Modeling and kinetic analysis for co-pyrolysis of sewage sludge and municipal solid waste under multiple factors

被引:0
|
作者
Zhang, Hongnan [1 ,2 ]
Sun, Yunan [1 ]
Tao, Junyu [1 ]
Du, Chengming [1 ]
Yan, Beibei [3 ,4 ,5 ]
Li, Xiangping [3 ,6 ]
Chen, Guanyi [1 ,2 ,3 ]
机构
[1] Tianjin Univ Commerce, Sch Mech Engn, Tianjin 300134, Peoples R China
[2] Tibet Univ, Sch Ecol & Environm, Lhasa 850012, Peoples R China
[3] Tianjin Univ, Sch Environm Sci & Engn, Tianjin 300350, Peoples R China
[4] Tianjin Univ, Tianjin Engn Res Ctr Bio Gas Oil Technol, Tianjin Key Lab Biomass Wastes Utilizat, Tianjin 300072, Peoples R China
[5] Tianjin Univ, Engn Res Ctr Organ Wastes Safe Disposal & Energy U, Tianjin 300072, Peoples R China
[6] Shandong Univ Sci & Technol, Coll Chem & Biol Engn, Qingdao 266590, Shandong, Peoples R China
基金
国家重点研发计划;
关键词
Sewage sludge; Municipal solid waste; Co-pyrolysis; Kinetic analysis; Machine learning models; MICROALGAE; DEGRADATION; BEHAVIOR; PLASTICS; SAWDUST; BIOMASS; MANURE; FUEL;
D O I
10.1007/s10668-024-05626-x
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Under the goal of promoting global carbon reduction to achieve sustainable development, solid waste disposal and utilization plays an important role in achieving carbon neutrality. In order to better realize the resources utilization, the multi-component solid waste collaborative disposal needs to seek suitable combination. The collaborative disposal of sewage sludge (SS) and municipal solid waste (MSW) has a good application prospect. In order to comprehensively understand their co-pyrolysis characteristics, this study conducted a series of thermogravimetric experiments. The synergistic and pyrolysis kinetics were investigated. Three kinetic models were used to calculate the activation energy (E alpha), and obtain the main reaction mechanism model. In order to explore the effect of blending ratio, heating rate, pyrolysis temperature, and residence time on co-pyrolysis product distribution, orthogonal experiments were conducted and the results were discussed by intuitive analysis and variance analysis. Artificial neural network, support vector machine, random forest, and multiple nonlinear regression models were established to precisely simulate the pyrolysis products distribution, thus to optimize pyrolysis conditions. The results indicated that the addition of MSW made pyrolysis process more stable, and the addition of SS reduced the E alpha. The main reaction mechanism followed diffusion model. The pyrolysis product distribution was affected the most by pyrolysis temperature. Pyrolysis temperature and residence time showed a certain interaction. Besides, multiple nonlinear regression model showed the optimal prediction accuracy for the co-pyrolysis process. It is hoped that this study can provide comprehensive fundamental knowledge for the collaborative disposal of SS and MSW and help to achieve carbon neutrality.
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页数:52
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