Hybrid model for fault detection and diagnosis in an industrial distillation column
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Julia Picabea
Abstract
The present work describes a method of automatic fault detection and identification based on a hybrid model (HM): First Principles – Neural Network. The FPM can simulate a wide range of situations while the NN corrects the model output using information from the historical data of the process. Operating conditions corresponding to different types of faults were simulated with the HM and saved with their description in a process state library. To detect a fault, the online measured data was compared with that corresponding to the operation under normal conditions. If a significant deviation was detected, the current state was compared with all the states stored in the process state library and it was identified as the one at the shortest distance. The method was tested with real data from a methanol-water industrial distillation column. During the studied period of operation of the plant, two faults were identified and reported. The proposed method was able to identify such failures more effectively than an equivalent model of first principles. The results obtained show that the proposed method has a great potential to be used in the automatic diagnosis of faults in refining and petrochemical processes.
Funding source: Universidad de Buenos Aires
Award Identifier / Grant number: UBACyT20020130100544BA
Funding source: Agencia Nacional de Promoción Científica y Tecnológica
Award Identifier / Grant number: PICT2014-0704
Funding source: Consejo Nacional de Investigaciones Científicas y Técnicas
Award Identifier / Grant number: PIP1122015-0100902CO
Acknowledgments
This work was supported by ANPCyT [PICT2014-0704]; Universidad de Buenos Aires [UBACyT 20020130100544BA] and CONICET [PIP1122015-0100902CO]. The authors would also like to acknowledge the operations staff of the methanol plant of YPF (Plaza Huincul).
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This research was funded by the ANPCyT [PICT2014-0704]; Universidad de Buenos Aires [UBACyT 20020130100544BA] and CONICET [PIP1122015-0100902CO].
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Articles in the same Issue
- Frontmatter
- Research Articles
- Optimization of operational and design parameters of a Simultaneous Mixer-Separator for enhanced continuous biodiesel production
- Hybrid model for fault detection and diagnosis in an industrial distillation column
- Modeling of CCLP in koryo extract production
- Response surface methodology for predicting the dimethylphenol removal from wastewater via reverse osmosis process
- Multi-objective approach for a combined heat and power geothermal plant optimization
- Design of new activated carbon based adsorbents for improved desulfurization of heavy gas oil: experiments and kinetic modeling
Articles in the same Issue
- Frontmatter
- Research Articles
- Optimization of operational and design parameters of a Simultaneous Mixer-Separator for enhanced continuous biodiesel production
- Hybrid model for fault detection and diagnosis in an industrial distillation column
- Modeling of CCLP in koryo extract production
- Response surface methodology for predicting the dimethylphenol removal from wastewater via reverse osmosis process
- Multi-objective approach for a combined heat and power geothermal plant optimization
- Design of new activated carbon based adsorbents for improved desulfurization of heavy gas oil: experiments and kinetic modeling
