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



  • Veerala Hari Krishna, L & T Technology Services Ltd, veeralahari.krishna@ltts.com

    In refinery and petrochemical operations, one of the major problems is heat exchanger fouling, causing loss of revenue in terms of equipment replacement cost, maintenance, and cleaning expenses. Moreover, fouling is responsible for productivity losses. Heat exchanger fouling also leads to a reduction of input feed rates to the plant. Heat transfer resistance increases due to increased fouling thickness, which continuously lowers the heat exchanger operating heat duty. This predicates the need for monitoring heat exchanger fouling and forecast cleaning.

    Heat exchanger operations are monitored only for the rate of heat transfer. Furthermore, the heat transfer rate can be controlled by altering flow rates. An experienced process operator can see when a heat exchanger no longer transfers its typical thermal output under certain flow conditions. In such cases, the operator has three options available to handle the situation: increasing the flow rate to increase the thermal output, reducing the flow rate to achieve the required outlet temperatures of the fluids, or doing nothing.

    In the first alternative, the heat transfer rate of the heat exchanger can be raised to its former level, but the outlet temperatures of the fluids do not attain their former levels. In the second alternative, the outlet temperatures of the fluids can be returned to approximately the starting position at the expense of the fluid flow rates, which may slow down production. In the third alternative, both the outlet temperatures of the fluids and the heat transfer rate are driven further away from their original operating points. It is clear, nevertheless, that in such a situation, the heat exchanger’s performance has deteriorated, and the previous operating level is no longer possible.

    Many parameters vary greatly depending on fluid flow rates. For the parameters to be comparable, they must, therefore, be proportioned to the prevailing flow conditions. Appropriate efficiency monitoring methods are limited by the available process measurements. However, diverse analyses can also be conducted with very few measurements. The efficiency of a heat exchanger can be examined, inter alia, with the help of the following measurements shown in Case 1 and Case 2.
    Some heat exchanger fouling prediction frameworks that can benefit refinery and petrochemical operations include:
    • Empirical correlation equations derived from experimental data that can be used to estimate fouling rates based on operating conditions. These correlations are often based on heat transfer coefficients and can be used to estimate fouling rates in a variety of heat exchanger types
    • Fouling indices metrics that can be used to quantify the tendency of fluids to foul heat exchangers. These indices are based on fluid properties such as viscosity, density, and thermal conductivity, and can be used to estimate fouling rates based on fluid composition and operating conditions
    • Artificial neural network (ANN) machine learning algorithms that can be used to predict fouling rates in heat exchangers. ANNs can learn from historical data to identify patterns and predict future fouling rates based on input variables, such as temperature, flow rate, and fluid properties
    • Expert systems software programs that use knowledge-based rules to predict fouling rates based on input variables such as fluid properties and operating conditions. Expert systems can incorporate knowledge from experienced operators and engineers to provide accurate predictions of fouling rates in heat exchangers.

    Overall, a combination of these frameworks can be used to provide accurate and reliable predictions of fouling rates in heat exchangers in 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.



  • Ajay Kumar Gupta, Dorf Ketal Chemicals, ajay.gupta@dorfketal.com

    Fouling prediction framework for heat exchangers in refinery and petrochemical plants can be best achieved with the integration of analytical, statistical, and AI-based predictive algorithm approaches:
    υ    Analytical approaches can include the primary fouling precursors in the feed stream to the exchanger and deposit sample analysis. Screening the correct antifoulant and adjusting the chemical dosage based on key feed quality variables and fouling precursors can significantly help in fouling management.
    ϖ    For fouling prediction on crude, changes in current feed with respect to baseline saturates, asphaltene, resin, and aromatics content/ratio can help gauge the potential fouling rate. Dorf Ketal uses the proprietary ‘Oil compatibility model (OCM)’ software to predict the stability of the crude blends.
    ω    For relative cleaning effectiveness, baseline fouling rate data analysis using good simulation software or statistical tools such as multiple variable regression analysis of previous runs can help to understand the performance deviation.
    ξ    Analysis of actual heat transfer rate, fouling factor and overall heat transfer coefficient (Ud), viscosity, and fluid velocity within the exchangers can help in understanding the fouling control before and after cleaning.
    ψ    AI-based models can help identify key operating variables, which are mainly impacting on fouling rate/run length of the exchanger performance. Modulating these variables based on AI model output has helped refiners to maximise the heat transfer rate/yield improvement.