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Total Suspended Particle Emissions Modelling in an Industrial Boiler

Author

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  • Guillermo Ronquillo-Lomeli

    (Department of Energy, Center for Engineering and Industrial Development, Santiago de Querétaro 76125, México)

  • Gilberto Herrera-Ruiz

    (Faculty of Engineering, Autonomous University of Queretaro, Santiago de Querétaro 76010, México)

  • José Gabriel Ríos-Moreno

    (Faculty of Engineering, Autonomous University of Queretaro, Santiago de Querétaro 76010, México)

  • Irving Alfredo Alejandro Ramirez-Maya

    (Department of Energy, Center for Engineering and Industrial Development, Santiago de Querétaro 76125, México)

  • Mario Trejo-Perea

    (Faculty of Engineering, Autonomous University of Queretaro, Santiago de Querétaro 76010, México)

Abstract

Particulate matter emission into the atmosphere is a massive-scale problem. Fossil fuel combustion is an important source of this kind of pollution. The knowledge of total suspended particle (TSP) emissions is the first step for TSP control. The formation of TSP emissions is poorly understood; therefore new approaches for TSP emissions source modelling are required. TSP modelling is a multi-variable non-linear problem that would only require basic information on boiler operation. This work reports the development of a non-linear model for TSP emissions estimation from an industrial boiler based on a one-layer neural network. Expansion polynomial basic functions combined with an orthogonal least-square and model structure selection approach were used for modelling. The model required five independent boiler variables for TSP emissions estimation. Data from the data acquisition system of a 350 MW industrial boiler were used for model development and validation. The results show that polynomial expansion basic functions are an excellent approach to solve modelling problems related to complex non-linear systems in the industry.

Suggested Citation

  • Guillermo Ronquillo-Lomeli & Gilberto Herrera-Ruiz & José Gabriel Ríos-Moreno & Irving Alfredo Alejandro Ramirez-Maya & Mario Trejo-Perea, 2018. "Total Suspended Particle Emissions Modelling in an Industrial Boiler," Energies, MDPI, vol. 11(11), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3097-:d:181651
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    References listed on IDEAS

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    1. Aragón, Fernando M. & Miranda, Juan Jose & Oliva, Paulina, 2017. "Particulate matter and labor supply: The role of caregiving and non-linearities," Journal of Environmental Economics and Management, Elsevier, vol. 86(C), pages 295-309.
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    Cited by:

    1. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    2. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.

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