State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery

Aditya Velidandi; Pradeep Kumar Gandam; Madhavi Latha Chinta; Srilekha Konakanchi; Anji reddy Bhavanam; Rama Raju Baadhe; Minaxi Sharma; James Gaffey; Quang D. Nguyen; Vijai Kumar Gupta

doi:10.1016/j.jechem.2023.02.020

State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery

Aditya Velidandi, Pradeep Kumar Gandam, Madhavi Latha Chinta, Srilekha Konakanchi, Anji reddy Bhavanam, Rama Raju Baadhe, Minaxi Sharma, James Gaffey, Quang D. Nguyen, Vijai Kumar Gupta

Research output: Journal Publication › Review article › peer-review

21 Citations (Scopus)

Abstract

Machine learning (ML) has emerged as a significant tool in the field of biorefinery, offering the capability to analyze and predict complex processes with efficiency. This article reviews the current state of biorefinery and its classification, highlighting various commercially successful biorefineries. Further, we delve into different categories of ML models, including their algorithms and applications in various stages of biorefinery lifecycle, such as biomass characterization, pretreatment, lignin valorization, chemical, thermochemical and biochemical conversion processes, supply chain analysis, and life cycle assessment. The benefits and limitations of each of these algorithms are discussed in detail. Finally, the article concludes with a discussion of the limitations and future prospects of ML in the field of biorefineries.

Original language	English
Pages (from-to)	42-63
Number of pages	22
Journal	Journal of Energy Chemistry
Volume	81
DOIs	https://doi.org/10.1016/j.jechem.2023.02.020
Publication status	Published - Jun 2023
Externally published	Yes

Keywords

Biofuel
Biomass characterization
Biorefinery
Life cycle assessment
Machine learning
Pretreatment

ASJC Scopus subject areas

Fuel Technology
Energy Engineering and Power Technology
Energy (miscellaneous)
Electrochemistry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.jechem.2023.02.020

Cite this

Velidandi, A., Kumar Gandam, P., Latha Chinta, M., Konakanchi, S., reddy Bhavanam, A., Raju Baadhe, R., Sharma, M., Gaffey, J., Nguyen, Q. D., & Gupta, V. K. (2023). State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery. Journal of Energy Chemistry, 81, 42-63. https://doi.org/10.1016/j.jechem.2023.02.020

@article{4b907d54c879488786f80294b197dad0,

title = "State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery",

abstract = "Machine learning (ML) has emerged as a significant tool in the field of biorefinery, offering the capability to analyze and predict complex processes with efficiency. This article reviews the current state of biorefinery and its classification, highlighting various commercially successful biorefineries. Further, we delve into different categories of ML models, including their algorithms and applications in various stages of biorefinery lifecycle, such as biomass characterization, pretreatment, lignin valorization, chemical, thermochemical and biochemical conversion processes, supply chain analysis, and life cycle assessment. The benefits and limitations of each of these algorithms are discussed in detail. Finally, the article concludes with a discussion of the limitations and future prospects of ML in the field of biorefineries.",

keywords = "Biofuel, Biomass characterization, Biorefinery, Life cycle assessment, Machine learning, Pretreatment",

author = "Aditya Velidandi and {Kumar Gandam}, Pradeep and {Latha Chinta}, Madhavi and Srilekha Konakanchi and {reddy Bhavanam}, Anji and {Raju Baadhe}, Rama and Minaxi Sharma and James Gaffey and Nguyen, {Quang D.} and Gupta, {Vijai Kumar}",

note = "Publisher Copyright: {\textcopyright} 2023 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences",

year = "2023",

month = jun,

doi = "10.1016/j.jechem.2023.02.020",

language = "English",

volume = "81",

pages = "42--63",

journal = "Journal of Energy Chemistry",

issn = "2095-4956",

publisher = "Elsevier BV",

}

TY - JOUR

T1 - State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery

AU - Velidandi, Aditya

AU - Kumar Gandam, Pradeep

AU - Latha Chinta, Madhavi

AU - Konakanchi, Srilekha

AU - reddy Bhavanam, Anji

AU - Raju Baadhe, Rama

AU - Sharma, Minaxi

AU - Gaffey, James

AU - Nguyen, Quang D.

AU - Gupta, Vijai Kumar

PY - 2023/6

Y1 - 2023/6

N2 - Machine learning (ML) has emerged as a significant tool in the field of biorefinery, offering the capability to analyze and predict complex processes with efficiency. This article reviews the current state of biorefinery and its classification, highlighting various commercially successful biorefineries. Further, we delve into different categories of ML models, including their algorithms and applications in various stages of biorefinery lifecycle, such as biomass characterization, pretreatment, lignin valorization, chemical, thermochemical and biochemical conversion processes, supply chain analysis, and life cycle assessment. The benefits and limitations of each of these algorithms are discussed in detail. Finally, the article concludes with a discussion of the limitations and future prospects of ML in the field of biorefineries.

AB - Machine learning (ML) has emerged as a significant tool in the field of biorefinery, offering the capability to analyze and predict complex processes with efficiency. This article reviews the current state of biorefinery and its classification, highlighting various commercially successful biorefineries. Further, we delve into different categories of ML models, including their algorithms and applications in various stages of biorefinery lifecycle, such as biomass characterization, pretreatment, lignin valorization, chemical, thermochemical and biochemical conversion processes, supply chain analysis, and life cycle assessment. The benefits and limitations of each of these algorithms are discussed in detail. Finally, the article concludes with a discussion of the limitations and future prospects of ML in the field of biorefineries.

KW - Biofuel

KW - Biomass characterization

KW - Biorefinery

KW - Life cycle assessment

KW - Machine learning

KW - Pretreatment

UR - http://www.scopus.com/inward/record.url?scp=85149751438&partnerID=8YFLogxK

U2 - 10.1016/j.jechem.2023.02.020

DO - 10.1016/j.jechem.2023.02.020

M3 - Review article

AN - SCOPUS:85149751438

SN - 2095-4956

VL - 81

SP - 42

EP - 63

JO - Journal of Energy Chemistry

JF - Journal of Energy Chemistry

ER -

State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this