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Li Z, Chen S, Liu L, Qian D, Yuan M, Yu J, Chen Z, Yang J, Su X, Hu J, Hou H. Formation mechanism of persistent free radicals during pyrolysis of Fenton-conditioned sewage sludge: Influence of NOM and iron. WATER RESEARCH 2024; 254:121376. [PMID: 38489852 DOI: 10.1016/j.watres.2024.121376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/31/2024] [Accepted: 02/23/2024] [Indexed: 03/17/2024]
Abstract
The present study provided an innovative insight into the formation mechanism of persistent free radicals (PFRs) during the pyrolysis of Fenton-conditioned sludge. Fenton conditioners simultaneously improve the dewatering performance of sewage sludge and catalyze the pyrolysis of sewage sludge for the formation of PFRs. In this process, PFRs with a total number of spins of 9.533×1019 spins/g DS could be generated by pyrolysis of Fenton-conditioned sludge at 400°C. The direct thermal decomposition of natural organic matter (NOM) fractions contributed to the formation of carbon-centered radicals, while the Maillard reaction produced phenols precursors. Additionally, the reaction between aromatic proteins and iron played a crucial role in the formation of phenoxyl or semiquinone-type radicals. Kinetics analysis using discrete distributed activation energy model (DAEM) demonstrated that the average activation energy for pyrolysis was reduced from 178.28 kJ/mol for raw sludge to 164.53 KJ/mol for Fenton conditioned sludge. The reaction factor (fi) indicated that the primary reaction in Fenton-conditioned sludge comprised of 27 parallel first-order reactions, resulting from pyrolysis cleavage of the NOM fractions, the Maillard reaction, and iron catalysis. These findings are significant for understanding the formation process of PFRs from NOM in Fenton-conditioned sludge and provide valuable insight for controlling PFRs formation in practical applications.
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Affiliation(s)
- Zhen Li
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycling, Wuhan 430074, PR China
| | - Sijing Chen
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycling, Wuhan 430074, PR China
| | - Lu Liu
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycling, Wuhan 430074, PR China
| | - Dingkang Qian
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycling, Wuhan 430074, PR China
| | - Mengjiao Yuan
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycling, Wuhan 430074, PR China
| | - Jie Yu
- State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China
| | - Zhuqi Chen
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
| | - Jiakuan Yang
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycling, Wuhan 430074, PR China; State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China
| | - Xintai Su
- School of Environment and Energy, Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou, Guangdong 510006, PR China
| | - Jingping Hu
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycling, Wuhan 430074, PR China; State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China.
| | - Huijie Hou
- School of Environmental Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, PR China; Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycling, Wuhan 430074, PR China.
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Arenas CN, Navarro MV, Martínez JD. Pyrolysis kinetics of biomass wastes using isoconversional methods and the distributed activation energy model. BIORESOURCE TECHNOLOGY 2019; 288:121485. [PMID: 31136890 DOI: 10.1016/j.biortech.2019.121485] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 05/10/2019] [Accepted: 05/13/2019] [Indexed: 05/22/2023]
Abstract
In this work, a thermogravimetric analyser was used to assess the pyrolysis kinetics of pineapple, orange and mango peel wastes and agro-industrial by-products, rice husk and pine wood. Five isoconversional methods (KAS, FWO, Starink, Vyazovkin and Friedman) and one model-fitting method (DAEM) accurately fitted the experimental data at three heating rates (5, 10 and 20 °C/min) between 10% and 90% conversion. These methods agree with the trends shown by the activation energy (Ea) distribution calculated, with fluctuations between 150 and 550 kJ/mol. The fluctuations of Ea in the whole range of conversion, in addition to a higher number of relevant reactions obtained by DAEM for fruit peel samples compared to agro-industrial samples, are associated with a higher extractive content in the peels. Kinetic parameters fitted by DAEM were successfully verified at the highest heating rate studied.
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Affiliation(s)
- Cindy Natalia Arenas
- Grupo de Investigaciones Ambientales (GIA), Universidad Pontificia Bolivariana (UPB), Circular 1ra N° 74-50, Medellín, Colombia
| | | | - Juan Daniel Martínez
- Grupo de Investigaciones Ambientales (GIA), Universidad Pontificia Bolivariana (UPB), Circular 1ra N° 74-50, Medellín, Colombia.
