IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6524-d1236893.html

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Author

Listed:
  • Manish Meena

    (Department of Chemical and Biochemical Engineering, Indian Institute of Technology Patna, Patna 801106, Bihar, India
    Centre for Energy and Environment, Malaviya National Institute of Technology Jaipur, Jaipur 302017, Rajasthan, India)

  • Hrishikesh Kumar

    (Centre for Energy and Environment, Malaviya National Institute of Technology Jaipur, Jaipur 302017, Rajasthan, India
    Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144011, Punjab, India)

  • Nitin Dutt Chaturvedi

    (Department of Chemical and Biochemical Engineering, Indian Institute of Technology Patna, Patna 801106, Bihar, India)

  • Andrey A. Kovalev

    (Federal State Budgetary Scientific Institution “Federal Scientific Agroengineering Center VIM”, 1st Institutskiy Proezd, 5, 109428 Moscow, Russia)

  • Vadim Bolshev

    (Federal State Budgetary Scientific Institution “Federal Scientific Agroengineering Center VIM”, 1st Institutskiy Proezd, 5, 109428 Moscow, Russia)

  • Dmitriy A. Kovalev

    (Federal State Budgetary Scientific Institution “Federal Scientific Agroengineering Center VIM”, 1st Institutskiy Proezd, 5, 109428 Moscow, Russia)

  • Prakash Kumar Sarangi

    (College of Agriculture, Central Agricultural University, Imphal 795004, Manipur, India)

  • Aakash Chawade

    (Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, 23053 Uppsala, Sweden)

  • Manish Singh Rajput

    (Department of Biotechnology, Dr. Ambedkar Institute of Technology for Handicapped, Kanpur 208024, Uttar Pradesh, India)

  • Vivekanand Vivekanand

    (Centre for Energy and Environment, Malaviya National Institute of Technology Jaipur, Jaipur 302017, Rajasthan, India)

  • Vladimir Panchenko

    (Russian University of Transport, 127994 Moscow, Russia)

Abstract

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies.

