IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1079-d1597741.html
   My bibliography  Save this article

A Data Reconciliation-Based Method for Performance Estimation of Entrained-Flow Pulverized Coal Gasification

Author

Listed:
  • Yan Zhang

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Beijing MWAY Technology Co., Ltd., Beijing 100176, China)

  • Kai Yue

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Beijing Key Laboratory for Energy Saving and Emission Reduction of Metallurgical Industry, Beijing 100083, China)

  • Chang Yuan

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Jiahao Xiang

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

Accurate performance estimation of the entrained-flow pulverized coal gasification unit is essential for production scheduling and process optimization, but these are often hindered by inaccurate or insufficient measurements in the industrial system. This paper proposes a data reconciliation-based method to address this challenge. The thermodynamic equilibrium model is employed as constraints of the gasification and quench processes, and the Particle Swarm Optimization (PSO) algorithm is applied for parameter estimation. Measured data under stable and variable operating conditions are reconciled, detecting and eliminating a 12% error in syngas flow rate at the scrubber outlet, thereby improving gasification performance accuracy. Two characteristic models concerning carbon conversion rate and the flow rate of reacted quench water are derived from the reconciled results. By combining these models with thermodynamic equilibrium models, the modified R 2 of offline predicted syngas flow rate exceeds 0.92, and those of syngas compositions reach 0.72–0.85. Additionally, an Artificial Neural Network (ANN) model, trained on reconciled and predicted data, is proposed for real-time performance estimation. The ANN model calculates performance metrics within 10 s and achieves R 2 values above 0.95 for most parameters. This method can be integrated into control systems and serves as a valuable tool for gasification process monitoring and optimization.

Suggested Citation

  • Yan Zhang & Kai Yue & Chang Yuan & Jiahao Xiang, 2025. "A Data Reconciliation-Based Method for Performance Estimation of Entrained-Flow Pulverized Coal Gasification," Energies, MDPI, vol. 18(5), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1079-:d:1597741
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1079/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1079/
    Download Restriction: no
    ---><---

