Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning

Soudagar, Manzoore Elahi M.; Shelare, Sagar; Marghade, Deepali; Belkhode, Pramod; Nur-E-Alam, Mohammad; Kiong, Tiong S.; Ramesh, S.; Rajabi, Armin; Venu, Harish; Yunus Khan, T.M.; Mujtaba, MA; Shahapurkar, Kiran; Kalam, MA; Fattah, I.M.R.

doi:10.1016/j.enconman.2024.118337

Energy Conversion and Management

2024

DOI: 10.1016/j.enconman.2024.118337

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Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning

Manzoore Elahi M. Soudagar,

Sagar Shelare,

Deepali Marghade

et al.

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Cited by 9 publications

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Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review

Pawar,

Shreeprakash,

Mokshanatha

et al. 2024

Arch Computat Methods Eng

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Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review

Pawar,

Shreeprakash,

Mokshanatha

et al. 2024

Arch Computat Methods Eng

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Probing the Efficiency of PPMG-Based Composite Electrolytes for Applications of Proton Exchange Membrane Fuel Cell

Ahmed,

Altaf,

Khan

et al. 2024

Trans. Tianjin Univ.

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PPMG-based composite electrolytes were fabricated via the solution method using the polyvinyl alcohol and polyvinylpyrrolidone blend reinforced with various contents of sulfonated inorganic filler. Sulfuric acid was employed as the sulfonating agent to functionalize the external surface of the inorganic filler, i.e., graphene oxide. The proton conductivities of the newly prepared proton exchange membranes (PEMs) were increased by increasing the temperature and content of sulfonated graphene oxide (SGO), i.e., ranging from 0.025 S/cm to 0.060 S/cm. The induction of the optimum level of SGO is determined to be an excellent route to enhance ionic conductivity. The single-cell performance test was conducted by sandwiching the newly prepared PEMs between an anode (0.2 mg/cm2 Pt/Ru) and a cathode (0.2 mg/cm2 Pt) to prepare membrane electrode assemblies, followed by hot pressing under a pressure of approximately 100 kg/cm2 at 60 °C for 5–10 min. The highest power densities achieved with PPMG PEMs were 14.9 and 35.60 mW/cm2 at 25 °C and 70 °C, respectively, at ambient pressure with 100% relative humidity. Results showed that the newly prepared PEMs exhibit good electrochemical performance. The results indicated that the prepared composite membrane with 6 wt% filler can be used as an alternative membrane for applications of high-performance proton exchange membrane fuel cell.

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Mathematical and artificial neural network modeling to predict the heat transfer of mixed convective electroosmotic nanofluid flow with Helmholtz-Smoluchowski velocity and multiple slip effects: An application of soft computing

Khan,

Farooq,

Imran

et al. 2024

Case Studies in Thermal Engineering

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Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning

Cited by 9 publications

References 448 publications

Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review

Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review

Probing the Efficiency of PPMG-Based Composite Electrolytes for Applications of Proton Exchange Membrane Fuel Cell

Mathematical and artificial neural network modeling to predict the heat transfer of mixed convective electroosmotic nanofluid flow with Helmholtz-Smoluchowski velocity and multiple slip effects: An application of soft computing

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