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Dynamic modeling, systematic analysis, and operation optimization for shell entrained-flow heavy residue gasifier

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  • Cao, Zhikai
  • Wu, Qi
  • Zhou, Hua
  • Chen, Pingping
  • You, Fengqi

Abstract

Simulation of Shell entrained-flow, heavy-residue gasifiers (SHERG) are challenging because of diverse homogenous and heterogeneous reactions involved in the gasification process and the complicated flow-field distribution. An integrated continuous stirred-tank reactor (CSTR) and plug flow reactor (PFR) model is proposed to describe the computationally efficient simulation of SHERG. The CSTR zone is determined by the results of computational flow dynamics. The proposed model is validated by comparing simulation results with industrial data. Meanwhile, a serial-CSTR model is applied to model the gasifier. The simulation results show that the accuracy of proposed model is higher than that of the serial-CSTR model. Moreover, comparison of the central processing unit time for solving the model reveals that the computational performance of the proposed model is superior than that of the serial-CSTR model. On the basis of the proposed model, different ratios of O2/oil and steam/oil for optimization of the gasifier operation are investigated.

Suggested Citation

  • Cao, Zhikai & Wu, Qi & Zhou, Hua & Chen, Pingping & You, Fengqi, 2020. "Dynamic modeling, systematic analysis, and operation optimization for shell entrained-flow heavy residue gasifier," Energy, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220303273
    DOI: 10.1016/j.energy.2020.117220
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    References listed on IDEAS

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    1. Wang, Han & Chaffart, Donovan & Ricardez-Sandoval, Luis A., 2019. "Modelling and optimization of a pilot-scale entrained-flow gasifier using artificial neural networks," Energy, Elsevier, vol. 188(C).
    2. Zhou, Hua & Xie, Taili & You, Fengqi, 2018. "On-line simulation and optimization of a commercial-scale shell entrained-flow gasifier using a novel dynamic reduced order model," Energy, Elsevier, vol. 149(C), pages 516-534.
    3. Lee, Hyeon-Hui & Lee, Jae-Chul & Joo, Yong-Jin & Oh, Min & Lee, Chang-Ha, 2014. "Dynamic modeling of Shell entrained flow gasifier in an integrated gasification combined cycle process," Applied Energy, Elsevier, vol. 131(C), pages 425-440.
    4. Cao, Zhikai & Li, Tao & Zhang, Quancong & Zhou, Hua & Song, Can & You, Fengqi, 2018. "Systems modeling, simulation and analysis for robust operations and improved design of entrained-flow pulverized coal gasifiers," Energy, Elsevier, vol. 148(C), pages 941-964.
    5. Ashizawa, Masami & Hara, Saburo & Kidoguchi, Kazuhiro & Inumaru, Jun, 2005. "Gasification characteristics of extra-heavy oil in a research-scale gasifier," Energy, Elsevier, vol. 30(11), pages 2194-2205.
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    Cited by:

    1. Lu, Yue & Li, Zhengqi & Jiang, Guangfei & Huang, Chunchao & Chen, Zhichao, 2024. "Study on mixing performance of atmospheric entrained flow gasification burner using fine ash as feedstock," Energy, Elsevier, vol. 292(C).
    2. Seferlis, Panos & Varbanov, Petar Sabev & Papadopoulos, Athanasios I. & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2021. "Sustainable design, integration, and operation for energy high-performance process systems," Energy, Elsevier, vol. 224(C).
    3. Slavomír Podolský & Miroslav Variny & Tomáš Kurák, 2023. "Carbon-Energy Impact Analysis of Heavy Residue Gasification Plant Integration into Oil Refinery," Resources, MDPI, vol. 12(6), pages 1-23, May.
    4. 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).

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