Suggested Citation

  • Manish Meena & Hrishikesh Kumar & Nitin Dutt Chaturvedi & Andrey A. Kovalev & Vadim Bolshev & Dmitriy A. Kovalev & Prakash Kumar Sarangi & Aakash Chawade & Manish Singh Rajput & Vivekanand Vivekanand , 2023. "Biomass Gasification and Applied Intelligent Retrieval in Modeling," Energies, MDPI, vol. 16(18), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6524-:d:1236893
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6524/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/18/6524/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    2. Silva, Isabelly P. & Lima, Rafael M.A. & Silva, Gabriel F. & Ruzene, Denise S. & Silva, Daniel P., 2019. "Thermodynamic equilibrium model based on stoichiometric method for biomass gasification: A review of model modifications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Mutlu, Ali Yener & Yucel, Ozgun, 2018. "An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification," Energy, Elsevier, vol. 165(PA), pages 895-901.
    4. Hung-Ta Wen & Jau-Huai Lu & Mai-Xuan Phuc, 2021. "Applying Artificial Intelligence to Predict the Composition of Syngas Using Rice Husks: A Comparison of Artificial Neural Networks and Gradient Boosting Regression," Energies, MDPI, vol. 14(10), pages 1-18, May.
    5. Chen, Wei-Hsin & Aniza, Ria & Arpia, Arjay A. & Lo, Hsiu-Ju & Hoang, Anh Tuan & Goodarzi, Vahabodin & Gao, Jianbing, 2022. "A comparative analysis of biomass torrefaction severity index prediction from machine learning," Applied Energy, Elsevier, vol. 324(C).
    6. Sara Maen Asaad & Abrar Inayat & Lisandra Rocha-Meneses & Farrukh Jamil & Chaouki Ghenai & Abdallah Shanableh, 2022. "Prospective of Response Surface Methodology as an Optimization Tool for Biomass Gasification Process," Energies, MDPI, vol. 16(1), pages 1-18, December.
    7. Lefevere, Raphaël & Mariani, Mauro & Zambotti, Lorenzo, 2011. "Large deviations for renewal processes," Stochastic Processes and their Applications, Elsevier, vol. 121(10), pages 2243-2271, October.
    8. Kim, Jun Young & Shin, Ui Hyeon & Kim, Kwangsu, 2023. "Predicting biomass composition and operating conditions in fluidized bed biomass gasifiers: An automated machine learning approach combined with cooperative game theory," Energy, Elsevier, vol. 280(C).
    9. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Rafiee, Shahin & Hafezi, Amir & Du, Xinyi & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2023. "Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning," Energy, Elsevier, vol. 278(PB).
    10. Li, Bin & Magoua Mbeugang, Christian Fabrice & Huang, Yong & Liu, Dongjing & Wang, Qian & Zhang, Shu, 2022. "A review of CaO based catalysts for tar removal during biomass gasification," Energy, Elsevier, vol. 244(PB).
    11. Krzywanski, J. & Czakiert, T. & Nowak, W. & Shimizu, T. & Zylka, A. & Idziak, K. & Sosnowski, M. & Grabowska, K., 2022. "Gaseous emissions from advanced CLC and oxyfuel fluidized bed combustion of coal and biomass in a complex geometry facility:A comprehensive model," Energy, Elsevier, vol. 251(C).
    12. Kargbo, Hannah O. & Zhang, Jie & Phan, Anh N., 2021. "Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network," Applied Energy, Elsevier, vol. 302(C).
    13. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Masoudnia, Nima & Rafiee, Shahin & Zhang, Yijia & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries," Renewable Energy, Elsevier, vol. 201(P2), pages 70-86.
    14. Ayub, Yousaf & Ren, Jingzheng & Shi, Tao & Shen, Weifeng & He, Chang, 2023. "Poultry litter valorization: Development and optimization of an electro-chemical and thermal tri-generation process using an extreme gradient boosting algorithm," Energy, Elsevier, vol. 263(PC).
    15. Shahbeik, Hossein & Rafiee, Shahin & Shafizadeh, Alireza & Jeddi, Dorsa & Jafary, Tahereh & Lam, Su Shiung & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Characterizing sludge pyrolysis by machine learning: Towards sustainable bioenergy production from wastes," Renewable Energy, Elsevier, vol. 199(C), pages 1078-1092.
    16. Babacar Gaye & Dezheng Zhang & Aziguli Wulamu, 2021. "Improvement of Support Vector Machine Algorithm in Big Data Background," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, June.
    17. Ayub, Yousaf & Hu, Yusha & Ren, Jingzheng, 2023. "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models," Renewable Energy, Elsevier, vol. 215(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. Escámez, Antonio & Aguado, Roque & Sánchez-Lozano, Daniel & Jurado, Francisco & Vera, David, 2025. "An ensemble multi-ANN approach for virtual oxygen sensing and air leakage prediction in biomass gasification plants," Renewable Energy, Elsevier, vol. 242(C).
    2. David Antonio Buentello-Montoya & Victor Manuel Maytorena-Soria, 2025. "Feature Importance Analysis of Solar Gasification of Biomass via Machine Learning Models," Energies, MDPI, vol. 18(16), pages 1-19, August.

