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Fuzzy model predictive control for small-scale biomass combustion furnaces

Author

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  • Böhler, Lukas
  • Krail, Jürgen
  • Görtler, Gregor
  • Kozek, Martin

Abstract

This work presents a fuzzy model predictive controller for small-scale grate furnaces based on a newly derived biomass combustion model. Several local linear controllers are designed for a selected number of operating points utilizing a gap metric. The resulting local predictive controllers are merged with membership functions to form a global nonlinear fuzzy control structure. The presented framework intends to improve the transient and steady state operation by applying an optimal control strategy with state estimation and to cover the entire operating range of the furnace. The open loop results of the introduced combustion model are parameterized and cross-validated with measured data from a test furnace. In order to find suitable parameters for the grey-box model, a local sensitivity analysis is conducted to contribute to an efficient parameter estimation process. Closed loop simulation results of the fuzzy model predictive controller, a linear model predictive controller and a PI control algorithm are presented and compared. Based on the performance of the proposed fuzzy controller, its application, advantages and disadvantages are discussed. Additionally, the impact of the different controllers on the formation of carbon monoxide is investigated based on estimation models from literature. The simulation results show that the fuzzy model predictive controller performs best in the considered categories.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920308515
    DOI: 10.1016/j.apenergy.2020.115339
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. Caposciutti, Gianluca & Barontini, Federica & Antonelli, Marco & Tognotti, Leonardo & Desideri, Umberto, 2018. "Experimental investigation on the air excess and air displacement influence on early stage and complete combustion gaseous emissions of a small scale fixed bed biomass boiler," Applied Energy, Elsevier, vol. 216(C), pages 576-587.
    4. Lerkkasemsan, Nuttapol, 2017. "Fuzzy logic-based predictive model for biomass pyrolysis," Applied Energy, Elsevier, vol. 185(P2), pages 1019-1030.
    5. Rahat, Alma A.M. & Wang, Chunlin & Everson, Richard M. & Fieldsend, Jonathan E., 2018. "Data-driven multi-objective optimisation of coal-fired boiler combustion systems," Applied Energy, Elsevier, vol. 229(C), pages 446-458.
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    Cited by:

    1. Ding, Haixu & Tang, Jian & Qiao, Junfei, 2023. "Dynamic modeling of multi-input and multi-output controlled object for municipal solid waste incineration process," Applied Energy, Elsevier, vol. 339(C).
    2. Chen, Tao & Sjöblom, Jonas & Ström, Henrik, 2022. "Numerical investigations of soot generation during wood-log combustion," Applied Energy, Elsevier, vol. 325(C).
    3. 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).
    4. Stanisławski, Rafał & Robert Junga, & Nitsche, Marek, 2022. "Reduction of the CO emission from wood pellet small-scale boiler using model-based control," Energy, Elsevier, vol. 243(C).
    5. Tuo Sheng & Haifeng Luo & Mingliang Wu, 2024. "Design and Simulation of a Multi-Channel Biomass Hot Air Furnace with an Intelligent Temperature Control System," Agriculture, MDPI, vol. 14(3), pages 1-18, March.

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