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Model predictive control utilizing fuel and moisture soft-sensors for the BioPower 5 combined heat and power (CHP) plant

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  • Kortela, J.
  • Jämsä-Jounela, S.-L.

Abstract

This paper presents a model predictive control (MPC) strategy for efficient energy production in BioGrate boiler. In addition to compensating for the main disturbances caused by variations in fuel quality such as fuel moisture content, and variations in fuel feed, this strategy models water evaporation, and models and controls the fuel bed height of the grate. Usually, combustion power in a furnace have been estimated by utilizing oxygen consumption. There is however a need for more accurate prediction and control of combustion power, which is greatly affected by the fuel bed height and fuel moisture content. It is shown that water evaporation and thermal decomposition of dry fuel can be estimated by utilizing fuel moisture soft-sensor and oxygen consumption calculations respectively. As a result, the primary air can be adjusted to produce the necessary combustion power, and the power output of the boiler can be accurately predicted. This enables efficient stabilization of plant operations. To verify the model, experiments were performed at a BioPower 5 CHP plant, which utilizes BioGrate combustion technology to enable the use of wet biomass fuels with a moisture content as high as 65%. Then the MPC strategy was compared with the currently used control strategy. Finally, the results are presented, analyzed, and discussed.

Suggested Citation

  • Kortela, J. & Jämsä-Jounela, S.-L., 2014. "Model predictive control utilizing fuel and moisture soft-sensors for the BioPower 5 combined heat and power (CHP) plant," Applied Energy, Elsevier, vol. 131(C), pages 189-200.
    • Handle: RePEc:eee:appene:v:131:y:2014:i:c:p:189-200
    DOI: 10.1016/j.apenergy.2014.06.014
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    References listed on IDEAS

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    1. Ström, Henrik & Thunman, Henrik, 2013. "A computationally efficient particle submodel for CFD-simulations of fixed-bed conversion," Applied Energy, Elsevier, vol. 112(C), pages 808-817.
    2. Kortela, J. & Jämsä-Jounela, S-L., 2013. "Fuel moisture soft-sensor and its validation for the industrial BioPower 5 CHP plant," Applied Energy, Elsevier, vol. 105(C), pages 66-74.
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    Cited by:

    1. Yang, Dan & Peng, Xin & Ye, Zhencheng & Lu, Yusheng & Zhong, Weimin, 2021. "Domain adaptation network with uncertainty modeling and its application to the online energy consumption prediction of ethylene distillation processes," Applied Energy, Elsevier, vol. 303(C).
    2. Böhler, Lukas & Fallmann, Markus & Görtler, Gregor & Krail, Jürgen & Schittl, Florian & Kozek, Martin, 2021. "Emission limited model predictive control of a small-scale biomass furnace," Applied Energy, Elsevier, vol. 285(C).
    3. Alexandre Boriouchkine & Sirkka-Liisa Jämsä-Jounela, 2016. "Simplification of a Mechanistic Model of Biomass Combustion for On-Line Computations," Energies, MDPI, vol. 9(9), pages 1-25, September.
    4. Palash Sarkar & Jukka Kortela & Alexandre Boriouchkine & Elena Zattoni & Sirkka-Liisa Jämsä-Jounela, 2017. "Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler," Energies, MDPI, vol. 10(2), pages 1-14, February.
    5. Böhler, Lukas & Görtler, Gregor & Krail, Jürgen & Kozek, Martin, 2019. "Carbon monoxide emission models for small-scale biomass combustion of wooden pellets," Applied Energy, Elsevier, vol. 254(C).
    6. Ferrari, Mario L., 2015. "Advanced control approach for hybrid systems based on solid oxide fuel cells," Applied Energy, Elsevier, vol. 145(C), pages 364-373.
    7. Liang Tian & Xinping Liu & Huanhuan Luo & Tuoyu Deng & Jizhen Liu & Guiping Zhou & Tianting Zhang, 2021. "Soft Sensor of Heating Extraction Steam Flow Rate Based on Frequency Complementary Information Fusion for CHP Plant," Energies, MDPI, vol. 14(12), pages 1-17, June.
    8. Böhler, Lukas & Krail, Jürgen & Görtler, Gregor & Kozek, Martin, 2020. "Fuzzy model predictive control for small-scale biomass combustion furnaces," Applied Energy, Elsevier, vol. 276(C).

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