Process Point successfully implemented a cutting-edge data-driven intelligence solution for a major Middle Eastern Petrochemical complex, revolutionizing their heat exchanger network management. By developing an innovative fouling prediction model, we significantly enhanced operational reliability and efficiency in their 500, 000 tons/year ethylene production facility.
Client Profile:
Industry: Petrochemicals
Location: Middle East
Production Capacity: 500, 000 tons/year of ethylene
Key Challenge: Optimizing heat exchanger performance with limited process data
Only 4 out of 6 required parameters are available for conventional heat transfer models.
Complex interdependencies among parameters hinder traditional fouling estimation.
Need for real-time fouling monitoring in cracked loop exchangers.
Optimization of cleaning schedules to minimize production disruptions.
Developed a novel proxy variable for fouling measurement using limited available data
Implemented techniques to account for throughput variations
Utilized a combination of classification, clustering, and regression methods
Employed grid optimization for selecting the most effective algorithms
Deployed state-of-the-art deep learning models for high-precision fouling predictions
