Artificial intelligence based modelling and hybrid optimization of linseed oil biodiesel with graphene nanoparticles to stringent biomedical safety and environmental standards

Rao，Papabathina Mastan1; Dhoria，Sneha Haresh2; Patro，S. Gopal Krishna3; Gopidesi，Radha Krishna4; Alkahtani，Meshel Q.5; Islam，Saiful5; Vijaya，Murkonda2; Jayanthi，Juturi Lakshmi6; Khan，Mohammad Amir7; Razak，Abdul8; Kumar，Raman9; Rizal，Achmad10; Ammarullah，Muhammad Imam11,12,13

2023-11-01

10.1016/j.csite.2023.103554

Case Studies in Thermal Engineering   影响因子和分区

2214-157X

In this work, experiments were carried out in line with Design of Experiments (DOE) standards to assess the performance and emission features of 5% graphene nanoparticles added linseed biodiesel. The engine was operated with the blends of B10, B20, and B30 with 5% graphene nano additives (designated as B10G5, B20G5, and B30G5). To find the parameter's optimum values, the Desirability Function approach (DFA), Swarm Salp single objective, Multi Objective Bat algorithm (MOBA), Response surface methodology (RSM) and D-optimal design approach were employed. Advanced machine learning (ML) techniques were employed to anticipate these characteristics. It was found that B20G5 had a better brake thermal efficiency (BTE), when compared to the other samples (and around 11% higher than diesel fuel at full load). The emissions of Carbon monoxide (CO) and Hydrocarbon (HC) were lower for B20G5 blended fuel than for diesel (Around 23.52% lower than diesel). In comparison to Response surface methodology (RSM), the overall coefficient of determination (R) value using Artificial Neural Network (ANN) for was high. As a result, it was revealed that the ANN was typically better than the RSM in forecasting the various factors affecting the engine performance. The optimum outcomes were achieved by single objective (Salp Swarm algorithm) and multi-objective algorithms. According to multi-objective algorithm, a B20G5 nano additive biodiesel mix at its maximum Brake power (BP) produced the highest value of BTE with the lowest Nitrogen Oxides (NOx) emissions. The comparison shows that B20G5 can be used easily without making any modifications to engines.

ANN Emissions Graphene nano additive Linseed biodiesel RSM Transesterification

SCI

英语

其他

Deanship of Scientific Research at King Khalid University[RGP2/556/44]

Thermodynamics

Thermodynamics

WOS:001090993300001

ELSEVIER

2-s2.0-85173445570

Scopus

1.Department of Mechanical Engineering,PVP Siddhartha Institute of Technology,Vijayawada,Andhra Pradesh,India
2.Department of Mechanical Engineering,R.V.R & J.C College of Engineering,Guntur,Andhra Pradesh,India
3.Department of Computer Engineering & Applications,Institute of Engineering & Technology,GLA University,Mathura,India
4.Department of Mechanical Engineering,Vignan's Lara Institute of Technology and Science,Guntur,Andhra Pradesh,India
5.Civil Engineering Department,College of Engineering,King Khalid University,Abha,Asir,61421,Saudi Arabia
6.Department of Chemical Engineering,R.V.R&J.C College of Engineering,Guntur,Andhra Pradesh,India
7.Department of Civil Engineering,Galgotias College of Engineering,Greater Noida,Knowledge Park I, Uttar Pradesh,201310,India
8.Department of Mechanical Engineering,P. A. College of Engineering (Affiliated to Visvesvaraya Technological University,Belagavi),Mangaluru,India
9.Department of Mechanical and Production Engineering,Guru Nanak Dev Engineering College,Ludhiana,Punjab,141006,India
10.School of Electrical Engineering,Telkom University,Bandung,West Java,40257,Indonesia
11.Department of Mechanics and Aerospace Engineering,College of Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
12.Biomechanics and Biomedics Engineering Research Centre,Universitas Pasundan,Bandung,West Java,40153,Indonesia
13.Undip Biomechanics Engineering & Research Centre (UBM-ERC),Universitas Diponegoro,Semarang,Central Java,50275,Indonesia

Rao，Papabathina Mastan,Dhoria，Sneha Haresh,Patro，S. Gopal Krishna,et al. Artificial intelligence based modelling and hybrid optimization of linseed oil biodiesel with graphene nanoparticles to stringent biomedical safety and environmental standards[J]. Case Studies in Thermal Engineering,2023,51.

Rao，Papabathina Mastan.,Dhoria，Sneha Haresh.,Patro，S. Gopal Krishna.,Gopidesi，Radha Krishna.,Alkahtani，Meshel Q..,...&Ammarullah，Muhammad Imam.(2023).Artificial intelligence based modelling and hybrid optimization of linseed oil biodiesel with graphene nanoparticles to stringent biomedical safety and environmental standards.Case Studies in Thermal Engineering,51.

Rao，Papabathina Mastan,et al."Artificial intelligence based modelling and hybrid optimization of linseed oil biodiesel with graphene nanoparticles to stringent biomedical safety and environmental standards".Case Studies in Thermal Engineering 51(2023).