scholarly journals Artificial Neural Network Modeling and Numerical Simulation of Syngas Fuel and Injection Timing Effects on the Performance and Emissions of a Heavy-Duty Compression Ignition Engine

ACS Omega ◽  
2021 ◽  
Author(s):  
Saeed Foroutani ◽  
Gholamreza Salehi ◽  
Hossein Fallahsohi ◽  
Kamran Lary ◽  
Afshin Mohseni Arasteh
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Injection Timing ◽  
Compression Ignition ◽  
Heavy Duty ◽  
Compression Ignition Engine ◽  
Artificial Neural Network Modeling ◽  
Performance And Emissions ◽  
Timing Effects

scholarly journals Artificial Neural Network Modeling and Sensitivity Analysis of Performance and Emissions in a Compression Ignition Engine Using Biodiesel Fuel

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2410 ◽  
Author(s):  
Farzad Jaliliantabar ◽  
Barat Ghobadian ◽  
Gholamhassan Najafi ◽  
Talal Yusaf
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Research Work ◽  
Neural Network Modeling ◽  
Biodiesel Fuel ◽  
Compression Ignition ◽  
Compression Ignition Engine ◽  
Performance And Emissions ◽  
Artificial Neural

In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.

scholarly journals Heat induction in two-dimensional graphene–Fe3O4 nanohybrids for magnetic hyperthermia applications with artificial neural network modeling

RSC Advances ◽  
2021 ◽  
Vol 11 (35) ◽  
pp. 21702-21715
Author(s):  
M. S. Dar ◽  
Khush Bakhat Akram ◽  
Ayesha Sohail ◽  
Fatima Arif ◽  
Fatemeh Zabihi ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Artificial Neural Network ◽  
Network Modeling ◽  
Magnetic Hyperthermia ◽  
Alternating Magnetic Field ◽  
Neural Network Modeling ◽  
Two Dimensional ◽  
Artificial Neural Network Modeling ◽  
Heat Induction

Synthesis of Fe3O4–graphene (FG) nanohybrids and magnetothermal measurements of FxG100–x (x = 0, 25, 45, 65, 75, 85, 100) nanohybrids (25 mg each) at a 633 kHz alternating magnetic field of strength 9.1 mT.

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