engleski

Fouling Detection in Industrial Heat Exchanger Using Neural Network Models

An online monitoring system is developed for a shell and tube heat exchanger at hydrocracking plant. Neural network models are developed using inferential variables (temperature and flow rates of hot and cold stream) for predicting heat exchanger outlet temperatures. The deviation between predicted and actual values indicates performance degradation due to fouling. The developed models are designed to establish an on-line monitoring system for maintaining operating efficiency of refinery plants.

Fouling detection ; Neural networks ; Fault detection ; Preventive maintenance

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