Applications of machine learning in thermochemical conversion of biomass-A review

Fuel, Journal Year: 2022, Volume and Issue: 332, P. 126055 - 126055

Published: Sept. 24, 2022

Language: Английский

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Recent advancements and challenges in emerging applications of biochar-based catalysts

Biotechnology Advances, Journal Year: 2023, Volume and Issue: 67, P. 108181 - 108181

Published: June 1, 2023

The sustainable utilization of biochar produced from biomass waste could substantially promote the development carbon neutrality and a circular economy. Due to their cost-effectiveness, multiple functionalities, tailorable porous structure, thermal stability, biochar-based catalysts play vital role in biorefineries environmental protection, contributing positive, planet-level impact. This review provides an overview emerging synthesis routes for multifunctional catalysts. It discusses recent advances biorefinery pollutant degradation air, soil, water, providing deeper more comprehensive information catalysts, such as physicochemical properties surface chemistry. catalytic performance deactivation mechanisms under different systems were critically reviewed, new insights into developing efficient practical large-scale use various applications. Machine learning (ML)-based predictions inverse design have addressed innovation with high-performance applications, ML efficiently predicts biochar, interprets underlying complicated relationships, guides synthesis. Finally, benefit economic feasibility assessments are proposed science-based guidelines industries policymakers. With concerted effort, upgrading protection reduce pollution, increase energy safety, achieve management, all which beneficial attaining several United Nations Sustainable Development Goals (UN SDGs) Environmental, Social Governance (ESG).

Language: Английский

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Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives

Renewable and Sustainable Energy Reviews, Journal Year: 2023, Volume and Issue: 182, P. 113405 - 113405

Published: May 25, 2023

Language: Английский

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Thermochemical conversion of large-size woody biomass for carbon neutrality: Principles, applications, and issues

Bioresource Technology, Journal Year: 2022, Volume and Issue: 370, P. 128562 - 128562

Published: Dec. 29, 2022

Language: Английский

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Artificial Intelligence in Biological Sciences

Life, Journal Year: 2022, Volume and Issue: 12(9), P. 1430 - 1430

Published: Sept. 14, 2022

Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve quality of life human beings. The fields AI and biological research are becoming more intertwined, methods for extracting applying information stored in live organisms constantly being refined. As field matures with trained algorithms, its application epidemiology, study host–pathogen interactions drug designing widens. is now applied several discovery, customized medicine, gene editing, radiography, image processing medication management. More precise diagnosis cost-effective treatment will be possible near future due AI-based technologies. In agriculture, farmers have reduced waste, increased output decreased amount time it takes bring their goods market advanced approaches. Moreover, use through machine learning (ML) deep-learning-based smart programs, one can modify metabolic pathways living systems obtain best outputs minimal inputs. Such efforts industrial strains microbial species maximize yield bio-based setup. This article summarizes potentials biology, such as industry.

Language: Английский

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A comparative analysis of biomass torrefaction severity index prediction from machine learning

Applied Energy, Journal Year: 2022, Volume and Issue: 324, P. 119689 - 119689

Published: July 22, 2022

Language: Английский

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Advances in machine learning technology for sustainable biofuel production systems in lignocellulosic biorefineries

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 886, P. 163972 - 163972

Published: May 8, 2023

Language: Английский

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Lignocellulosic biofuel properties and reactivity analyzed by thermogravimetric analysis (TGA) toward zero carbon scheme: A critical review

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 22, P. 100538 - 100538

Published: Jan. 28, 2024

Biomass is an organic substance widely available in nature as a fresh or waste material considered renewable energy that aligns with the zero-carbon scheme to reduce dependency on fossil fuels. However, after conversion, biomass's physical chemical properties highly affect biofuel characteristics. A variety of instruments can be used figure out reactivity. Considering commonly adopted instruments, thermogravimetric analysis (TGA) simple, fast, and efficient way determine The TGA method has capability analyze (proximate analysis: moisture, volatile matter, fixed carbon, ash) combustion features biomass (such ignition, reactivity, etc). Most importantly, TG curvatures (TGA DTG) reveal behavior during thermodegradation process. As consequence, quality quantity analyses reactivity investigated comprehensively. Moreover, some integration artificial intelligence (AI) been studied better understand management technology for future development. outcome TGA-AI may obtain excellent result fit value R2>95 %. This study aims comprehensively review relevant research using lignocellulosic discussion this extended perspective, challenges, work.

Language: Английский

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A review of noncatalytic and catalytic pyrolysis and co-pyrolysis products from lignocellulosic and algal biomass using Py-GC/MS

Journal of Industrial and Engineering Chemistry, Journal Year: 2024, Volume and Issue: 134, P. 51 - 64

Published: Jan. 9, 2024

Biomass pyrolysis has garnered significant attention as a sustainable energy production method utilizing various biomass feedstocks. Pyrolysate is any product generated from the process, including solid, liquid, and gas types. This review focuses on application of analytical pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) in context four modes: single feedstock pyrolysis, co-pyrolysis, catalytic co-pyrolysis to gain insights into characteristics pyrolysates. A comprehensive understanding each mode's unique products, benefits, limitations achieved by analyzing pyrolysates different feedstocks, lignocellulosic algal biomass. Moreover, this study discusses integration Py-GC/MS with techniques such density function theory (DFT), which estimating reactions' activation energies or kinetic studies concentrating reaction rate mechanism further insight mechanisms. Lastly, design experiment (DoE) are proposed for optimization obtain more assessment parameter's influence factors levels.

Language: Английский

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Introduction of machine learning and artificial intelligence in biofuel technology

Current Opinion in Green and Sustainable Chemistry, Journal Year: 2024, Volume and Issue: 47, P. 100928 - 100928

Published: April 26, 2024

Language: Английский

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