Biomass Gasification and Applied Intelligent Retrieval in Modeling

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 () and Vladimir Panchenko ()
Additional contact information
Manish Meena: Department of Chemical and Biochemical Engineering, Indian Institute of Technology Patna, Patna 801106, Bihar, India
Hrishikesh Kumar: Centre for Energy and Environment, Malaviya National Institute of Technology Jaipur, Jaipur 302017, Rajasthan, 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

Energies, 2023, vol. 16, issue 18, 1-21

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.

Keywords: gasification technology; machine learning; biomass gasification; energy; applications (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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