometimes reduces throughput to meet desired product purity. As a result, it increases operating costs and, most likely, increases CO₂ emissions. Additionally, as the fouling layer reduces the tube inlet diameter and the tube pitch, pressure drop increases and can cause bottlenecks in the feed pump. More severe fouling can force the plant to shut down. Therefore, any refinery or petrochemical plant needs an effective tool to manage fouling.

    While fouling is not measured, it can be calculated from kinetics, such as that introduced by Ebert and Panchal in 1997. However, the activation energy and other parameters must be determined for each crude type, which may be difficult to obtain. Moreover, fouling kinetics is mostly applied to chemical reaction fouling, such as coking. It cannot be used to determine deposition fouling, so kinetic modelling is unlikely to give the complete answer. The most practical way of calculating fouling involves comparing experimental data to rigorous exchanger modelling.

    Rigorous models are available to understand not only the performance of shell and tube exchangers, commonly used in refineries and petrochemical complexes but also other exchangers such as plate heat exchangers. These models calculate the exchanger’s overall heat transfer coefficient when the exchanger is clean (Uclean), and performance is optimised to predict the outlet temperatures. The operating heat transfer coefficient of the exchanger (Uactual) can be calculated from the plant data (such as inlet and outlet temperatures, flow rates, and composition). For a fouling exchanger, Uactual will be lower, so its difference, when compared to Uclean, determines the exchanger’s fouling factor.

    The complexity of these calculations is compounded because plant data often contain errors. Before calculating fouling factors, data reconciliation covering the whole heat exchanger train is needed to fit the measured data to the geometry model to find and correct errors. As a result, fouling calculations become convoluted. Several tools monitor fouling to manage these complexities, such as KBC’s HX Monitor, HTRI’s SmartPM, to name a few. Using a rigorous data reconciliation package, HX Monitor calculates fouling factors, runs cleaning cases, and estimates the benefits of cleaning exchanges. Then, fouling trends calculated for multiple datasets can be used to predict future fouling.



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