Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine
نویسندگان
چکیده
The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. gloomy peril inflated demand and depleting fuel reservoirs in sector supposedly instigated urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) compression ignition (CI) engines various flow rates with diesel assessing brake-specific consumption (BSFC) brake thermal efficiency (BTE). enrichment neat 10 dm3/min HHO resulted most substantial decrease BSFC improved BTE at all test speeds range 1000–2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed designing ANN performance-predicting model engine operating on HHO. correlation coefficients (R) given predicting were 0.99764 0.99902, respectively. mean root errors (MRE) both parameters (BSFC BTE) within 1–3% while square (RMSE) 0.0122 kg/kWh 0.2768% BTE, In addition, coupled response surface methodology (RSM) technique comprehending individual impact design their statistical interactions governing output parameters. R2 values RSM responses near 1 MRE designated range. comparative evaluation models revealed that RMSE are also well desired but be outrightly accurate precise, choice should potentially endorsed. Thus, combined use could used effectively predictions effective study interactions.
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ژورنال
عنوان ژورنال: Sustainability
سال: 2021
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su13169373