IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v272y2023ics0360544223005285.html
   My bibliography  Save this article

Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation

Author

Listed:
  • Liu, Shanke
  • Yang, Yan
  • Yu, Lijun
  • Cao, Yu
  • Liu, Xinyi
  • Yao, Anqi
  • Cao, Yaping

Abstract

Integrated supercritical water gasification of biomass for power generation (ISSCWBPG) is a promising energy conversion technology. ISSCWBPG is a complex system affected by multiple factors that determine whether the system can self-heat and whether it needs an external heat source. So it is a meaningful job to establish a model that can predict its power generation and heat difference. A process model with 86 types of biomass as raw materials was established, and 4709 samples of power generation indicators were obtained. The artificial neural network (ANN) was constructed based on these samples. Its coefficients of determination (R2) on the test set are above 0.999 in the cases of power generation and heat difference, showing good generalization ability. Self-heating optimization of ISSCWBPG using the ANN model was carried out by taking wheat stalk as an example. Under the determined design flow and different temperatures, the matching operating parameters (DMC, FR) and the corresponding power generation under self-heating conditions were obtained. Compared with the calculation of the thermodynamic equilibrium model of Aspen plus, the errors were all within 10%. This work will provide theoretical guidance for the process design and optimization of the ISSCWBPG.

Suggested Citation

  • Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).
  • Handle: RePEc:eee:energy:v:272:y:2023:i:c:s0360544223005285
    DOI: 10.1016/j.energy.2023.127134
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223005285
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.127134?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. Safarian, Sahar & Ebrahimi Saryazdi, Seyed Mohammad & Unnthorsson, Runar & Richter, Christiaan, 2020. "Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant," Energy, Elsevier, vol. 213(C).
    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. Shahbaz, Muhammad & Taqvi, Syed A. & Minh Loy, Adrian Chun & Inayat, Abrar & Uddin, Fahim & Bokhari, Awais & Naqvi, Salman Raza, 2019. "Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO," Renewable Energy, Elsevier, vol. 132(C), pages 243-254.
    4. Onsree, Thossaporn & Tippayawong, Nakorn, 2021. "Machine learning application to predict yields of solid products from biomass torrefaction," Renewable Energy, Elsevier, vol. 167(C), pages 425-432.
    5. M. M. Sarafraz & Mohammad Reza Safaei & M. Jafarian & Marjan Goodarzi & M. Arjomandi, 2019. "High Quality Syngas Production with Supercritical Biomass Gasification Integrated with a Water–Gas Shift Reactor," Energies, MDPI, vol. 12(13), pages 1-14, July.
    6. Chen, Zhewen & Zhang, Xiaosong & Han, Wei & Gao, Lin & Li, Sheng, 2018. "A power generation system with integrated supercritical water gasification of coal and CO2 capture," Energy, Elsevier, vol. 142(C), pages 723-730.
    7. Li, Jie & Suvarna, Manu & Pan, Lanjia & Zhao, Yingru & Wang, Xiaonan, 2021. "A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification," Applied Energy, Elsevier, vol. 304(C).
    8. Ziyue Ma & Xiaofang Wang & Jinguang Yang & Wei Wang & Wenyang Shao & Xiaowu Jiang, 2021. "Acid Corrosion Analysis in the Initial Condensation Zone of a H 2 O/CO 2 Turbine," Energies, MDPI, vol. 14(11), pages 1-14, June.
    9. Liu, Yuanbin & Hong, Weixiang & Cao, Bingyang, 2019. "Machine learning for predicting thermodynamic properties of pure fluids and their mixtures," Energy, Elsevier, vol. 188(C).
    10. Ozonoh, M. & Oboirien, B.O. & Higginson, A. & Daramola, M.O., 2020. "Performance evaluation of gasification system efficiency using artificial neural network," Renewable Energy, Elsevier, vol. 145(C), pages 2253-2270.
    11. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).
    12. Chen, Zhewen & Gao, Lin & Zhang, Xiaosong & Han, Wei & Li, Sheng, 2018. "High-efficiency power generation system with integrated supercritical water gasification of coal," Energy, Elsevier, vol. 159(C), pages 810-816.
    13. Kargbo, Hannah O. & Zhang, Jie & Phan, Anh N., 2021. "Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network," Applied Energy, Elsevier, vol. 302(C).
    14. Cao, Changqing & Guo, Liejin & Jin, Hui & Cao, Wen & Jia, Yi & Yao, Xiangdong, 2017. "System analysis of pulping process coupled with supercritical water gasification of black liquor for combined hydrogen, heat and power production," Energy, Elsevier, vol. 132(C), pages 238-247.
    Full references (including those not matched with items on IDEAS)

