[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124814-en":3,"doc-seo-124814-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124814,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","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 sustainable decomposition of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor, targeting the tar analogue compound (TAC) decomposition process. Key influences—input power, concentration, and residence time—are evaluated through kinetic modeling, reactor performance assessment, and machine learning. An apparent decomposition rate constant is integrated into a novel plug-flow reactor analogy model and simulated in MATLAB via ODE45, while ML accuracy is assessed using RMSE, MSE, and MAE. Results show higher discharge power and longer residence time increase TAC decomposition, reaching 82.9% with ML matching experiments at 83.01% and flagging hotspots at 15% and 25% reactor length.","energies   \nArticle  \nAdvancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor  \nMuhammad Yousaf Arshad 1,2, *, Muhammad Azam Saeed 2, Muhammad Wasim Tahir 2, Halina Pawlak-Kruczek 3,*, Anam Suhail Ahmad 4 and Lukasz Niedzwiecki 3,5  \nCitation: Arshad, M.Y.; Saeed, M.A.; Tahir, M.W.; Pawlak-Kruczek, H.; Ahmad, A.S.; Niedzwiecki, L. 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 2023, 16, 5835 . [https://doi.org/10.3390/en16155835](https://doi.org/10.3390/en16155835)  \nAcademic Editor: Dimitrios Sidiras  \nReceived: 15 July 2023  \nRevised: 29 July 2023  \nAccepted: 4 August 2023  \nPublished: 7 August 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Corporate Sustainability and Digital Chemical Management Division, Interloop Limited, Faisalabad 38000, Pakistan  \n2 Department of Chemical Engineering, University of Engineering and Technology, Lahore 54000, Pakistan; [azam.saeed@uet.edu.pk](azam.saeed@uet.edu.pk) (M.A.S.); [wasim.tahir@uet.edu.pk](wasim.tahir@uet.edu.pk) (M.W.T.)  \n3 Department of Energy Conversion Engineering, Wrocław University of Science and Technology, Wyb.Wyspia ´nskiego 27, 50-370 Wrocław, Poland; [lukasz.niedzwiecki@pwr.edu.pl](lukasz.niedzwiecki@pwr.edu.pl)  \n[4](4 Halliburton Worldwide)[ Halliburton Worldwide](4 Halliburton Worldwide), [3000](3000), [N Sam Houston Parkway E](N Sam Houston Parkway E), [Houston](Houston), [TX 77032-3219](TX 77032-3219), [USA](USA); [anam.ahmed@halliburton.com](anam.ahmed@halliburton.com)  \n5 Energy Research Centre, Centre for Energy and Environmental Technologies, VŠB—Technical University of Ostrava, 17 . Listopadu 2172/15, 708 00 Ostrava, Czech Republic  \n* [Correspondence: yousaf.arshad96@yahoo.com](Correspondence: yousaf.arshad96@yahoo.com) (M.Y.A.); [halina.pawlak@pwr.edu.pl](halina.pawlak@pwr.edu.pl) (H.P.-K.)  \nAbstract: 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.  \nKeywords: NTP reactor; benzene plasma decomposition; kinetic modeling; reactor performance and simulation; machine learning studies  \n1. Introduction  \nBiomass processing is a dual approach for hand","cbCairCBdZxOtCgh","https://ap.wps.com/l/cbCairCBdZxOtCgh","pdf",4586290,1,26,"English","en",105,"# Introduction\n## Biomass processing and producer gas\n## Tar classification and challenges\n## Tar removal methods and limitations\n# Non-thermal plasma for tar decomposition","[{\"question\":\"What reactor and plasma technology are used in the study?\",\"answer\":\"The study uses a dielectric barrier discharge (DBD) reactor with non-thermal plasma (NTP) to drive benzene decomposition reactions.\"},{\"question\":\"Which factors are analyzed for the decomposition process?\",\"answer\":\"Input power, concentration, and residence time are investigated to understand how they affect the decomposition of the tar analogue compound (TAC).\"},{\"question\":\"How do the modeling and machine learning approaches contribute to the results?\",\"answer\":\"A kinetic model with an apparent decomposition rate constant is simulated using MATLAB ODE45, while machine learning models improve prediction accuracy using metrics such as RMSE, MSE, and MAE and identify potential reactor hotspots.\"}]","Advancing Sustainable Decomposition of Biomass Tar Model Compound - 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