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Xu D, Chai M, Dong Z, Rahman MM, Yu X, Cai J. Kinetic compensation effect in logistic distributed activation energy model for lignocellulosic biomass pyrolysis. BIORESOURCE TECHNOLOGY 2018; 265:139-145. [PMID: 29890438 DOI: 10.1016/j.biortech.2018.05.092] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 05/22/2018] [Accepted: 05/24/2018] [Indexed: 06/08/2023]
Abstract
The kinetic compensation effect in the logistic distributed activation energy model (DAEM) for lignocellulosic biomass pyrolysis was investigated. The sum of square error (SSE) surface tool was used to analyze two theoretically simulated logistic DAEM processes for cellulose and xylan pyrolysis. The logistic DAEM coupled with the pattern search method for parameter estimation was used to analyze the experimental data of cellulose pyrolysis. The results showed that many parameter sets of the logistic DAEM could fit the data at different heating rates very well for both simulated and experimental processes, and a perfect linear relationship between the logarithm of the frequency factor and the mean value of the activation energy distribution was found. The parameters of the logistic DAEM can be estimated by coupling the optimization method and isoconversional kinetic methods. The results would be helpful for chemical kinetic analysis using DAEM.
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Affiliation(s)
- Di Xu
- Biomass Energy Engineering Research Center, Key Laboratory of Urban Agriculture (South) Ministry of Agriculture, School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People's Republic of China
| | - Meiyun Chai
- Biomass Energy Engineering Research Center, Key Laboratory of Urban Agriculture (South) Ministry of Agriculture, School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People's Republic of China
| | - Zhujun Dong
- Biomass Energy Engineering Research Center, Key Laboratory of Urban Agriculture (South) Ministry of Agriculture, School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People's Republic of China
| | - Md Maksudur Rahman
- Biomass Energy Engineering Research Center, Key Laboratory of Urban Agriculture (South) Ministry of Agriculture, School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People's Republic of China
| | - Xi Yu
- European Bioenergy Research Institute, Aston University, Aston Triangle, Birmingham B4 7ET, United Kingdom
| | - Junmeng Cai
- Biomass Energy Engineering Research Center, Key Laboratory of Urban Agriculture (South) Ministry of Agriculture, School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People's Republic of China; CAS Key Laboratory of Renewable Energy, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China. http://biofuels.sjtu.edu.cn
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Lin Y, Chen Z, Dai M, Fang S, Liao Y, Yu Z, Ma X. Co-pyrolysis kinetics of sewage sludge and bagasse using multiple normal distributed activation energy model (M-DAEM). BIORESOURCE TECHNOLOGY 2018; 259:173-180. [PMID: 29550731 DOI: 10.1016/j.biortech.2018.03.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 03/05/2018] [Accepted: 03/06/2018] [Indexed: 05/26/2023]
Abstract
In this study, the kinetic models of bagasse, sewage sludge and their mixture were established by the multiple normal distributed activation energy model. Blending with sewage sludge, the initial temperature declined from 437 K to 418 K. The pyrolytic species could be divided into five categories, including analogous hemicelluloses I, hemicelluloses II, cellulose, lignin and bio-char. In these species, the average activation energies and the deviations situated at reasonable ranges of 166.4673-323.7261 kJ/mol and 0.1063-35.2973 kJ/mol, respectively, which were conformed to the references. The kinetic models were well matched to experimental data, and the R2 were greater than 99.999%y. In the local sensitivity analysis, the distributed average activation energy had stronger effect on the robustness than other kinetic parameters. And the content of pyrolytic species determined which series of kinetic parameters were more important.
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Affiliation(s)
- Yan Lin
- School of Electric Power, South China University of Technology, 510640 Guangzhou, China; Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, 510640 Guangzhou, China
| | - Zhihao Chen
- School of Electric Power, South China University of Technology, 510640 Guangzhou, China; Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, 510640 Guangzhou, China
| | - Minquan Dai
- School of Electric Power, South China University of Technology, 510640 Guangzhou, China; Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, 510640 Guangzhou, China
| | - Shiwen Fang
- School of Electric Power, South China University of Technology, 510640 Guangzhou, China; Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, 510640 Guangzhou, China
| | - Yanfen Liao
- School of Electric Power, South China University of Technology, 510640 Guangzhou, China; Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, 510640 Guangzhou, China.
| | - Zhaosheng Yu
- School of Electric Power, South China University of Technology, 510640 Guangzhou, China; Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, 510640 Guangzhou, China
| | - Xiaoqian Ma
- School of Electric Power, South China University of Technology, 510640 Guangzhou, China; Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, 510640 Guangzhou, China
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