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Analytical Formulation for Diesel Engine Fueled with Fusel Oil/Diesel Blends

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Title Analytical Formulation for Diesel Engine Fueled with Fusel Oil/Diesel Blends
 
Creator AKÇAY, Mehmet
ÖZER, Salih
SATILMIŞ, Gökhan
 
Subject Artificial intelligence
Diesel engine
Engine performance
Symbolic regression
 
Description 712-719
The experiments related to reduction of gases from the exhaust emissions of internal combustion engines, usually
conducted in laboratory conditions, are quite laborious and costly. For these purposes, modelling engine experiments with
algorithms have emerged as a way forward. In this paper, the operation of diesel engine is modelled through experimental
dataset, which has input variables such as engine load, fuel type and output variables such as carbon monoxide (CO), carbon
dioxide (CO2), oxides of nitrogen (NOx), hydrocarbon (HC), smoke, Brake Specific Energy Consumption (BSEC) and
maximum in-cylinder pressure (Cpmax). Artificial intelligence based Symbolic Regression (SR) algorithms have been used to
derive analytical equations of each output variable. The derived equations and experimental results are plotted on the same
graph to show the accuracy of the obtained equations. The coefficient of determination (R2) is between 0.98 and 0.99 in all
equations. In addition, Mean Error Percentage (MEP) value is less than 10 in all equations. The performance of SR
algorithms is compared with Artificial Neural Network (ANN), Support Vector Machines (SVM), instance-based and K
nearest based classifier (IBk), ensemble method-based bagging algorithm, and decision tree-based REPTree algorithms. SR
algorithms exhibit the best performance for all output variables. IBk algorithm exhibits the second-best performance for the
BSEC, CO, CO2, HC and NOx output variable. SVM algorithm exhibits the second-best performance for the Cpmax output
variable and Bagging algorithms exhibits the second-best performance for the smoke output variable. The operation of
diesel engine can be predicted using these equations and algorithms for further research.
 
Date 2022-07-06T07:07:01Z
2022-07-06T07:07:01Z
2022-07
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscpr.res.in/handle/123456789/60060
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(07) [July 2022]