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

Author

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  • 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
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    References listed on IDEAS

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