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

Chen, Wei-Hsin; Ho, Kuan-Yu; Aniza, Ria; Sharma, Amit Kumar; Saravanakumar, Ayyadurai; Hoang, Anh Tuan

doi:10.1016/j.jiec.2024.01.020

Cited by 12 publications

(1 citation statement)

References 112 publications

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“…G. Nguyen et al, 2024f;Umer et al, 2024). At 300-650 °C in the absence of air, biomass feedstock pyrolyzes to produce bio-oil, charcoal, and pyrolytic gas (Chen et al, 2024a;Hoang et al, 2021d). Torrefied biomass is produced when pyrolysis, a milder kind, takes place without oxygen at 200-320 °C under air pressure (Niu et al, 2019;Tang et al, 2020).…”

Section: Application Of Machine Learning In Biomassmentioning

confidence: 99%

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

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This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

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Section: Application Of Machine Learning In Biomassmentioning

confidence: 99%

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

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Circular economy and waste production models for sustainable development goals 12 and 14: Evidence from cruise sustainability reporting

Di Vaio,

Dell'Amura,

Chhabra

et al. 2024

Sustainable Development

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The relationship between the practices and initiatives governing “waste production models” (sustainable development goal [SDG]12) and marine biodiversity goals (SDG14) is relatively unexplored. Aiming to bridge this gap by drawing on stakeholder and legitimacy theories, this study examines onboard cruise ships' circular economy (CE)‐based waste management practices and initiatives, correlating SGDs 12 and 14. Consequently, Carnival Corporation Plc's 2020–2022 sustainability reports are analyzed using content analysis by both Leximancer software (ver. 5.0) and manual methods. The results highlight the corporation's increasing commitment to green technologies for “waste production models” to achieve SDG14. However, its sustainability reports provide unclear evidence of the impact on marine biodiversity. Findings implies that practitioners should partner and invest in green technologies for “waste production models” to achieve SDG14. Besides being the first to explore the link between the two SDGs within the CE framework, this study advances insights into waste management within “waste production models,” enhancing the understanding of sustainable practices.

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Energy out of waste: kinetics and thermolysis of co-pyrolysis of biomass and municipal plastic waste

Baranitharan,

Elaiyarasan,

Sakthivel

et al. 2024

Biomass Conv. Bioref.

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

Cited by 12 publications

References 112 publications

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Circular economy and waste production models for sustainable development goals 12 and 14: Evidence from cruise sustainability reporting

Energy out of waste: kinetics and thermolysis of co-pyrolysis of biomass and municipal plastic waste

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