IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v317y2025ics0360544225002907.html

A novel waste-to-energy conversion plant based on catalytic pyrolytic conditions: Modeling and optimization using supervised machine learning and desirability-driven methodologies

Author

Listed:
  • Ahmad, Aqueel
  • Yadav, Ashok Kumar
  • Hasan, Shifa

Abstract

The persistent nature of plastics significantly contributes to waste generation, making their disposal a critical issue. Pyrolysis, which converts plastic waste into fuel, is a promising solution to the energy crisis. This study evaluates the pyrolysis potential of low-density polyethylene (LDPE) plastic waste, focusing on the influence of process parameters (reactor temperature, residence time, N2 flow rate, and catalyst concentration) and their optimization using seven supervised machine learning models (RF, GB, ET, DT, KNN, AB, and XGB) and a desirability approach. Feature selection techniques (PCC, F-score, and MI) were employed to identify pertinent input features, with MI exhibiting superior performance in accurately identifying and combining the most relevant features for the model's predictive accuracy. The gradient boosting (GB) model demonstrated the highest predictive accuracy with R2 scores of 0.948, 0.9799, and 0.9802 for bio-oil, biochar, and syngas yields, respectively, and the lowest errors (RMSE: 0.6859, 0.1349, and 0.4194; MAE: 0.4505, 0.078, and 0.2673; MAPE: 0.681, 1.1379, and 0.9629). The tree-based pipeline optimization approach shows the optimal conditions of a reactor temperature of 450 °C, a residence time of 2 h, an N2 flow rate of 150 mL/min, and a catalyst concentration of 6 wt %, resulting in a maximum bio-oil yield of 72.20 % and biochar and syngas yields of 6.35 % and 23.48 %, respectively. Detailed characterization via GC-MS analysis indicated that bio-oil, which is composed of various organic compounds, such as phenols, ketones, alcohols, esters, and aromatic hydrocarbons, can be upgraded for use as a hydrocarbon fuel. Biochar and syngas can be used in power generation, heating, industrial boilers, cement kilns, and as soil amendments or chemical feedstocks. These findings support the effective use of LDPE as a plastic waste for bioenergy precursor production.

