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Simplification of a Mechanistic Model of Biomass Combustion for On-Line Computations

Author

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  • Alexandre Boriouchkine

    (Department of Biotechnology and Chemical Technology, School of Chemical Technology, Aalto University, Aalto 00076, Finland)

  • Sirkka-Liisa Jämsä-Jounela

    (Department of Biotechnology and Chemical Technology, School of Chemical Technology, Aalto University, Aalto 00076, Finland)

Abstract

Increasing utilization of intermittent energy resources requires flexibility from energy boilers which can be achieved with advanced control methods employing dynamic process models. The performance of the model-based control methods depends on the ability of the underlying model to describe combustion phenomena under varying power demand. This paper presents an approach to the simplification of a mechanistic model developed for combustion phenomena investigation. The aim of the approach is to simplify the dynamic model of biomass combustion for applications requiring fast computational times while retaining the ability of the model to describe the underlying combustion phenomena. The approach for that comprises three phases. In the first phase, the main mechanisms of heat and mass transfer and limiting factors of the reactions are identified in each zone. In the second phase, each of the partial differential equations from the full scale model are reduced to a number of ordinary differential equations (ODEs) defining the overall balances of the zones. In the last phase, mathematical equations are formulated based on the mass and energy balances formed in the previous step. The simplified model for online computations was successfully built and validated against industrial data.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:735-:d:77921
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    2. Jan Valíček & Zuzana Palková & Marta Harničárová & Milena Kušnerová & Ondrej Lukáč, 2017. "Thermal and Performance Analysis of a Gasification Boiler and Its Energy Efficiency Optimization," Energies, MDPI, vol. 10(7), pages 1-13, July.
    3. Steven, Soen & Restiawaty, Elvi & Bindar, Yazid, 2021. "Routes for energy and bio-silica production from rice husk: A comprehensive review and emerging prospect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Ion V. Ion & Florin Popescu & Razvan Mahu & Eugen Rusu, 2021. "A Numerical Model of Biomass Combustion Physical and Chemical Processes," Energies, MDPI, vol. 14(7), pages 1-19, April.
    5. Mohammad Hosseini Rahdar & Fuzhan Nasiri, 2020. "Operation Adaptation of Moving Bed Biomass Combustors under Various Waste Fuel Conditions," Energies, MDPI, vol. 13(23), pages 1-18, December.

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