Additionally, SmartPM enabled detailed evaluation of various anti-foulant treatments under changing crude oil blends. By fitting dynamic fouling models to historical performance data, ENEOS was able to simulate and compare the thermal benefits of each anti-foulant strategy. In one of the studied cycles, an optimised anti- foulant blend led to an improvement in FIT of up to 11°C. This result also helped the operations team estimate the maximum justifiable cost for the anti-foulant, based on measurable thermal benefits and fuel savings. In each of these cases, ENEOS leveraged SmartPM’s modelling and data integration capabilities to develop tailored optimisation strategies. Rather than relying solely on empirical rules or fixed schedules, the refinery teams used validated simulation-backed decision-making to enhance operational performance, reduce maintenance efforts, and better manage costs. Case study 2: Closing air cooler blind spot Air-cooled heat exchangers (ACHEs), or air coolers, are critical in many process industries, especially in locations with limited water availability. However, they are often overlooked in plant optimisation, frequently referred to as a ‘known blind spot’ in process performance management. Variability in ambient air temperature, fluctuating process conditions, and limited instrumentation often contribute to suboptimal operation, leading to energy inefficiency, cooling bottlenecks, and undetected mechanical issues such as fan slippage. SmartPM and Xace provide a combined digital solution to overcome the blind spot in air cooler performance monitoring. X ace, HTRI’s modelling tool for ACHEs, delivers accurate thermal and hydraulic simulations based on validated correlations. Integrated with SmartPM, it enables continuous performance monitoring, diagnostics, and predictive maintenance when the air cooler is part of a larger heat exchanger network. By leveraging monitoring data from a plant data historian and robust models, users can optimise ACHE operation by accounting for seasonal air temperature variation, process changes, and mechanical degradation, ultimately improving cooling performance, energy efficiency, and overall asset reliability. Despite their prevalence, air coolers are often
Cleaning method
Cleaning events
Number of shells cleaned
Conventional method
Chemical & mechanical Mechanical cleaning
3
20
4
10
Optimised
Optimised cleaning
Conventional cleaning
B
3
A
2
No cleaning
1
Date
methods. However, after adopting the SmartPM platform, engineers were able to evaluate the thermal contribution and fouling severity of each exchanger in more detail. This led to a new strategy: cleaning only 10 shells across four campaigns using mechanical methods exclusively. The approach not only reduced maintenance hours and chemical usage but also achieved better energy recovery. This was evident in the improved furnace inlet temperatures and area-under-curve analysis, which showed superior thermal performance over time (see Figure 2 ). Ultimately, the optimised strategy delivered more efficient operation with significantly fewer resources. The Osaka Refinery team used SmartPM to tackle uneven flow distribution and assess the effectiveness of anti-foulant chemical blends. Initially, the refinery operated three parallel exchanger branches with a default flow distribution of approximately 63%, 19%, and 19%. Simulations revealed that this configuration could be improved by redistributing flows to a new pattern of around 44%, 27%, and 29%. This revised flow split allowed for more balanced thermal loading across the exchangers, which in turn improved heat recovery and raised the FIT by approximately 1.5°C. Figure 2 FIT profile illustrating the comparison between the conventional and optimised cleaning process at Marifu Refinery. Labels 1, 2, and 3 denote the cleaning events associated with the conventional cleaning method. Area B denotes the benefit of using the conventional cleaning method and Area A denotes the benefit of switching to the optimised method. The difference in Areas A and B provides the benefit of switching to the optimised method
Refining India
34
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