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Spark‐assisted HCCI engine using hydrous methanol as a fuel: an ANN approach
Author(s) -
Muthu Venkatesan,
NavaneethaKrishnan Shenbaga Vinayaga Moorthi,
Panimayam Arul Franco,
Ramaiya Karthikeyan,
AyyaSamy Manivannan
Publication year - 2015
Publication title -
biofuels, bioproducts and biorefining
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.931
H-Index - 83
eISSN - 1932-1031
pISSN - 1932-104X
DOI - 10.1002/bbb.1555
Subject(s) - nox , mean effective pressure , homogeneous charge compression ignition , automotive engineering , methanol , thermal efficiency , ignition system , turbocharger , brake specific fuel consumption , brake , materials science , diesel fuel , nuclear engineering , chemistry , analytical chemistry (journal) , environmental science , combustion , thermodynamics , compression ratio , engineering , physics , internal combustion engine , organic chemistry , combustion chamber , turbine
Abstract The main purpose of the experiment is to investigate the performance and emission characteristics of a hydrous methanol (85% methanol and 15% water) fueled homogeneous charge compression ignition ( HCCI ) engine through various spark timings ( SPT ) (32°, 34°, 36°, 38° and 40° before Top Dead Centre). In this study a spark plug is used for assisting auto‐ignition. Experimental investigation reveals that the brake thermal efficiency ( BTE ) of an HCCI engine increases when the SPT is increased and a maximum BTE of 28.5% was obtained for 40° SPT . Emission analysis reveals a significant decrease in nitrogen oxides ( NOx ) as there are slightly higher emissions of hydrocarbon ( HC ) and carbon monoxide ( CO ). An artificial neural network ( ANN ) was developed with brake mean effective pressure ( BMEP ), SPT , and energy share as the input data and BTE , NOx , HC , CO , and rate of pressure rise as output data. In this prediction technique, about 80% of experimental data obtained were used in training and 20% of data were used to test the model developed. The performance in the developed ANN models was compared with experimental data, and statistically evaluated; they are seen to have good agreement. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd