Innovative composite machine learning approach for biodiesel production in public vehicles

Yang, Yun; Gao, Lizhen; Abbas, Mohamed; Elkamchouchi, Dalia H.; Alkhalifah, Tamim; Alturise, Fahad; Ponnore, Joffin Jose

doi:10.1016/j.advengsoft.2023.103501

Cited by 7 publications

(5 citation statements)

References 90 publications

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“…A subset of AI called machine learning helps systems learn from and adapt to data, improving their capacity to carry out tasks without explicit programming [33,34]. Computer vision enables computers to analyze and make decisions based on visual input, while natural language processing helps humans and machines communicate [35,36]. Figure 1 depicts an overview of AI.…”

Section: Ai In Sustainable Energy Contextmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency of energy systems. This article conducts an exhaustive analysis of the critical aspects concerning the AI-sustainable energy nexus, encompassing the challenges in technological integration and the facilitation of intelligent decision-making processes pivotal for sustainable energy frameworks. It is demonstrated that AI applications, ranging from optimization algorithms to predictive analytics, possess a revolutionary capacity to bolster intelligent decision-making in sustainable energy. However, this integration is not without its challenges, which span technological complexities and socio-economic impacts. The article underscores the imperative for deploying AI in a manner that is transparent, equitable, and inclusive. Best practices and solutions are proposed to navigate these challenges effectively. Additionally, the discourse extends to recent advancements in AI, including edge computing, quantum computing, and explainable AI, offering insights into the evolving landscape of sustainable energy. Future research directions are delineated, emphasizing the importance of enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining socio-economic ramifications, exploring models of human-AI collaboration, fortifying security measures, and evaluating the impact of emerging technologies. This comprehensive analysis aims to inform academics, practitioners, and policymakers, guiding the creation of a resilient and sustainable energy future.

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Section: Ai In Sustainable Energy Contextmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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“…AdaBoost was efficient in the optimization of process parameters [283] Model forecasting of engine operation…”

Section: Rf and Adaboostmentioning

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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“…Gradient boosting ML model combined with Genetic algorithm (GA) were utilized to predict and optimize biodiesel production yield from waste cooking oil feedstock [28]. AdaBoost regression ML models were utilized to predict fuel properties including flash point, oxidation stability, density, and viscosity for biodiesel fuel application public vehicles considering various reaction operating parameters including agitation speed, fuel blend composition, reaction duration and temperature [29]. Hoang et al conducted experimental analysis of pyrolysis oil and biodiesel oil blends for an application of natural gas enriched homogeneous charge compression-ignition engine [30].…”

Section: Supervised Machine Learning Models In Biodiesel Production R...mentioning

confidence: 99%

pplication of Supervised Machine Learning Models in Biodiesel Production Research - A Short Review

2024

BJoST

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Innovative composite machine learning approach for biodiesel production in public vehicles

Cited by 7 publications

References 90 publications

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

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

pplication of Supervised Machine Learning Models in Biodiesel Production Research - A Short Review

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