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Image-based deep neural network prediction of the heat output of a step-grate biomass boiler

Author

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  • Tóth, Pál
  • Garami, Attila
  • Csordás, Bernadett

Abstract

This work investigates the usage of deep neural networks for predicting the thermal output of a 3MW, grate-fired biomass boiler, based on routinely measured operating parameters and real-time flame imaging. It is hypothesized that flame imaging can provide information regarding the quasi-instantaneous state of combustion, therefore supplementing conventional measurements that generally produce lagging feedback. A deep neural network-based, continuous multistep-ahead prediction scheme was proposed and evaluated by using operational and image data collected through extensive campaigns. It was found that flame imaging increases the accuracy of predictions compared to those obtained by only using operational data. The complexity of biomass combustion was well captured by the proposed deep neural network; furthermore, the deep architecture produced better predictions than shallower ones. The proposed system can reliably predict output water temperatures with errors up to ±1°C, up to approximately 30min ahead of the current time.

Suggested Citation

  • Tóth, Pál & Garami, Attila & Csordás, Bernadett, 2017. "Image-based deep neural network prediction of the heat output of a step-grate biomass boiler," Applied Energy, Elsevier, vol. 200(C), pages 155-169.
    • Handle: RePEc:eee:appene:v:200:y:2017:i:c:p:155-169
    DOI: 10.1016/j.apenergy.2017.05.080
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    Citations

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    Cited by:

    1. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    2. Halil Akbaş & Gültekin Özdemir, 2020. "An Integrated Prediction and Optimization Model of a Thermal Energy Production System in a Factory Producing Furniture Components," Energies, MDPI, vol. 13(22), pages 1-29, November.
    3. Chen, Hua & Yan, Tingting & Zhang, Xiaogang, 2020. "Burning condition recognition of rotary kiln based on spatiotemporal features of flame video," Energy, Elsevier, vol. 211(C).
    4. Han, Zhezhe & Hossain, Md. Moinul & Wang, Yuwei & Li, Jian & Xu, Chuanlong, 2020. "Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network," Applied Energy, Elsevier, vol. 259(C).
    5. Vakalis, Stergios & Moustakas, Konstantinos, 2019. "Modelling of advanced gasification systems (MAGSY): Simulation and validation for the case of the rising co-current reactor," Applied Energy, Elsevier, vol. 242(C), pages 526-533.
    6. Héctor Rodríguez-Rángel & Dulce María Arias & Luis Alberto Morales-Rosales & Victor Gonzalez-Huitron & Mario Valenzuela Partida & Joan García, 2022. "Machine Learning Methods Modeling Carbohydrate-Enriched Cyanobacteria Biomass Production in Wastewater Treatment Systems," Energies, MDPI, vol. 15(7), pages 1-18, March.
    7. Ögren, Yngve & Tóth, Pál & Garami, Attila & Sepman, Alexey & Wiinikka, Henrik, 2018. "Development of a vision-based soft sensor for estimating equivalence ratio and major species concentration in entrained flow biomass gasification reactors," Applied Energy, Elsevier, vol. 226(C), pages 450-460.

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