excluded from real-time optimisation frameworks due to their perceived simplicity, air being ‘free’, and a lack of comprehensive monitoring tools. Operational challenges include seasonal variations in ambient air temperatures, wind, and humidity, variations in process stream flow rates, temperature, and composition, lack of visibility into fan performance, and uncertainty in heat duty profile along each fan bay. The result is frequent overcooling or undercooling, excessive fan power consumption, missed opportunities to reduce utility costs, or having to reduce capacity when the required cooling is not achieved. The SmartPM model-based performance monitoring platform integrates field data with proprietary heat transfer and pressure drop methods. For air coolers, this enables: • Modelling with actual process conditions. • Accounting for seasonal air temperature variation. • Evaluating required number of fans. • Detecting fan drive slippage and unstable operation. This non-invasive detection mechanism allows effective planning for maintenance and avoids catastrophic cooling failures. In a mid-sized crude refinery, an ACHE is used to cool a product stream before it flows into storage. Its performance is subject to seasonal variation, upstream process changes, and equipment condition. SmartPM/ X ace helped the refinery predict fan usage trends throughout the year and uncover significant opportunities for optimisation (see Figure 3 ). A year-long operational analysis showed the following fan usage pattern: • Early summer : As ambient temperatures peak, the air cooler reaches full load conditions. All fans operate continuously to meet the high cooling demand and maintain temperature specifications for the product stream. • Autumn to winter : Cooling demand naturally declines with lower ambient temperatures. The number of fans in operation decreases – from full capacity to five fans in autumn, and just four in winter – without compromising cooling performance. • Post-cleaning impact : Following a cleaning event in late winter for an upstream heat exchanger, thermal efficiency improved in the preheat train, resulting in a lower outlet
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temperature from upstream units. This reduced the thermal load on the air cooler, enabling operations to reduce the number of active fans further – down to three fans – while still achieving target storage temperatures. • Spring to summer transition : As temperatures begin to rise again in spring, the required number of fans gradually increases to keep up with the growing cooling demand. Analysing these data through the SmartPM/ X ace platform allows the refinery to: • Optimise fan operation seasonally, avoiding unnecessary mechanical wear and energy consumption by adjusting the number of operating fans based on real-time performance metrics. Figure 3 Predicted air cooler operation with optimised fan usage showing number of fans in operation, standard face velocity, total fan driver power, and air side pressure drop
Refining India
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