Fouling is a leading cause of heat exchanger downtime, which can significantly lower the availability of the overall plant. Here’s how predictive analytics can help optimize heat exchanger maintenance.
Heat exchangers are widely used across industrial processes in automotive, power, oil and gas (O&G), pharmaceuticals, and metals and mining. However, one of the most extreme operating conditions in which heat exchangers are deployed is in refineries and carbon black manufacturing. In these applications, heat exchangers require maintenance every few weeks to ensure efficient functioning. Every heat exchanger maintenance cycle, the facility must be halted – as a result of which, over maintenance (running preventive maintenance before it is required) can result in significant losses, especially for refineries. Moreover, unexpected failures result in even more detrimental outcomes, along with safety hazards.
In this article, see what degrades heat exchanger reliability and performance, and how it can be maximized with predictive maintenance.
Key factors affecting heat exchanger reliability and performance
Shell and tube heat exchangers are the most commonly used heat exchangers in industrial applications, owing to their versatility. However, depending on their application their failure can be caused by numerous fault modes. Thermal or pressure-induced stress corrosion, hole corrosion, pipe bundle failures, and sealing surface leakage are some of the common causes that lead to heat exchangers failure.
In addition, heat exchangers can also suffer significant damage from water sprays due to thermal shock in events of a fire. Therefore, continuous operation within controlled parameters is usually the safer, and more cost-effective course.
Heat exchanger fouling
One of the most common phenomena to which heat exchangers are subject, is fouling. This refers to the deposition of a substance, usually on the tube of the heat exchanger. This substance is usually coke or carbon when heat exchangers are deployed in carbon black plants, although the substance may differ based on their application. Estimates suggest that the losses associated with fouling in heat exchangers amount to ~$16bn across the US and the UK alone.
The deposit of fouling on the heat exchanger tubes lowers the overall heat transfer coefficient (measuring the heat flow between fluids), thereby reducing the efficiency of heat transfer between the cold and the hot fluid. Moreover, fouling increases fluid friction, which in turn leads to a pressure drop across the heat exchanger. To mitigate this, a process called burn out is carried out during maintenance to burn the fouled substance with oxygen and heat. This restores the heat transfer coefficient to its usual operational value, although it may drop over the course of the useful life of the heat exchanger.
Optimizing heat exchanger maintenance with predictive analytics
The first step to enhancing heat exchanger reliability and uptime, is to implement continuous monitoring through the use of sensors. To this end, a few variables must be measured across the heat exchanger. These include the inlet and outlet temperatures of both the fluids, flow of fluids across the heat exchanger (this is usually measured with magnetic inductive flow sensors), and pressure drop at critical points. Advanced systems make use of sensor technology to assess the performance of the cooling tower, and to ensure effective and reliable cleaning of the heat exchanger during maintenance cycles.
With these sensors, it is possible to study the effect of operational parameters on the heat exchange efficiency and mean time between repair cycles. In addition to extending the uptime, this also helps maximize the remaining useful life (RUL) of the heat exchanger, which is typically between 10-25 years.
Predictive maintenance for heat exchanger fouling: how it works
One of the most important use cases of predictive maintenance for heat exchangers, is the prediction of fouling rate to optimize cleaning schedules. Because fouling is a continuous process that necessitates repetitive repairs, using predictive maintenance to mitigate its effect can help reduce maintenance costs by as much as 30%.
Heat exchanger predictive maintenance makes use of models to estimate the rate of fouling, and the resultant heat transfer degradation in a given operating condition. For this, simpler models make use of temperature data streams from four points in a heat exchanger – that is, the inlet and outlet points of the two fluids. These variables are used to estimate heat transfer performance over time, and how it varies across multiple maintenance cycles.
More advanced models contextualize the model to the specific scenario of application. For example, they will take into account the specific heat capacities of the hot and cold liquids, logarithmic mean temperature difference (LMTD), and mass flow rates to calculate the thermal capacity ratio.
Deep learning is shown to be an effective technique, and ensemble learning shows advantage over single-model neural networks. With deep learning, 90%+ prediction accuracy can be achieved, although the method is sensitive to noise in the database. For some organizations, lack of training data can be a key hurdle in implementing a predictive maintenance solution for heat exchangers, because they have been carrying out maintenance and monitoring using manual methods. This is where technology partners can prove immensely valuable. Their solutions bring deployment-ready models that have been trained on hundreds of datasets, and can quickly scale to maximum prediction accuracy within a few maintenance cycles.
Finally, a key point to note when deploying a predictive maintenance solution for exchangers, is to take a long view when it comes to sensor technology. Digitizing and automating heat exchanger control can significantly improve the efficiency and performance of the overall thermodynamic process. To this end, implementing fast-reacting temperature transmitters can be useful for achieving faster heat-valve control, which in turn can increase energy savings through lower safety margins.
Final words
Heat exchangers are a key equipment in industrial processes, and negligence in maintenance schedules can result in suboptimal operations, losses from downtime and energy waste, and safety hazards. Optimizing maintenance schedules to mitigate the effects of fouling can significantly improve the reliability of heat exchangers and maximize their availability. Predictive maintenance solutions are the key to achieving these outcomes, especially in industries like O&G and petrochemicals.
Maximize heat exchanger availability and improve the uptime of your plant with UptimeAI for predictive maintenance. Contact now to get started.