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    Experimental investigation and performance prediction of gasoline engine operating parameters fueled with diisopropyl ether-gasoline blends: Response surface methodology based optimization
    (Elsevier Ltd., 2022) Sathyanarayanan, Seetharaman; Suresh, Sivan; Saravanan, C. G; Vikneswaran, M.; Dhamodaran, Gopinath; Sonthalia, Ankit; Joseph Shobana Bai, Femilda Josephin; Varuvel, Edwin Geo
    In this research, gasoline engine performance and emission characteristics were studied when powered by diisopropyl ether-gasoline blends. The main objective of this study is to determine the behavior of diisopropyl ether-gasoline blends at various engine speeds and compression ratios. Further, the engine parameters were optimized using the response surface methodology. Enriched oxygen, higher latent heat of vaporization, and the readily volatile nature of the fuel enhanced the brake thermal efficiency and lowered the hydrocarbons and carbon monoxide due to a better combustion rate. The developed model exhibited superior R2 values with a 0.957 desirability factor. The optimum parameters such as speed, compression ratio, and fuel-blend concentrations were found at 2250 rpm, 10:1, and D25 (75% gasoline and 25% diisopropyl ether), respectively. The responses for the optimal input parameters were brake thermal efficiency (31.53%), specific fuel consumption (0.2923 kg/kWh), carbon monoxide (0.14% by Vol.), hydrocarbons (31 ppm), and oxides of nitrogen (708 ppm). The predicted values for optimum engine parameters were validated with the experimental data, and their percentage of absolute error was found to be less than 5%. Thus, the study concludes that diisopropyl-ether gasoline blends can be used as an alternative fuel to enhance the brake thermal efficiency and reduce the pollution level, and the proposed numerical model can predict the responses with high accuracy.
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    Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: a combined application of artificial neural network and response surface methodology
    (Pergamon-elsevier science, 2024) Seetharaman, Sathyanarayanan; Suresh, S.; Shivaranjani, R. S.; Dhamodaran, Gopinath; Bai, Femilda Josephin Joseph Shobana; Alharbi, Sulaiman Ali; Pugazhendhi, Arivalagan; Varuvel, Edwin Geo
    This research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R 2 between 0.99426 and 0.9998 was observed by ANN whereas R 2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN 's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.