IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v195y2020ics0360544220301444.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220301444
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramos, Ana & Monteiro, Eliseu & Rouboa, Abel, 2019. "Numerical approaches and comprehensive models for gasification process: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 188-206.
    2. Vakalis, Stergios & Moustakas, Konstantinos, 2019. "Modelling of advanced gasification systems (MAGSY): Simulation and validation for the case of the rising co-current reactor," Applied Energy, Elsevier, vol. 242(C), pages 526-533.
    3. Sérgio Ferreira & Eliseu Monteiro & Paulo Brito & Cândida Vilarinho, 2019. "A Holistic Review on Biomass Gasification Modified Equilibrium Models," Energies, MDPI, vol. 12(1), pages 1-31, January.
    4. Aydin, Ebubekir Siddik & Yucel, Ozgun & Sadikoglu, Hasan, 2017. "Development of a semi-empirical equilibrium model for downdraft gasification systems," Energy, Elsevier, vol. 130(C), pages 86-98.
    5. Silva, Isabelly P. & Lima, Rafael M.A. & Silva, Gabriel F. & Ruzene, Denise S. & Silva, Daniel P., 2019. "Thermodynamic equilibrium model based on stoichiometric method for biomass gasification: A review of model modifications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    6. Ismail, Tamer M. & Ramos, Ana & Monteiro, Eliseu & El-Salam, M. Abd & Rouboa, Abel, 2020. "Parametric studies in the gasification agent and fluidization velocity during oxygen-enriched gasification of biomass in a pilot-scale fluidized bed: Experimental and numerical assessment," Renewable Energy, Elsevier, vol. 147(P1), pages 2429-2439.
    7. Elmaz, Furkan & Yücel, Özgün & Mutlu, Ali Yener, 2020. "Predictive modeling of biomass gasification with machine learning-based regression methods," Energy, Elsevier, vol. 191(C).
    8. Safarian, Sahar & Unnthorsson, Runar & Richter, Christiaan, 2020. "The equivalence of stoichiometric and non-stoichiometric methods for modeling gasification and other reaction equilibria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    9. Silva, Isabelly P. & Lima, Rafael M.A. & Santana, Hortência E.P. & Silva, Gabriel F. & Ruzene, Denise S. & Silva, Daniel P., 2022. "Development of a semi-empirical model for woody biomass gasification based on stoichiometric thermodynamic equilibrium model," Energy, Elsevier, vol. 241(C).
    10. Safarian, Sahar & Unnþórsson, Rúnar & Richter, Christiaan, 2019. "A review of biomass gasification modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 378-391.
    11. Qitai Eri & Wenzhen Wu & Xinjun Zhao, 2017. "Numerical Investigation of the Air-Steam Biomass Gasification Process Based on Thermodynamic Equilibrium Model," Energies, MDPI, vol. 10(12), pages 1-19, December.
    12. Samadi, Seyed Hashem & Ghobadian, Barat & Nosrati, Mohsen, 2020. "Prediction and estimation of biomass energy from agricultural residues using air gasification technology in Iran," Renewable Energy, Elsevier, vol. 149(C), pages 1077-1091.
    13. Csaba Fogarassy & Laszlo Toth & Marton Czikkely & David Christian Finger, 2019. "Improving the Efficiency of Pyrolysis and Increasing the Quality of Gas Production through Optimization of Prototype Systems," Resources, MDPI, vol. 8(4), pages 1-14, December.
    14. Sreejith, C.C. & Haridasan, Navaneeth & Muraleedharan, C. & Arun, P., 2014. "Allothermal air–steam gasification of biomass with CO2 (carbon dioxide) sorption: Performance prediction based on a chemical kinetic model," Energy, Elsevier, vol. 69(C), pages 399-408.
    15. Einara Blanco Machin & Daniel Travieso Pedroso & Daviel Gómez Acosta & Maria Isabel Silva dos Santos & Felipe Solferini de Carvalho & Adrian Blanco Machín & Matías Abner Neira Ortíz & Reinaldo Sánchez, 2022. "Techno-Economic and Environmental Assessment of Municipal Solid Waste Energetic Valorization," Energies, MDPI, vol. 15(23), pages 1-17, November.
    16. Freda, Cesare & Tarquini, Pietro & Sharma, Vinod Kumar & Braccio, Giacobbe, 2022. "Thermodynamic improvement of solar driven gasification compared to conventional one," Energy, Elsevier, vol. 261(PA).
    17. Amaro, Jordan & Mendiburu, Andrés Z. & Ávila, Ivonete, 2018. "Modeling of syngas composition obtained from fluidized bed gasifiers using Kuhn–Tucker multipliers," Energy, Elsevier, vol. 152(C), pages 371-382.
    18. Huang, Y.W. & Chen, M.Q. & Li, Q.H. & Xing, W., 2018. "Hydrogen-rich syngas produced from co-gasification of wet sewage sludge and torrefied biomass in self-generated steam agent," Energy, Elsevier, vol. 161(C), pages 202-213.
    19. Bassani, Andrea & Pirola, Carlo & Maggio, Enrico & Pettinau, Alberto & Frau, Caterina & Bozzano, Giulia & Pierucci, Sauro & Ranzi, Eliseo & Manenti, Flavio, 2016. "Acid Gas to Syngas (AG2S™) technology applied to solid fuel gasification: Cutting H2S and CO2 emissions by improving syngas production," Applied Energy, Elsevier, vol. 184(C), pages 1284-1291.
    20. Rahimi, Mohammad Javad & Ghorbani, Bahram & Amidpour, Majid & Hamedi, Mohammad Hossein, 2021. "Configuration optimization of a multi-generation plant based on biomass gasification," Energy, Elsevier, vol. 227(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301444. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.