    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. Guo, Shenghui & Meng, Fanrui & Peng, Pai & Xu, Jialing & Jin, Hui & Chen, Yunan & Guo, Liejin, 2022. "Thermodynamic analysis of the superiority of the direct mass transfer design in the supercritical water gasification system," Energy, Elsevier, vol. 244(PA).
    2. Guo, Shenghui & Wang, Yu & Shang, Fei & Yi, Lei & Chen, Yunan & Chen, Bin & Guo, Liejin, 2023. "Thermodynamic analysis of the series system for the supercritical water gasification of coal-water slurry," Energy, Elsevier, vol. 283(C).
    3. Xu, Jialing & Rong, Siqi & Sun, Jingli & Peng, Zhiyong & Jin, Hui & Guo, Liejin & Zhang, Xiang & Zhou, Teng, 2022. "Optimal design of non-isothermal supercritical water gasification reactor: From biomass to hydrogen," Energy, Elsevier, vol. 244(PB).
    4. 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).
    5. Kargbo, Hannah O. & Zhang, Jie & Phan, Anh N., 2021. "Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network," Applied Energy, Elsevier, vol. 302(C).
    6. Benim, Ali Cemal & Pfeiffelmann, Björn & Ocłoń, Paweł & Taler, Jan, 2019. "Computational investigation of a lifted hydrogen flame with LES and FGM," Energy, Elsevier, vol. 173(C), pages 1172-1181.
    7. Owen Sedej & Eric Mbonimpa & Trevor Sleight & Jeremy Slagley, 2022. "Application of Machine Learning to Predict the Performance of an EMIPG Reactor Using Data from Numerical Simulations," Energies, MDPI, vol. 15(7), pages 1-22, March.
    8. Xue, Xiaodong & Liu, Changchun & Han, Wei & Wang, Zefeng & Zhang, Na & Jin, Hongguang & Wang, Xiaodong, 2023. "Proposal and investigation of a high-efficiency coal-fired power generation system enabled by chemical recuperative supercritical water coal gasification," Energy, Elsevier, vol. 267(C).
    9. Lin, Junhao & Sun, Shichang & Cui, Chongwei & Ma, Rui & Fang, Lin & Zhang, Peixin & Quan, Zonggang & Song, Xin & Yan, Jianglong & Luo, Juan, 2019. "Hydrogen-rich bio-gas generation and optimization in relation to heavy metals immobilization during Pd-catalyzed supercritical water gasification of sludge," Energy, Elsevier, vol. 189(C).
    10. Mu, Ruiqi & Liu, Ming & Huang, Yan & Chong, Daotong & Hu, Zhiping & Yan, Junjie, 2024. "Proposal and performance analysis of a novel hydrogen and power cogeneration system with CO2 capture based on coal supercritical water gasification," Energy, Elsevier, vol. 305(C).
    11. Cheng, Shulei & Wu, Yinyin & Chen, Hua & Chen, Jiandong & Song, Malin & Hou, Wenxuan, 2019. "Determinants of changes in electricity generation intensity among different power sectors," Energy Policy, Elsevier, vol. 130(C), pages 389-408.
    12. Ascher, Simon & Sloan, William & Watson, Ian & You, Siming, 2022. "A comprehensive artificial neural network model for gasification process prediction," Applied Energy, Elsevier, vol. 320(C).
    13. Lu, Junhui & Cao, Haishan & Li, JunMing, 2020. "Energy and cost estimates for separating and capturing CO2 from CO2/H2O using condensation coupled with pressure/vacuum swing adsorption," Energy, Elsevier, vol. 202(C).
    14. Zhang, Bowei & Guo, Simao & Jin, Hui, 2022. "Production forecast analysis of BP neural network based on Yimin lignite supercritical water gasification experiment results," Energy, Elsevier, vol. 246(C).
    15. Adnan, Muflih A. & Hidayat, Arif & Hossain, Mohammad M. & Muraza, Oki, 2021. "Transformation of low-rank coal to clean syngas and power via thermochemical route," Energy, Elsevier, vol. 236(C).
    16. Büyükkanber, Kaan & Haykiri-Acma, Hanzade & Yaman, Serdar, 2023. "Calorific value prediction of coal and its optimization by machine learning based on limited samples in a wide range," Energy, Elsevier, vol. 277(C).
    17. Liu, Jia & Hu, Nan & Fan, Li-Wu, 2022. "Optimal design and thermodynamic analysis on the hydrogen oxidation reactor in a combined hydrogen production and power generation system based on coal gasification in supercritical water," Energy, Elsevier, vol. 238(PB).
    18. Wang, Zhen & Mu, Lin & Miao, Hongchao & Shang, Yan & Yin, Hongchao & Dong, Ming, 2023. "An innovative application of machine learning in prediction of the syngas properties of biomass chemical looping gasification based on extra trees regression algorithm," Energy, Elsevier, vol. 275(C).
    19. Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2023. "Prediction of Fuel Properties of Torrefied Biomass Based on Back Propagation Neural Network Hybridized with Genetic Algorithm Optimization," Energies, MDPI, vol. 16(3), pages 1-11, February.
    20. Wang, Yanhong & Cao, Lihua & Li, Xingcan & Wang, Jiaxing & Hu, Pengfei & Li, Bo & Li, Yong, 2020. "A novel thermodynamic method and insight of heat transfer characteristics on economizer for supercritical thermal power plant," Energy, Elsevier, vol. 191(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:272:y:2023:i:c:s0360544223005285. 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.