Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

Muhammad Usman, Haris Hussain, Fahid Riaz, Muneeb Irshad, Rehmat Bashir, Muhammad Haris Shah, Adeel Ahmad Zafar, Usman Bashir, M. A. Kalam, M. A. Mujtaba and Manzoore Elahi M. Soudagar
Additional contact information
Muhammad Usman: Department of Mechanical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Haris Hussain: Department of Mechanical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Fahid Riaz: Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore
Muneeb Irshad: Department of Physics, University of Engineering and Technology Lahore, Lahore 54890, Pakistan
Rehmat Bashir: Department of Mechanical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Muhammad Haris Shah: Department of Mechanical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Adeel Ahmad Zafar: Department of Mechanical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Usman Bashir: Department of Mechanical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
M. A. Kalam: Center for Energy Science, Department of Mechanical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
M. A. Mujtaba: Department of Mechanical Engineering, New Campus, University of Engineering and Technology, Lahore 54890, Pakistan
Manzoore Elahi M. Soudagar: Department of Mechanical Engineering, School of Technology, Glocal University, Delhi-Yamunotri Marg, SH-57, Mirzapur Pole, Saharanpur 247121, Uttar Pradesh, India

Sustainability, 2021, vol. 13, issue 16, 1-24

Abstract: 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.

Keywords: diesel; oxyhydrogen; artificial neural network; response surface methodology; prediction; desirability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/16/9373/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/16/9373/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:16:p:9373-:d:618504

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().