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The present study aims the prediction of CI engine operating parameters using Niger seed oil methyl ester blends and hydrogen in dual-fuel operation with artificial neural networks (ANNs) technique. The standard diesel, NSOME10, NSOME20 and NSOME40 were taken as test flues at diverse compression ratios of 16, 17.5 and 18.5 and different loads of 29.43, 58.86, 88.29 and 117.72 N. Further, the B20 was enriched with hydrogen at different flow rates of 5, 10 and 15 lpm. A back-propagation ANN algorithm is used to predict the experimental data. The fuel blend, compression ratio, engine load and flow rate of hydrogen be taken as input values while the performance, combustion, emission, vibration and noise parameters are used as target values in ANNs. The correlation coefficient ( R2 ) value for all the test run conditions is above 0.95 signifying superior vigorous between the experimental and ANNs predicted data.

international journal of ambient energy

Artificial neural networks applications in prediction of performance, combustion, emissions, vibrations and noise parameters of VCR diesel engine using Niger seed oil methyl ester blends and hydrogen in dual fuel mode