CFP last date
20 June 2024
Reseach Article

ANN and MLR Model of Specific Fuel Consumption for Pyrolysis Oil Blended with Diesel used in a Single Cylinder Diesel Engine: A Comparative Study

by Saumil C Patel, Pragnesh K Brahmbhatt
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 10
Year of Publication: 2015
Authors: Saumil C Patel, Pragnesh K Brahmbhatt
10.5120/20373-2585

Saumil C Patel, Pragnesh K Brahmbhatt . ANN and MLR Model of Specific Fuel Consumption for Pyrolysis Oil Blended with Diesel used in a Single Cylinder Diesel Engine: A Comparative Study. International Journal of Computer Applications. 116, 10 ( April 2015), 22-26. DOI=10.5120/20373-2585

@article{ 10.5120/20373-2585,
author = { Saumil C Patel, Pragnesh K Brahmbhatt },
title = { ANN and MLR Model of Specific Fuel Consumption for Pyrolysis Oil Blended with Diesel used in a Single Cylinder Diesel Engine: A Comparative Study },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 10 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number10/20373-2585/ },
doi = { 10.5120/20373-2585 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:56:44.383488+05:30
%A Saumil C Patel
%A Pragnesh K Brahmbhatt
%T ANN and MLR Model of Specific Fuel Consumption for Pyrolysis Oil Blended with Diesel used in a Single Cylinder Diesel Engine: A Comparative Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 10
%P 22-26
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main objective of the research is to compare the accurateness of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) model for Specific Fuel Consumption for pyrolysis oil blended with diesel used in a single cylinder diesel engine. In this study, parameters i. e. Injection Timing, Injection Pressure, Compression Ratio, and Load are taken. Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) models were prepared using the results of Experiments to predict Specific Fuel Consumption for pyrolysis oil blended with diesel used in a single cylinder diesel engine. The results show that ANN prediction is more accurate than MLR prediction.

References
  1. Besalatpour, A. , Hajabbasi, M. A. , Ayoubi, S. , Afyuni, M. , Jalalian, A. , & Schulin, R. (2012). Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system. Soil Science and Plant Nutrition, 58(2), 149-160.
  2. Brey, T. , Jarre-Teichmann, A. , & Borlich, O. (1996). Artificial neural network versus multiple linear regression prediciting P/B ratios from empirical data. Marine ecology-progress series, 140, 251-256.
  3. Mouhibi, R. , Zahouily, M. , El Akri, K. , & Hanafi, N. (2013). Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas. Open Journal of Medicinal Chemistry, 3, 7.
  4. Balamuruga, G. (2010) PREDICTION OF WORK PIECE HARDNESS USING ARTIFICIAL NEURAL NETWORK. International Journal of Design and Manufacturing Technology (IJDMT), pp. 29-44
  5. Tushar, M. P. , & Bhatt, N. (2013). ANN and MLR Model for Shear Stress Prediction of Eicher 11. 10 Chassis Frame: A Comparative Study. International Journal of Mechanical Engineering & Technology (IJMET), 4(5), 216 - 223.
  6. Ul-Saufie, A. Z. , Yahya, A. S. , Ramli, N. A. , & Hamid, H. A. (2011). Comparison Between Multiple Linear Regression And Feed forward Back propagation Neural Network Models For Predicting PM10 Concentration Level Based On GaseousAndMeteorological arameters. International Journal of Applied, 1(4).
  7. Patel, T. M. , & Bhatt, N. M. (2013). Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11. 10 Chassis Frame. IOSR Journal of Mechanical and Civil Engineering. PP 22-32.
  8. Zahouily, M. , Rhihil, A. , Bazoui, H. , Sebti, S. , & Zakarya, D. (2002). Structure-toxicity relationships study of a series of organophosphorus insecticides. Molecular modeling annual, 8(5), 168-172.
  9. Sahoo, S. , & Jha, M. K. (2013). Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeology Journal, 21(8), 1865-1887.
  10. Gevrey, M. , Lek, S. , & Oberdorff, T. (2006). Utility of sensitivity analysis by artificial neural network models to study patterns of endemic fish species. InEcological Informatics (pp. 293-306). Springer Berlin Heidelberg.
Index Terms

Computer Science
Information Sciences

Keywords

C. I. Engine Pyrolysis Oil SFC ANN MLR