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Distribution of Hg during sewage sludge and municipal solid waste Co-pyrolysis: Influence of multiple factors

机译:污泥污泥和城市固体废弃物共热分解期间HG的分布:多因素的影响

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摘要

Co-pyrolysis is a promising approach to recover energy from sewage sludge (SS) and municipal solid waste (MSW). Hg emission during this process has serious environmental risks. To reduce the environmental impact, orthogonal experiments on the blending ratio, heating rate, pyrolysis temperature, and residence time were conducted during SS and MSW co-pyrolysis. Variance analysis was used to determine the influence and synergetic effects of these factors. Multivariate nonlinear, neural network, random forest, and support vector machine models were used to simulate the Hg distribution based on four parameters, which were later optimized to optimize the Hg fixing ratio in pyrolysis char. The Hg was mainly distributed in the pyrolysis gas and char. The variance analysis results indicate that the blending ratio is the key factor influencing Hg distribution, and there is little synergetic effect among the four factors. Further experiments showed that a blending ratio of 87.5 SS wt% could enhance Hg fixation in char. The neural network model shows the best simulation performance with a mean relative error of 8.92%. The optimal parameters are a heating rate of 7 °C/min, pyrolysis temperature of 300 °C, and residence time of 10 min, resulting in a Hg fixing ratio of 25.68 wt% in pyrolysis char. The simulated Hg fixation characteristics correlate with the experimental results. This study provides insights into Hg distribution under various conditions during co-pyrolysis of SS and MSW. It is hoped that this work can contribute to the control of Hg during the waste treatment and utilization process.
机译:共热分解是一种有望的方法来从污水污泥(SS)和市政固体废物(MSW)中恢复能量。此过程中的HG排放具有严重的环境风险。为了减少环境影响,在SS和MSW共热分解期间进行了对混合比的正交实验,加热速率,热解温和停留时间。方差分析用于确定这些因素的影响和协同效应。多变量非线性,神经网络,随机森林和支持向量机模型用于模拟基于四个参数的HG分布,后来优化,以优化热解焦炭中的HG固定比。 Hg主要分布在热解气体和炭。方差分析结果表明,混合比是影响HG分布的关键因素,并且四种因素之间几乎没有协同效果。进一步的实验表明,混合比为87.5sswt%可以增强炭中的Hg固定。神经网络模型显示出最佳的模拟性能,平均相对误差为8.92%。最佳参数是7℃/ min的加热速率,热解温度为300℃,并且停留时间为10分钟,导致热解焦炭中的Hg定影比为25.68wt%。模拟的Hg固定特性与实验结果相关。本研究在SS和MSW共热解析的各种条件下提供了对HG分布的见解。希望这项工作能够在废物处理和利用过程中控制HG的控制。

著录项

  • 来源
    《Waste Management》 |2020年第4期|276-284|共9页
  • 作者单位

    School of Environmental Science and Engineering/State Key Lab of Engines Tianjin University Tianjin 300072 China;

    School of Environmental Science and Engineering/State Key Lab of Engines Tianjin University Tianjin 300072 China;

    School of Science Tibet University No. 36 Jiangsu Street Lhasa 850012 Tibet Autonomous Region China Tianjin Engineering Center of Biomass-derived Gas/Oil Technology Tianjin 300072 China;

    School of Environmental Science and Engineering/State Key Lab of Engines Tianjin University Tianjin 300072 China Tianjin Key Lab of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil Technology Tianjin 300072 China;

    School of Environmental Science and Engineering/State Key Lab of Engines Tianjin University Tianjin 300072 China Tianjin Key Lab of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil Technology Tianjin 300072 China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sewage sludge; Municipal solid waste; Mercury; Co-pyrolysis; Variance analysis; Neural network;

    机译:污水污泥;城市生活垃圾;汞;共热分解;方差分析;神经网络;

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