    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. Santhappan, Joseph Sekhar & Boddu, Muralikrishna & Gopinath, Arun S. & Mathimani, Thangavel, 2024. "Analysis of 27 supervised machine learning models for the co-gasification assessment of peanut shell and spent tea residue in an open-core downdraft gasifier," Renewable Energy, Elsevier, vol. 235(C).
    2. Mu, Lin & Wang, Zhen & Sun, Meng & Shang, Yan & Pu, Hang & Dong, Ming, 2024. "Machine learning model with a novel self–adjustment method: A powerful tool for predicting biomass ash fusibility and enhancing its potential applications," Renewable Energy, Elsevier, vol. 237(PA).
    3. Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Li, Jiadong & Chen, Yong, 2024. "Biomass hydrothermal gasification characteristics study: based on deep learning for data generation and screening strategies," Energy, Elsevier, vol. 312(C).
    4. Cakar, Mislina & Insel, Mert Akin & Sadikoglu, Hasan & Yucel, Ozgun, 2026. "Multi-target deep learning models for syngas yield and exergy estimation in hybrid fixed and fluidized bed biomass-lignite gasifiers," Energy, Elsevier, vol. 342(C).
    5. Chalak Qazani, Mohammad Reza & Ghasemi, Mostafa & Asadi, Houshyar, 2026. "Optimising the power regeneration and chemical oxygen demand removal in microbial fuel cell systems using integrated soft computing methods and multiple-objective optimisation," Renewable Energy, Elsevier, vol. 256(PD).
    6. 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).
    7. Arsad, A.Z. & Hannan, M.A. & Ong, H.C. & Ker, Pin Jern & Wong, Richard TK. & Begum, R.A. & Jang, Gilsoo & Mahlia, T M Indra, 2025. "Artificial intelligence in hydrogen energy transitions: A comprehensive survey and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    8. Ayub, Yousaf & Hu, Yusha & Ren, Jingzheng, 2023. "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models," Renewable Energy, Elsevier, vol. 215(C).
    9. Ayub, Yousaf & Zhou, Jianzhao & Shen, Weifeng & Ren, Jingzheng, 2023. "Innovative valorization of biomass waste through integration of pyrolysis and gasification: Process design, optimization, and multi-scenario sustainability analysis," Energy, Elsevier, vol. 282(C).
    10. Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Chen, Yong, 2024. "Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning," Energy, Elsevier, vol. 290(C).
    11. Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).
    12. Olca, Kadriye Deniz & Yücel, Özgün, 2024. "Unveiling the potential of operating time in improving machine learning models’ performance for waste biomass gasification systems," Renewable Energy, Elsevier, vol. 237(PA).
    13. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Masoudnia, Nima & Rafiee, Shahin & Zhang, Yijia & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries," Renewable Energy, Elsevier, vol. 201(P2), pages 70-86.
    14. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Rafiee, Shahin & Hafezi, Amir & Du, Xinyi & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2023. "Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning," Energy, Elsevier, vol. 278(PB).
    15. Escámez, Antonio & Aguado, Roque & Sánchez-Lozano, Daniel & Jurado, Francisco & Vera, David, 2025. "An ensemble multi-ANN approach for virtual oxygen sensing and air leakage prediction in biomass gasification plants," Renewable Energy, Elsevier, vol. 242(C).
    16. Bhattarai, Ashish & Kafle, Sagar & Sakhakarmy, Manish & Moogi, Surendar & Adhikari, Sushil, 2024. "Fluidized-bed gasification kinetics model development using genetic algorithm for biomass, coal, municipal plastic waste, and their blends," Energy, Elsevier, vol. 313(C).
    17. Liu, Hongyu & Tang, Yuting & Tang, Jiehong & Yue, Wenchang & Chen, Weilong & Chen, Xinfei & Ma, Xiaoqian, 2026. "Utilizing steel slag in sorption-enhanced biomass gasification: A pathway to syngas production with integrated CO2 capture," Applied Energy, Elsevier, vol. 402(PB).
    18. Yu, Yulong & Lv, Shuangyu & Wang, Qiuyu & Xian, Lei & Chen, Lei & Tao, Wen-Quan, 2024. "A two-stage framework for quantifying the impact of operating parameters and optimizing power density and oxygen distribution quality of PEMFC," Renewable Energy, Elsevier, vol. 236(C).
    19. Feng, Xiangdong & Liu, Shanjian & Yue, Kang & Wei, Heng & Bi, Dongmei & Zhao, Wenjing, 2023. "Insight into the promotional effect of Mn-modified nitrogenous biochar on the NH3-SCR denitrification activity at low temperatures," Energy, Elsevier, vol. 285(C).
    20. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).

    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:gam:jeners:v:16:y:2023:i:18:p:6524-:d:1236893. 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: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    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.