    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. Kim, Mukyeong & Ye, Insoo & Jo, Hyunbin & Ryu, Changkook & Kim, Bongkeun & Lee, Jeongsoo, 2020. "New reduced-order model optimized for online dynamic simulation of a Shell coal gasifier," Applied Energy, Elsevier, vol. 263(C).
    3. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
    4. 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).
    5. Yu, Jianxi & Han, Wenquan & Chen, Kang & Liu, Pei & Li, Zheng, 2022. "Gross error detection in steam turbine measurements based on data reconciliation of inequality constraints," Energy, Elsevier, vol. 253(C).
    6. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Data reconciliation and gross error detection for operational data in power plants," Energy, Elsevier, vol. 75(C), pages 14-23.
    7. Ayub, Yousaf & Hu, Yusha & Ren, Jingzheng, 2023. "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models," Renewable Energy, Elsevier, vol. 215(C).
    8. Wang, Kangcheng & Zhang, Jie & Shang, Chao & Huang, Dexian, 2021. "Operation optimization of Shell coal gasification process based on convolutional neural network models," Applied Energy, Elsevier, vol. 292(C).
    9. 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).
    10. Chen, Chih-Jung & Hung, Chen-I. & Chen, Wei-Hsin, 2012. "Numerical investigation on performance of coal gasification under various injection patterns in an entrained flow gasifier," Applied Energy, Elsevier, vol. 100(C), pages 218-228.
    11. Loyola-Fuentes, José & Smith, Robin, 2019. "Data reconciliation and gross error detection in crude oil pre-heat trains undergoing shell-side and tube-side fouling deposition," Energy, Elsevier, vol. 183(C), pages 368-384.
    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. Sylwia Polesek-Karczewska & Paulina Hercel & Behrouz Adibimanesh & Izabela Wardach-Świȩcicka, 2024. "Towards Sustainable Biomass Conversion Technologies: A Review of Mathematical Modeling Approaches," Sustainability, MDPI, vol. 16(19), pages 1-43, October.
    2. Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Chen, Yong, 2024. "Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning," Energy, Elsevier, vol. 290(C).
    3. Mu, Lin & Wang, Zhen & Sun, Meng & Shang, Yan & Pu, Hang & Dong, Ming, 2024. "Machine learning model with a novel self–adjustment method: A powerful tool for predicting biomass ash fusibility and enhancing its potential applications," Renewable Energy, Elsevier, vol. 237(PA).
    4. Olca, Kadriye Deniz & Yücel, Özgün, 2024. "Unveiling the potential of operating time in improving machine learning models’ performance for waste biomass gasification systems," Renewable Energy, Elsevier, vol. 237(PA).
    5. Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Li, Jiadong & Chen, Yong, 2024. "Biomass hydrothermal gasification characteristics study: based on deep learning for data generation and screening strategies," Energy, Elsevier, vol. 312(C).
    6. Ayub, Yousaf & Ren, Jingzheng & He, Chang, 2024. "Unlocking waste potential: A neural network approach to forecasting sustainable acetaldehyde production from ethanol upcycling in biomass waste gasification," Energy, Elsevier, vol. 299(C).
    7. Yu, Jianxi & Han, Wenquan & Chen, Kang & Liu, Pei & Li, Zheng, 2022. "Gross error detection in steam turbine measurements based on data reconciliation of inequality constraints," Energy, Elsevier, vol. 253(C).
    8. Xu, Shisen & Ren, Yongqiang & Wang, Baomin & Xu, Yue & Chen, Liang & Wang, Xiaolong & Xiao, Tiancun, 2014. "Development of a novel 2-stage entrained flow coal dry powder gasifier," Applied Energy, Elsevier, vol. 113(C), pages 318-323.
    9. Lv, Xuefei & Lv, Ying & Zhu, Yiping, 2023. "Multi-variable study and MOPSO-based multi-objective optimization of a novel cogeneration plant using biomass fuel and geothermal energy: A complementary hybrid design," Energy, Elsevier, vol. 270(C).
    10. Ayub, Yousaf & Ren, Jingzheng & Shi, Tao & Shen, Weifeng & He, Chang, 2023. "Poultry litter valorization: Development and optimization of an electro-chemical and thermal tri-generation process using an extreme gradient boosting algorithm," Energy, Elsevier, vol. 263(PC).
    11. Mohd Zeeshan & Rohan R. Pande & Purnanand V. Bhale, 2024. "A modeling study for the gasification of refuse-derived fuel as an alternative to waste disposal," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 23985-24008, September.
    12. Kim, Ryang-Gyoon & Hwang, Chan-Won & Jeon, Chung-Hwan, 2014. "Kinetics of coal char gasification with CO2: Impact of internal/external diffusion at high temperature and elevated pressure," Applied Energy, Elsevier, vol. 129(C), pages 299-307.
    13. Chen, Wei-Hsin & Chen, Chih-Jung & Hung, Chen-I & Shen, Cheng-Hsien & Hsu, Heng-Wen, 2013. "A comparison of gasification phenomena among raw biomass, torrefied biomass and coal in an entrained-flow reactor," Applied Energy, Elsevier, vol. 112(C), pages 421-430.
    14. Santhappan, Joseph Sekhar & Boddu, Muralikrishna & Gopinath, Arun S. & Mathimani, Thangavel, 2024. "Analysis of 27 supervised machine learning models for the co-gasification assessment of peanut shell and spent tea residue in an open-core downdraft gasifier," Renewable Energy, Elsevier, vol. 235(C).
    15. Szega, Marcin, 2018. "Issues of an optimization of measurements location in redundant measurements systems of an energy conversion process – A case study," Energy, Elsevier, vol. 165(PA), pages 1034-1047.
    16. Li, Fenghai & Li, Zhenzhu & Huang, Jiejie & Fang, Yitian, 2014. "Understanding mineral behaviors during anthracite fluidized-bed gasification based on slag characteristics," Applied Energy, Elsevier, vol. 131(C), pages 279-287.
    17. Yu, Jianxi & Liu, Pei & Li, Zheng, 2021. "Data reconciliation of the thermal system of a double reheat power plant for thermal calculation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    18. 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).
    19. Loyola-Fuentes, José & Smith, Robin, 2019. "Data reconciliation and gross error detection in crude oil pre-heat trains undergoing shell-side and tube-side fouling deposition," Energy, Elsevier, vol. 183(C), pages 368-384.
    20. Klemeš, Jiří Jaromír & Wang, Qiu-Wang & Varbanov, Petar Sabev & Zeng, Min & Chin, Hon Huin & Lal, Nathan Sanjay & Li, Nian-Qi & Wang, Bohong & Wang, Xue-Chao & Walmsley, Timothy Gordon, 2020. "Heat transfer enhancement, intensification and optimisation in heat exchanger network retrofit and operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(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:gam:jeners:v:18:y:2025:i:5:p:1079-:d:1597741. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.