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Biomass Gasification and Applied Intelligent Retrieval in Modeling

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  • 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
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    References listed on IDEAS

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