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<p>Chemical looping gasification (CLG) can inherently split the traditional gasification into two processes to produce high-quality syngas, avoiding the N<sub>2</sub> dilution for syngas. CLG of solid wastes has gained attention for its satisfactory performance with waste valorization. The chemical looping co-gasification (CLCG) performances of rice husk and textile wastes are investigated, which are typical solid wastes used in industry as alternative fuels. A thermodynamic process model of CLCG is established, and the effects of different operating parameters are quantitatively analyzed. Furthermore, a multi-input and multi-output back propagation neural network model (BPNNM) is trained using process model results for the performance prediction. Key findings reveal that increasing equivalence ratios of oxygen carrier and steam (<i>α</i><sub>OC/F</sub> and <i>α</i><sub>steam/F</sub>) significantly affect gasification efficiency. Specifically, increasing <i>α</i><sub>OC/F</sub> to 0.5 decreases gasification efficiency to 60.82%. Conversely, increasing <i>α</i><sub>steam/F</sub> from 0.1 to 0.5 leads to a slight decrease in gasification efficiency from 85.95 to 84.80%, while simultaneously increasing hydrogen concentration in syngas from 39.91 to 46.28%. Elevating the gasification temperature from 650 to 850&#xa0;°C can raise the <i>η</i> from 81.52% up to 86.00%. The blending ratio of the rice husk and textile waste also dramatically affects gasification efficiency, with efficiency decreasing from 92.27 to 74.41% as the blending ratio <i>R</i><sub>r</sub> increases from 0 to 1. The tests of random conditions demonstrate that the trained BPNNM can be a very accurate tool for the prediction of syngas compositions and gasification indicators in CLCG.</p>

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Process simulation and BPNNM prediction for chemical looping co-gasification of rice husk and textile wastes as cement alternative fuels

  • 期刊论文

  • 发表日期：

    2025年4月23日

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  • Congxi Tao,
  • Hao Wang,
  • Qingmei Li,
  • Minghai He,
  • Qian Liang,
  • Xudong Wang

摘要

Chemical looping gasification (CLG) can inherently split the traditional gasification into two processes to produce high-quality syngas, avoiding the N2 dilution for syngas. CLG of solid wastes has gained attention for its satisfactory performance with waste valorization. The chemical looping co-gasification (CLCG) performances of rice husk and textile wastes are investigated, which are typical solid wastes used in industry as alternative fuels. A thermodynamic process model of CLCG is established, and the effects of different operating parameters are quantitatively analyzed. Furthermore, a multi-input and multi-output back propagation neural network model (BPNNM) is trained using process model results for the performance prediction. Key findings reveal that increasing equivalence ratios of oxygen carrier and steam (αOC/F and αsteam/F) significantly affect gasification efficiency. Specifically, increasing αOC/F to 0.5 decreases gasification efficiency to 60.82%. Conversely, increasing αsteam/F from 0.1 to 0.5 leads to a slight decrease in gasification efficiency from 85.95 to 84.80%, while simultaneously increasing hydrogen concentration in syngas from 39.91 to 46.28%. Elevating the gasification temperature from 650 to 850 °C can raise the η from 81.52% up to 86.00%. The blending ratio of the rice husk and textile waste also dramatically affects gasification efficiency, with efficiency decreasing from 92.27 to 74.41% as the blending ratio Rr increases from 0 to 1. The tests of random conditions demonstrate that the trained BPNNM can be a very accurate tool for the prediction of syngas compositions and gasification indicators in CLCG.