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A hybrid approach for advanced monitoring and forecasting of fouling with application to an ethylene oxide plant
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  • Joel Sansana,
  • Ricardo Rendall,
  • Ivan Castillo,
  • Luc de Bruijne,
  • Jonathan Huggins,
  • Ailene Phillips,
  • Marco Reis
Joel Sansana
University of Coimbra Faculty of Sciences and Technology
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Ricardo Rendall
The Dow Chemical Company
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Ivan Castillo
Dow Chemical Company
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Luc de Bruijne
The Dow Chemical Company
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Jonathan Huggins
The Dow Chemical Company
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Ailene Phillips
The Dow Chemical Company
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Marco Reis
University of Coimbra

Corresponding Author:[email protected]

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Abstract

Fouling in heat exchangers leads to increased pressure drop, associated with higher energy consumption, utility costs, and CO$_2$ 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 a key performance indicator (KPI) that acts as a fouling surrogate. The forecasting model can predict one-month ahead with an accuracy of R$^2$ = 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.