Renewable energy approach towards powering the CI engine with ternary blends of algal biodiesel-diesel-diethyl ether: Bayesian optimized Gaussian process regression for modeling-optimization

Alruqi, Mansoor; Sharma, Prabhakar; Shaik, Feroz

doi:10.1016/j.fuel.2022.126827

Cited by 53 publications

(8 citation statements)

References 54 publications

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“…Table 4 displays the uncertainty of the measured parameters. The uncertainty parameter is quantified as 35,36 …”

Section: Methodsmentioning

confidence: 99%

Exploratory of novel thermal barrier coating on diesel engine performance

Raja,

Sivanandi,

Dhandabani

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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This research presents an experimental and theoretical evaluation of the effect of poly-composition thermal barrier coatings on the performance and emission characteristics of the compression ignition engine. This work investigated the thermal and physical behavior of thermal barrier coating. The thermal barrier coating was coated into two layers, and the thermal plasma spray method was used to make the coating. The first layer is AT13, and the top layer is Yttria-stabilized zirconia. The coated and uncoated pistons were analyzed in the Ansys, and it has shown the temperature distribution of the pistons. The experimental report shows that the brake thermal and volumetric efficiency was increased by 7.52% and 1.06%, brake-specific fuel consumption was reduced up to 18.91%, emission of carbon monoxide was reduced by 50%, and hydrocarbon was reduced by 52.38%.

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“…Table 4 displays the uncertainty of the measured parameters. The uncertainty parameter is quantified as 35,36 …”

Section: Methodsmentioning

confidence: 99%

Exploratory of novel thermal barrier coating on diesel engine performance

Raja,

Sivanandi,

Dhandabani

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…It could be observed that ANN was mainly used in this domain. Besides this other ML techniques like ANFIS [244], [245], XGBoost [246], GPR [247], Gene expression programming (GEP) [248], [249], SVM, and RF [250], [251] are being applied for model predictions.…”

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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“…ANNs are generally applied as the meta-model in the optimization methods based on the meta-model [ 23 ]. This has been trained by a small representative sample achieved by any sampling technique [ 24 ]. Nevertheless, it is not possible to define the correct training sample size previously due to that it depends on the problem [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Applying modified coot optimization algorithm with artificial neural network meta-model for building energy performance optimization: A case study

You,

Yan,

Thwin

2023

Heliyon

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Renewable energy approach towards powering the CI engine with ternary blends of algal biodiesel-diesel-diethyl ether: Bayesian optimized Gaussian process regression for modeling-optimization

Cited by 53 publications

References 54 publications

Exploratory of novel thermal barrier coating on diesel engine performance

Exploratory of novel thermal barrier coating on diesel engine performance

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

Applying modified coot optimization algorithm with artificial neural network meta-model for building energy performance optimization: A case study

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