Spark‐assisted HCCI engine using hydrous methanol as a fuel: an ANN approach

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