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采用 BPM 神经网络分析污水污泥模型组分的独立并行热解动力学。

Independent parallel pyrolysis kinetics of model components in sewage sludge analyzed by BPM neural network.

机构信息

Zhongye Changtian International Engineering Co., Ltd., Changsha, 410205, China.

School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China.

出版信息

Environ Sci Pollut Res Int. 2023 Sep;30(43):97486-97497. doi: 10.1007/s11356-023-29184-3. Epub 2023 Aug 18.

DOI:10.1007/s11356-023-29184-3
PMID:37594705
Abstract

Analyzing the kinetic behavior of sewage sludge pyrolysis is essential for the design of efficient reactors to produce biofuel and syngas. To understand the complex pyrolysis process of sewage sludge, we pyrolyzed six model components (i.e., cellulose, hemicellulose, lignin, protein, soluble sugars, and lipid) using a thermogravimetric analyzer. The effects of the heating rate on the pyrolysis process were examined at four different heating rates (5, 15, 25, and 50 °C/min). As temperature increased, the derivative thermogravimetric peaks shifted to higher temperature zones. The temperature ranges of the maximum mass loss rate for cellulose, hemicellulose, lignin, protein, soluble sugars, and lipid were within 326.1-368.0 °C, 288.7-315.5 °C, 375.1-429.4 °C, 291.9-308.0 °C, 251.0-314.1 °C, and 410.8-454.1 °C, respectively. The apparent activation energies of the model components were obtained using non-isothermal kinetic analysis methods (Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose). In addition, a back-propagation artificial neural network with a momentum algorithm (BPM) was developed to predict the relationship between the pyrolysis experiment and the activation value. The best BPM model (BPM5) for predicting the cellulose pyrolysis was identified.

摘要

分析污水污泥热解的动力学行为对于设计高效反应器以生产生物燃料和合成气至关重要。为了了解污水污泥的复杂热解过程,我们使用热重分析仪对六种模型组分(即纤维素、半纤维素、木质素、蛋白质、可溶性糖和脂质)进行了热解。在四个不同的加热速率(5、15、25 和 50°C/min)下,考察了加热速率对热解过程的影响。随着温度的升高,导数热重峰向更高的温度区域移动。纤维素、半纤维素、木质素、蛋白质、可溶性糖和脂质的最大质量损失率的温度范围分别在 326.1-368.0°C、288.7-315.5°C、375.1-429.4°C、291.9-308.0°C、251.0-314.1°C 和 410.8-454.1°C 内。使用非等温热力学分析方法(Flynn-Wall-Ozawa 和 Kissinger-Akahira-Sunose)获得了模型组分的表观活化能。此外,还开发了一种带有动量算法的反向传播人工神经网络(BPM),用于预测热解实验与活化值之间的关系。确定了用于预测纤维素热解的最佳 BPM 模型(BPM5)。

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