Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm(3)/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000-2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1-3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respectively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions governing the output parameters. The R-2 values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions.

Automated summary

The study explores the use of hydrogen-oxygen gas (HHO) in compression ignition (CI) engines with diesel to improve fuel consumption and thermal efficiency. The combination of Artificial Intelligence (AI) and Response Surface Methodology (RSM) was effective in predicting engine performance and studying statistical interactions. The results demonstrate a substantial decrease in brake-specific fuel consumption (BSFC) and enhanced brake thermal efficiency (BTE) with the addition of HHO, while the ANN and RSM models showed promising accuracy and precision in predictions.