Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor
This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through ki...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/15/5835 |
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| author | Muhammad Yousaf Arshad Muhammad Azam Saeed Muhammad Wasim Tahir Halina Pawlak-Kruczek Anam Suhail Ahmad Lukasz Niedzwiecki |
| author_facet | Muhammad Yousaf Arshad Muhammad Azam Saeed Muhammad Wasim Tahir Halina Pawlak-Kruczek Anam Suhail Ahmad Lukasz Niedzwiecki |
| author_sort | Muhammad Yousaf Arshad |
| collection | DOAJ |
| description | This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance assessment, and machine learning techniques. To further enhance the understanding and modeling of the decomposition process, the researchers determine the apparent decomposition rate constant, which is incorporated into a kinetic model using a novel theoretical plug flow reactor analogy model. The resulting reactor model is simulated using the ODE45 solver in MATLAB, with advanced machine learning algorithms and performance metrics such as RMSE, MSE, and MAE employed to improve accuracy. The analysis reveals that higher input discharge power and longer residence time result in increased tar analogue compound (TAC) decomposition. The results indicate that higher input discharge power leads to a significant improvement in the TAC decomposition rate, reaching 82.9%. The machine learning model achieved very good agreement with the experiments, showing a decomposition rate of 83.01%. The model flagged potential hotspots at 15% and 25% of the reactor’s length, which is important in terms of engineering design of scaled-up reactors. |
| first_indexed | 2024-03-11T00:27:54Z |
| format | Article |
| id | doaj.art-f2fbf2d17f0a4ec9bd17814f2a0bea59 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T00:27:54Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f2fbf2d17f0a4ec9bd17814f2a0bea592023-11-18T22:53:26ZengMDPI AGEnergies1996-10732023-08-011615583510.3390/en16155835Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge ReactorMuhammad Yousaf Arshad0Muhammad Azam Saeed1Muhammad Wasim Tahir2Halina Pawlak-Kruczek3Anam Suhail Ahmad4Lukasz Niedzwiecki5Corporate Sustainability and Digital Chemical Management Division, Interloop Limited, Faisalabad 38000, PakistanDepartment of Chemical Engineering, University of Engineering and Technology, Lahore 54000, PakistanDepartment of Chemical Engineering, University of Engineering and Technology, Lahore 54000, PakistanDepartment of Energy Conversion Engineering, Wrocław University of Science and Technology, Wyb.Wyspiańskiego 27, 50-370 Wrocław, PolandHalliburton Worldwide, 3000, N Sam Houston Parkway E, Houston, TX 77032-3219, USADepartment of Energy Conversion Engineering, Wrocław University of Science and Technology, Wyb.Wyspiańskiego 27, 50-370 Wrocław, PolandThis study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance assessment, and machine learning techniques. To further enhance the understanding and modeling of the decomposition process, the researchers determine the apparent decomposition rate constant, which is incorporated into a kinetic model using a novel theoretical plug flow reactor analogy model. The resulting reactor model is simulated using the ODE45 solver in MATLAB, with advanced machine learning algorithms and performance metrics such as RMSE, MSE, and MAE employed to improve accuracy. The analysis reveals that higher input discharge power and longer residence time result in increased tar analogue compound (TAC) decomposition. The results indicate that higher input discharge power leads to a significant improvement in the TAC decomposition rate, reaching 82.9%. The machine learning model achieved very good agreement with the experiments, showing a decomposition rate of 83.01%. The model flagged potential hotspots at 15% and 25% of the reactor’s length, which is important in terms of engineering design of scaled-up reactors.https://www.mdpi.com/1996-1073/16/15/5835NTP reactorbenzene plasma decompositionkinetic modelingreactor performance and simulationmachine learning studies |
| spellingShingle | Muhammad Yousaf Arshad Muhammad Azam Saeed Muhammad Wasim Tahir Halina Pawlak-Kruczek Anam Suhail Ahmad Lukasz Niedzwiecki Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor Energies NTP reactor benzene plasma decomposition kinetic modeling reactor performance and simulation machine learning studies |
| title | Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor |
| title_full | Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor |
| title_fullStr | Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor |
| title_full_unstemmed | Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor |
| title_short | Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor |
| title_sort | advancing sustainable decomposition of biomass tar model compound machine learning kinetic modeling and experimental investigation in a non thermal plasma dielectric barrier discharge reactor |
| topic | NTP reactor benzene plasma decomposition kinetic modeling reactor performance and simulation machine learning studies |
| url | https://www.mdpi.com/1996-1073/16/15/5835 |
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