Heat exchanger fouling analysis is crucial in optimizing refinery operations, but it often poses challenges due to the expertise and time required for proprietary first principal models. This paper introduces the Canvass AI HX Fouling Management Solution as a game-changing alternative. By automating data processing and analysis, this solution simplifies the optimization of heat exchanger cleaning schedules, making it accessible to existing refinery engineers.
Unlike traditional first principle simulations, Canvass AI's AI-driven approach offers a holistic view of the entire heat exchanger network, accounting for interconnections and delivering more accurate fouling assessments and unfouled outlet temperature predictions. The AI Solution leverages daily plant data, including flow rates, temperatures, and various parameters, to enhance data accuracy and completeness. With this robust dataset, engineers can easily identify fouled heat exchangers, predict heat recovery from clean ones, and plan optimal cleaning schedules. Additionally, the AI model is versatile, capable of predicting heat exchanger fouling from operational changes, promoting best practices in refinery operations. This solution streamlines and enhances heat exchanger fouling management, offering significant benefits to the refinery industry.
An average refinery saves $3.5M/year to $7M/year with heat exchanger fouling management without the need for any outside engineering expertise for implementation or use. Get started with applying this Solution today!
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