Suggested Citation

  • Ahmad, Aqueel & Yadav, Ashok Kumar & Hasan, Shifa, 2025. "A novel waste-to-energy conversion plant based on catalytic pyrolytic conditions: Modeling and optimization using supervised machine learning and desirability-driven methodologies," Energy, Elsevier, vol. 317(C).
  • Handle: RePEc:eee:energy:v:317:y:2025:i:c:s0360544225002907
    DOI: 10.1016/j.energy.2025.134648
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225002907
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.134648?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Cao, Bin & Shen, Weiwei & Pan, Cheng & Kumar, Sunel & Yuan, Chuan & Jiang, Ding & Hu, Yamin & Ling, Qifan & Abomohra, Abdelfatah & Gong, Xun & Esakkimuthu, Sivakumar & Wang, Shuang, 2024. "Optimizing macroalgal biochar-based catalysts for monophenols recovery during wood dust catalytic pyrolysis: A comparative study using response surface methodology and artificial neural network," Renewable Energy, Elsevier, vol. 237(PA).
    2. Azubuike Francis Anene & Siw Bodil Fredriksen & Kai Arne Sætre & Lars-Andre Tokheim, 2018. "Experimental Study of Thermal and Catalytic Pyrolysis of Plastic Waste Components," Sustainability, MDPI, vol. 10(11), pages 1-11, October.
    3. Vlasopoulos, Antonis & Malinauskaite, Jurgita & Żabnieńska-Góra, Alina & Jouhara, Hussam, 2023. "Life cycle assessment of plastic waste and energy recovery," Energy, Elsevier, vol. 277(C).
    4. Isahak, Wan Nor Roslam Wan & Hisham, Mohamed W.M. & Yarmo, Mohd Ambar & Yun Hin, Taufiq-yap, 2012. "A review on bio-oil production from biomass by using pyrolysis method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5910-5923.
    5. Rigamonti, L. & Grosso, M. & Møller, J. & Martinez Sanchez, V. & Magnani, S. & Christensen, T.H., 2014. "Environmental evaluation of plastic waste management scenarios," Resources, Conservation & Recycling, Elsevier, vol. 85(C), pages 42-53.
    6. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
    7. Duan, Dengle & Feng, Zhiqiang & Dong, Xiaoyong & Chen, Xiaoru & Zhang, Yayun & Wan, Kun & Wang, Yunpu & Wang, Qin & Xiao, Gengsheng & Liu, Huifan & Ruan, Roger, 2021. "Improving bio-oil quality from low-density polyethylene pyrolysis: Effects of varying activation and pyrolysis parameters," Energy, Elsevier, vol. 232(C).
    8. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    9. Singh, Satyansh & Chakraborty, Jyoti Prasad & Mondal, Monoj Kumar, 2019. "Optimization of process parameters for torrefaction of Acacia nilotica using response surface methodology and characteristics of torrefied biomass as upgraded fuel," Energy, Elsevier, vol. 186(C).
    10. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).
    11. Chen, Xiangmeng & Shafizadeh, Alireza & Shahbeik, Hossein & Nadian, Mohammad Hossein & Golvirdizadeh, Milad & Peng, Wanxi & Lam, Su Shiung & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2025. "Enhanced bio-oil production from biomass catalytic pyrolysis using machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 209(C).
    12. Sharma, Bhasha & Goswami, Yagyadatta & Sharma, Shreya & Shekhar, Shashank, 2021. "Inherent roadmap of conversion of plastic waste into energy and its life cycle assessment: A frontrunner compendium," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ha, Byeongmin & Lee, Hyeonjeong & Hwangbo, Soonho, 2025. "Towards sustainable energy efficiency: Data-driven optimization in large-scale plants using machine learning applications," Energy, Elsevier, vol. 331(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anna Matuszewska & Marlena Owczuk & Krzysztof Biernat, 2022. "Current Trends in Waste Plastics’ Liquefaction into Fuel Fraction: A Review," Energies, MDPI, vol. 15(8), pages 1-32, April.
    2. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).
    3. Sinha, Shruti & Sankar Rao, Chinta & Kumar, Abhishankar & Venkata Surya, Dadi & Basak, Tanmay, 2024. "Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model," Renewable Energy, Elsevier, vol. 231(C).
    4. Yeole, Shreya P. & Prasad, Tanishq & Kundu, Debashis, 2025. "Molecular descriptor-based artificial intelligence frameworks for the estimation of bio-oil yield from agricultural waste," Renewable Energy, Elsevier, vol. 239(C).
    5. Divine Senanu Ametefe & George Dzorgbenya Ametefe & Dah John & Abdulmalik Adozuka Aliu & Macaulay M. Owen & Solehuddin Shuib & Aisha Hamid, 2025. "Energy Generation from Plastic Composites: A Systematic Review of Sustainable Practices and Technologies," Circular Economy and Sustainability, Springer, vol. 5(2), pages 1307-1343, April.
    6. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).
    7. Zang, Haixiang & Jiang, Xin & Cheng, LiLin & Zhang, Fengchun & Wei, Zhinong & Sun, Guoqiang, 2022. "Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations," Renewable Energy, Elsevier, vol. 195(C), pages 795-808.
    8. Jiang, Chengcheng & Zhu, Qunzhi, 2023. "Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer," Applied Energy, Elsevier, vol. 348(C).
    9. Ma, Xingxing & Tian, Yajun & Wang, Nana & Zhao, Jinghao & Li, Wen-ying & Xie, Kechang, 2026. "Machine learning methods for understanding coal pyrolysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PC).
    10. Huang, Jijiang & Veksha, Andrei & Chan, Wei Ping & Giannis, Apostolos & Lisak, Grzegorz, 2022. "Chemical recycling of plastic waste for sustainable material management: A prospective review on catalysts and processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    11. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    12. Karimzadeh, Sara, 2026. "Waste delinking: A pathway to degrowth?," Ecological Economics, Elsevier, vol. 241(C).
    13. Liao, Zhouyi & Coimbra, Carlos F.M., 2024. "Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks," Renewable Energy, Elsevier, vol. 232(C).
    14. Buentello-Montoya, D.A. & Duarte-Ruiz, C.A. & Maldonado-Escalante, J.F., 2023. "Co-gasification of waste PET, PP and biomass for energy recovery: A thermodynamic model to assess the produced syngas quality," Energy, Elsevier, vol. 266(C).
    15. Duan, Yumin & Wang, Zhi & Ganeshan, Prabakaran & Sar, Taner & Xu, Suyun & Rajendran, Karthik & Sindhu, Raveendran & Binod, Parameswaran & Pandey, Ashok & Zhang, Zengqiang & Taherzadeh, Mohammad J. & A, 2025. "Anaerobic digestion in global bio-energy production for sustainable bioeconomy: Potential and research challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    16. Cui, Yang & Chen, Zhenghong & He, Yingjie & Xiong, Xiong & Li, Fen, 2023. "An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events," Energy, Elsevier, vol. 263(PC).
    17. Mao, Mingxuan & Wang, Lining & Jiang, Sheng & Zhang, Hao, 2026. "Meteorological element prediction for renewable energy systems: Comprehensive comparison on deep learning algorithms with/without hyperparameters tuning," Energy, Elsevier, vol. 346(C).
    18. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    19. Zang, Haixiang & Li, Wenan & Cheng, Lilin & Liu, Jingxuan & Wei, Zhinong & Sun, Guoqiang, 2025. "Short-term multi-site solar irradiance prediction with dynamic-graph-convolution-based spatial-temporal correlation capturing," Renewable Energy, Elsevier, vol. 246(C).
    20. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:317:y:2025:i:c:s0360544225002907. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.