Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Ahmad, Iftikhar; Sana, Adil; Kano, Manabu; Cheema, Izzat Iqbal; Menezes, Brenno C.; Shahzad, Junaid; Ullah, Zahid; Khan, Muzammil; Habib, Asad

doi:10.3390/en14165072

Cited by 16 publications

(2 citation statements)

References 138 publications

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“… Machine learning algorithms are able to evaluate the environmental consequences of biofuels over their whole lifespan. These effects include but are not limited to changes in land usage, water consumption, and emissions of greenhouse gases [281]. By addressing these difficulties, machine learning improves the efficiency and long-term viability of the biofuel business and contributes to the sector's growth and integration into the global energy environment.…”

Section: ) Biofuel-based Energy Forecastingmentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

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Section: ) Biofuel-based Energy Forecastingmentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…The use of heterogeneous solid base catalysts (single metal oxides [145], mixed metal oxides [146], supported alkali metal/metal ion [147], clay mineral (hydrotalcites) [148] and organic solid bases [149,150] in the synthesis of biodiesel from vegetable oil-based feedstock has received a lot of attention. These included the metal oxides La 2 O 3 , MgO, ZnO, and CaO.…”

Section: Heterogeneous Base Catalysts For Biodiesel Productionmentioning

confidence: 99%

A Short Review on Catalyst, Feedstock, Modernised Process, Current State and Challenges on Biodiesel Production

et al. 2021

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Biodiesel, comprising mono alkyl fatty acid esters or methyl ethyl esters, is an encouraging option to fossil fuels or diesel produced from petroleum; it has comparable characteristics and its use has the potential to diminish carbon dioxide production and greenhouse gas emissions. Manufactured from recyclable and sustainable feedstocks, e.g., oils originating from vegetation, biodiesel has biodegradable properties and has no toxic impact on ecosystems. The evolution of biodiesel has been precipitated by the continuing environmental damage created by the deployment of fossil fuels. Biodiesel is predominantly synthesised via transesterification and esterification procedures. These involve a number of key constituents, i.e., the feedstock and catalytic agent, the proportion of methanol to oil, the circumstances of the reaction and the product segregation and purification processes. Elements that influence the yield and standard of the obtained biodiesel encompass the form and quantity of the feedstock and reaction catalyst, the proportion of alcohol to feedstock, the temperature of the reaction, and its duration. Contemporary research has evaluated the output of biodiesel reactors in terms of energy production and timely biodiesel manufacture. In order to synthesise biodiesel for industrial use efficaciously, it is essential to acknowledge the technological advances that have significant potential in this sector. The current paper therefore offers a review of contemporary progress, feedstock categorisation, and catalytic agents for the manufacture of biodiesel and production reactors, together with modernised processing techniques. The production reactor, form of catalyst, methods of synthesis, and feedstock standards are additionally subjects of discourse so as to detail a comprehensive setting pertaining to the chemical process. Numerous studies are ongoing in order to develop increasingly efficacious techniques for biodiesel manufacture; these acknowledge the use of solid catalytic agents and non-catalytic supercritical events. This review appraises the contemporary situation with respect to biodiesel production in a range of contexts. The spectrum of techniques for the efficacious manufacture of biodiesel encompasses production catalysed by homogeneous or heterogeneous enzymes or promoted by microwave or ultrasonic technologies. A description of the difficulties to be surmounted going forward in the sector is presented.

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Bacterial Biofuel Production: An Assessment of Machine Learning Technologies in Valorisation of Waste/Wastewater and Circular Economy

Dey,

Gangaraju,

Paul

et al. 2024

Trash or Treasure

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Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Cited by 16 publications

References 138 publications

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

A Short Review on Catalyst, Feedstock, Modernised Process, Current State and Challenges on Biodiesel Production

Bacterial Biofuel Production: An Assessment of Machine Learning Technologies in Valorisation of Waste/Wastewater and Circular Economy

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