Determination of Pyrolysis and Kinetics Characteristics of Chicken Manure Using Thermogravimetric Analysis Coupled with Particle Swarm Optimization
The valorization of chicken manure via pyrolysis can give biowaste a second life to generate value and contribute to the circular economy. In the present study, the thermal degradation and pyrolysis characteristics of chicken manure pyrolysis were investigated via thermogravimetric analyses (TGA) co...
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author | Jie Gu Cheng Tung Chong Guo Ren Mong Jo-Han Ng William Woei Fong Chong |
author_facet | Jie Gu Cheng Tung Chong Guo Ren Mong Jo-Han Ng William Woei Fong Chong |
author_sort | Jie Gu |
collection | DOAJ |
description | The valorization of chicken manure via pyrolysis can give biowaste a second life to generate value and contribute to the circular economy. In the present study, the thermal degradation and pyrolysis characteristics of chicken manure pyrolysis were investigated via thermogravimetric analyses (TGA) coupled with optimization methods. Thermogravimetric data were obtained for the samples at five heating rates of 5, 10, 20, 30 and 50 °C/min over a range of temperature under inert conditions. The manure devolatilization process was initiated at between 328 and 367 °C to overcome the global activation energy barrier. The determined activation energy of the manure via Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), Friedman and Kissinger methods was in the range of 167.5–213.9 kJ/mol. By using the particle swarm optimization (PSO) method, the pyrolytic kinetic parameters of the individual component present in the manure were calculated, in which the activation energy for cellulose (227.8 kJ/mol) was found to be higher than that of hemicellulose (119 kJ/mol) and lignin (134.3 kJ/mol). Based on intrinsic transition-state theory, the pre-exponential factor and activation energy of the manure can be correlated through a linear equation ln <i>A<sub>α</sub></i> = 0.2006 <i>E<sub>α</sub></i> − 1.2847. The devolatilization characteristics of the chicken manure were elucidated via the optimization process, paving the way for the design of thermochemical conversion reactors and processes. |
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issn | 1996-1073 |
language | English |
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spelling | doaj.art-545383433efb43f1b6b0dd8159a94d322023-11-16T20:19:40ZengMDPI AGEnergies1996-10732023-02-01164191910.3390/en16041919Determination of Pyrolysis and Kinetics Characteristics of Chicken Manure Using Thermogravimetric Analysis Coupled with Particle Swarm OptimizationJie Gu0Cheng Tung Chong1Guo Ren Mong2Jo-Han Ng3William Woei Fong Chong4China-UK Low Carbon College, Shanghai Jiao Tong University, Lingang, Shanghai 201306, ChinaChina-UK Low Carbon College, Shanghai Jiao Tong University, Lingang, Shanghai 201306, ChinaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang 43900, Selangor, MalaysiaFaculty of Engineering and Physical Sciences, University of Southampton Malaysia, Iskandar Puteri 79100, Johor, MalaysiaAutomotive Development Centre (ADC), Institute for Vehicle System & Engineering (IVeSE), Universiti Teknologi Malaysia, Skudai 81310, Johor, MalaysiaThe valorization of chicken manure via pyrolysis can give biowaste a second life to generate value and contribute to the circular economy. In the present study, the thermal degradation and pyrolysis characteristics of chicken manure pyrolysis were investigated via thermogravimetric analyses (TGA) coupled with optimization methods. Thermogravimetric data were obtained for the samples at five heating rates of 5, 10, 20, 30 and 50 °C/min over a range of temperature under inert conditions. The manure devolatilization process was initiated at between 328 and 367 °C to overcome the global activation energy barrier. The determined activation energy of the manure via Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), Friedman and Kissinger methods was in the range of 167.5–213.9 kJ/mol. By using the particle swarm optimization (PSO) method, the pyrolytic kinetic parameters of the individual component present in the manure were calculated, in which the activation energy for cellulose (227.8 kJ/mol) was found to be higher than that of hemicellulose (119 kJ/mol) and lignin (134.3 kJ/mol). Based on intrinsic transition-state theory, the pre-exponential factor and activation energy of the manure can be correlated through a linear equation ln <i>A<sub>α</sub></i> = 0.2006 <i>E<sub>α</sub></i> − 1.2847. The devolatilization characteristics of the chicken manure were elucidated via the optimization process, paving the way for the design of thermochemical conversion reactors and processes.https://www.mdpi.com/1996-1073/16/4/1919pyrolysischicken manurethermogravimetric analysiskinetic analysisparticle swarm optimization |
spellingShingle | Jie Gu Cheng Tung Chong Guo Ren Mong Jo-Han Ng William Woei Fong Chong Determination of Pyrolysis and Kinetics Characteristics of Chicken Manure Using Thermogravimetric Analysis Coupled with Particle Swarm Optimization Energies pyrolysis chicken manure thermogravimetric analysis kinetic analysis particle swarm optimization |
title | Determination of Pyrolysis and Kinetics Characteristics of Chicken Manure Using Thermogravimetric Analysis Coupled with Particle Swarm Optimization |
title_full | Determination of Pyrolysis and Kinetics Characteristics of Chicken Manure Using Thermogravimetric Analysis Coupled with Particle Swarm Optimization |
title_fullStr | Determination of Pyrolysis and Kinetics Characteristics of Chicken Manure Using Thermogravimetric Analysis Coupled with Particle Swarm Optimization |
title_full_unstemmed | Determination of Pyrolysis and Kinetics Characteristics of Chicken Manure Using Thermogravimetric Analysis Coupled with Particle Swarm Optimization |
title_short | Determination of Pyrolysis and Kinetics Characteristics of Chicken Manure Using Thermogravimetric Analysis Coupled with Particle Swarm Optimization |
title_sort | determination of pyrolysis and kinetics characteristics of chicken manure using thermogravimetric analysis coupled with particle swarm optimization |
topic | pyrolysis chicken manure thermogravimetric analysis kinetic analysis particle swarm optimization |
url | https://www.mdpi.com/1996-1073/16/4/1919 |
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