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Data-driven identification and model predictive control of biomass gasification process for maximum energy production

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  • Elmaz, Furkan
  • Yücel, Özgün

Abstract

Biomass gasification is an environment-friendly energy conversion process that utilizes bio-waste materials to produce combustible gases. In recent literature, machine learning-based techniques are used to model biomass gasification process. Even though these methods are reported for being viable, developed models’ time-independent structure fundamentally limited their prediction capabilities. Furthermore, control of biomass gasification is not studied in the literature despite its importance for industrial applications. We conducted this study in two parts. Firstly, we developed a time-dependent identification model to describe and predict outcomes of biomass gasification using non-linear autoregressive with exogenous neural networks (NARXNN) and experimentally collected data set. The developed model showed exceptional success by reaching R2> 0.98 for all output variables. Secondly, we designed a model predictive controller (MPC) in order to control a certain output variable at the desired state. For this purpose, we created polynomial regression models and online optimization routines. Moreover, the designed controller is challenged in practical scenarios such as maximum hydrogen production to test its usability in practical applications. MPC showed satisfactory performance for all scenarios and also showed high compliance with the experimental data which further strengthened its practical usability potential.

Suggested Citation

  • Elmaz, Furkan & Yücel, Özgün, 2020. "Data-driven identification and model predictive control of biomass gasification process for maximum energy production," Energy, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301444
    DOI: 10.1016/j.energy.2020.117037
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    References listed on IDEAS

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    1. Baruah, Dipal & Baruah, D.C., 2014. "Modeling of biomass gasification: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 806-815.
    2. Mutlu, Ali Yener & Yucel, Ozgun, 2018. "An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification," Energy, Elsevier, vol. 165(PA), pages 895-901.
    3. Mendiburu, Andrés Z. & Carvalho, João A. & Zanzi, Rolando & Coronado, Christian R. & Silveira, José L., 2014. "Thermochemical equilibrium modeling of a biomass downdraft gasifier: Constrained and unconstrained non-stoichiometric models," Energy, Elsevier, vol. 71(C), pages 624-637.
    4. Mendiburu, Andrés Z. & Carvalho, João A. & Coronado, Christian J.R., 2014. "Thermochemical equilibrium modeling of biomass downdraft gasifier: Stoichiometric models," Energy, Elsevier, vol. 66(C), pages 189-201.
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    Cited by:

    1. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Rafiee, Shahin & Hafezi, Amir & Du, Xinyi & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2023. "Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning," Energy, Elsevier, vol. 278(PB).
    2. Zhang, Jinchun & Hou, Jinxiu & Zhang, Zichuan, 2022. "Real-time identification of out-of-control and instability in process parameter for gasification process: Integrated application of control chart and kalman filter," Energy, Elsevier, vol. 238(PB).
    3. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    4. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).

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