Hybrid Approach for Advanced Monitoring and Forecasting of Fouling with Application to an Ethylene Oxide Plant

Abstract

Fouling in heat exchangers leads to increased pressure drop, associated with higher energy consumption, utility costs, and CO2 emissions. However, other effects can also take place, threatening process operations and safety. This is the case of ethylene oxide operations, where unplanned outages and decomposition events pose significant safety risks. Therefore, the development of a framework for advanced monitoring and forecasting of heat exchanger fouling is both important and opportune to improve the reliability and safety of the operation. We propose a hybrid approach, where knowledge-based feature generation is integrated with data-driven methods, to forecast key performance indicator that acts as a fouling surrogate. The forecasting model can predict one month ahead with an accuracy of R2 = 0.7. We also show that long-term forecasting is possible with this model, which can be applied to optimize maintenance scheduling. The solution can be extended to other situations where fouling takes place.