Advanced convection models, coupled with AI-driven insights, enable real-time performance assessment, predictive maintenance, and proactive decision-making in ethylene cracker furnaces.
Ethylene furnaces are crucial to the performance of petrochemical complexes as they impact capacity, efficiency, and reliability. Smart data analytics transform furnace data into actionable insights, addressing challenges like differential coking and process control variations to optimize run length and yield.
This article focuses on convection bank challenges, where applied artificial intelligence (AI) can provide visibility to address capacity constraints and maintenance costs. AI-driven convection models enhance operational performance by evaluating bank performance, identifying fouling, and detecting fluegas channeling. This approach includes a live modeling solution for estimating coil tube metal temperatures (TMTs), enabling proactive risk management.
An AI-powered dashboard provides real-time insights, performance indicators, and actionable recommendations without overwhelming operators. Integrating convection bank performance prediction with radiant section models further optimizes furnace operations, promoting sustainable production while minimizing risks. Effective monitoring of the convection section is essential for achieving optimal thermal performance, efficiency, and safe operations...
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