Impact of Soybean Biodiesel Blends with Mixed Graphene Nanoparticles on Compression Ignition Engine Performance and Emission: An Experimental and ANN Analysis

Prakash M. Kadam; Dhananjay R. Dolas; Sagnik Pal; Sameer S. Gajghate

doi:10.15282/ijame.21.3.2024.5.0888

Authors

Prakash M. Kadam Mechanical Engineering Department, Jawaharlal Nehru Engineering College, MGM University Aurangabad, 431003 Maharashtra, India
Dhananjay R. Dolas Mechanical Engineering Department, Jawaharlal Nehru Engineering College, MGM University Aurangabad, 431003 Maharashtra, India
Sagnik Pal Mechanical Engineering Department, National Institute of Technology Agartala, 799046 Tripura, India https://orcid.org/0000-0001-8923-3158
Sameer S. Gajghate Faculty Mechanical Engineering Department, G H Raisoni College of Engineering and Management, Pune, 412207 Maharashtra, India https://orcid.org/0000-0002-8781-7122

DOI:

https://doi.org/10.15282/ijame.21.3.2024.5.0888

Keywords:

Graphene nanoplatelets, Diesel engine, Soybean, Performance, Emission

Abstract

The extensive use of fuels in power generation plants, industries, and transportation has led to a scarcity of fossil fuels and has contributed to global warming. This has prompted researchers to focus on improving internal combustion engine performance, as the transportation system accounts for 50% of global fuel consumption. Soybean biodiesel plays a vital role in reducing emissions and fuel consumption in engines. In the current work, a blend of soybean oil is used as a biodiesel (B20, B40, and B60), with and without the addition of graphene nanoplatelets (GNPs) examined on a constant-speed compression ignition (CI) engine with respect to pure diesel. The blends are denoted as B20GNP10, B20GNP20, B20GNP40, B20GNP60, B20GNP80, and B20GNP100, according to their proposition of biodiesel and graphene nanoplatelets. Similar blends were prepared for B40 and B60 combinations with similar GNPs concentrations. The prepared blend properties were measured, and good thermophysical properties were found. The trial includes testing the engine emissions and performance at altering loads ranging from 0 to 12 kg for all blends. The artificial neural network (ANN) tool is used to forecast the accuracy of experimental results. Compared to pure diesel, the B60GNP100 blend at 12 kg load condition showed the lowest brake specific fuel consumption at 12.58 % and the highest brake thermal efficiency at 27.13%. Emissions were estimated using a gas analyzer, and the outcomes indicated that the biodiesel blends have controlled levels of CO, CO₂, NOx, and HC compared to pure diesel. The ANN model with 99.99% accuracy was developed using experimental data, confirming the accuracy of the experimental results with lower simulation time and cost. Additionally, the B60GNP100 blend yielded better results compared to previous studies.

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