Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

• Main Authors: Muhammad Usman, Haris Hussain, Fahid Riaz, Muneeb Irshad, Rehmat Bashir, Muhammad Haris Shah, Adeel Ahmad Zafar, Usman Bashir, M. A. Kalam, M. A. Mujtaba, Manzoore Elahi M. Soudagar
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Sustainability
• Subjects: diesel; oxyhydrogen; artificial neural network; response surface methodology; prediction; desirability
• Online Access: https://www.mdpi.com/2071-1050/13/16/9373

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 dm³/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 R² 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.