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    Downdraft Gasification for Biogas Production: The Role of Artificial Intelligence

    Source: Journal of Energy Resources Technology:;2024:;volume( 146 ):;issue: 012::page 120801-1
    Author:
    Sharma, Vandana
    ,
    Upreti, Kamal
    ,
    Natarajan, Arul Kumar
    ,
    Jain, Nishi
    ,
    Kumar, Sanjay
    ,
    Bara, Anant Rajee
    ,
    Kumari, Sushma
    DOI: 10.1115/1.4066059
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Artificial intelligence (AI) can help improve many areas of waste management and biogas generation. The world has reached a state where waste generation is increasing daily, while an effective waste management system is essential for the sustainable development of a country. AI could be of great use in optimizing the waste management scheme by technical differentiation of all sorts and recycling techniques. AI can contribute to the improvement of waste segmentation, recycling, and disposal. Thus, by assessing availability and composition, AI can easily contribute to the selection of the most suitable feedstock for biogas generation. This paper will discuss the optimization of gasifier design, an important part of biogas production, to enhance gasification efficiency for more efficient syngas production. Several gains accrue from AI applications, and among them is the selection of feedstocks and gasifiers optimal for more efficient and sustainable waste management and use in the production of biogas systems. This review paper identifies the potential application areas in either waste management practices or biogas production and puts forward ways in which AI can be used in these areas.
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      Downdraft Gasification for Biogas Production: The Role of Artificial Intelligence

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303263
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    • Journal of Energy Resources Technology

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    contributor authorSharma, Vandana
    contributor authorUpreti, Kamal
    contributor authorNatarajan, Arul Kumar
    contributor authorJain, Nishi
    contributor authorKumar, Sanjay
    contributor authorBara, Anant Rajee
    contributor authorKumari, Sushma
    date accessioned2024-12-24T19:05:25Z
    date available2024-12-24T19:05:25Z
    date copyright8/20/2024 12:00:00 AM
    date issued2024
    identifier issn0195-0738
    identifier otherjert_146_12_120801.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303263
    description abstractArtificial intelligence (AI) can help improve many areas of waste management and biogas generation. The world has reached a state where waste generation is increasing daily, while an effective waste management system is essential for the sustainable development of a country. AI could be of great use in optimizing the waste management scheme by technical differentiation of all sorts and recycling techniques. AI can contribute to the improvement of waste segmentation, recycling, and disposal. Thus, by assessing availability and composition, AI can easily contribute to the selection of the most suitable feedstock for biogas generation. This paper will discuss the optimization of gasifier design, an important part of biogas production, to enhance gasification efficiency for more efficient syngas production. Several gains accrue from AI applications, and among them is the selection of feedstocks and gasifiers optimal for more efficient and sustainable waste management and use in the production of biogas systems. This review paper identifies the potential application areas in either waste management practices or biogas production and puts forward ways in which AI can be used in these areas.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleDowndraft Gasification for Biogas Production: The Role of Artificial Intelligence
    typeJournal Paper
    journal volume146
    journal issue12
    journal titleJournal of Energy Resources Technology
    identifier doi10.1115/1.4066059
    journal fristpage120801-1
    journal lastpage120801-13
    page13
    treeJournal of Energy Resources Technology:;2024:;volume( 146 ):;issue: 012
    contenttypeFulltext
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