A machine learning-response surface optimization approach to enhance the performance of diesel engine using novel blends of Aloe vera biodiesel with MWCNT nanoparticles and hydrogen

Ahmad, Aqueel; Yadav, Ashok Kumar; Singh, Achhaibar; Singh, Dinesh Kumar

doi:10.1016/j.psep.2024.04.013

Process Safety and Environmental Protection

2024

DOI: 10.1016/j.psep.2024.04.013

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A machine learning-response surface optimization approach to enhance the performance of diesel engine using novel blends of Aloe vera biodiesel with MWCNT nanoparticles and hydrogen

Aqueel Ahmad,

Ashok Kumar Yadav,

Achhaibar Singh

et al.

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Cited by 14 publications

References 43 publications

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Optimization of Thermal Performance in Lauric Acid‐Based Phase Change Materials Using a Priority Clustering Approach

Khan,

Parvez,

Kumari

et al. 2024

Energy Storage

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This study investigates the thermal properties of lauric acid (LA) as a phase change material (PCM) using the K‐Means clustering method to analyze the melting characteristics. This study focuses on the optimization of PCMs using a hybrid methodology of analytic hierarchy process (AHP) and K‐Means clustering. LA, enhanced with zinc oxide (ZnO) nanoparticles, was evaluated for its thermal performance. LA's suitability as a PCM is evaluated based on initial temperature, heating rate, final temperature, and time to melt. AHP was employed to determine the weightage for three critical outcomes: latent heat, melting point, and thermal conductivity. The weightages assigned were 59%, 31%, and 11%, respectively, reflecting the relative importance of each outcome in assessing the efficiency of LA as a PCM. Furthermore, K‐Means clustering was then applied to categorize the data based on these weighted outcomes. AHP was utilized to determine the weightage of input parameters, assigning 27% to initial temperature, 15% to heating rate, and 22% to final temperature, underscoring their significance in the analysis. The optimal input conditions identified were an initial temperature of 24.8°C, a ieating rate of 5.6°C/min, a final temperature of 81.4°C, and a time to melt of 10.6 min. These conditions resulted in optimal outcomes of 208 J/g for latent heat, a melting point of 80.9°C, and a thermal conductivity of 0.21 W/m·K. This hybrid approach provides a robust framework for optimizing PCM performance, facilitating enhanced thermal energy storage and release in practical applications.

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Optimization of Thermal Performance in Lauric Acid‐Based Phase Change Materials Using a Priority Clustering Approach

Khan,

Parvez,

Kumari

et al. 2024

Energy Storage

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Application of an Inline Mixer to Produce Surfactant-Free Biodiesel-diesel/Water Emulsion Fuel: An Analysis of Water Droplets Characteristics and Drive Cycle Emissions

Tamam,

Yahya,

Abdul Rahman

et al. 2024

Arab J Sci Eng

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Assessment of ammonia-diesel fuel blends on compression ignition engine performance and emissions using machine learning techniques

Arivalagan,

Kamarudin,

Ali Alharbi

et al. 2024

Fuel

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A machine learning-response surface optimization approach to enhance the performance of diesel engine using novel blends of Aloe vera biodiesel with MWCNT nanoparticles and hydrogen

Cited by 14 publications

References 43 publications

Optimization of Thermal Performance in Lauric Acid‐Based Phase Change Materials Using a Priority Clustering Approach

Optimization of Thermal Performance in Lauric Acid‐Based Phase Change Materials Using a Priority Clustering Approach

Application of an Inline Mixer to Produce Surfactant-Free Biodiesel-diesel/Water Emulsion Fuel: An Analysis of Water Droplets Characteristics and Drive Cycle Emissions

Assessment of ammonia-diesel fuel blends on compression ignition engine performance and emissions using machine learning techniques

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