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.
Waste Manag. 2020 Apr 15;107:276-284. doi: 10.1016/j.wasman.2020.04.020. Epub 2020 Apr 19.
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.
共热解是一种从污水污泥 (SS) 和城市固体废物 (MSW) 中回收能源的有前途的方法。在此过程中汞的排放具有严重的环境风险。为了降低环境影响，在 SS 和 MSW 共热解过程中进行了混合比、加热速率、热解温度和停留时间的正交实验。方差分析用于确定这些因素的影响和协同作用。多元非线性、神经网络、随机森林和支持向量机模型用于基于四个参数模拟 Hg 分布，然后对其进行优化以优化热解炭中的 Hg 固定率。Hg 主要分布在热解气和炭中。方差分析结果表明，混合比对 Hg 分布有重要影响，四个因素之间协同作用很小。进一步的实验表明，SS 质量分数为 87.5%的混合比可以提高炭中 Hg 的固定率。神经网络模型的模拟性能最好，平均相对误差为 8.92%。最佳参数为加热速率为 7°C/min、热解温度为 300°C、停留时间为 10min，在此条件下，热解炭中 Hg 的固定率为 25.68wt%。模拟的 Hg 固定特性与实验结果相关。本研究提供了在 SS 和 MSW 共热解过程中各种条件下 Hg 分布的见解。希望这项工作能够为控制废物处理和利用过程中的 Hg 提供帮助。
