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Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

Author

Listed:
  • 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)

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.

Suggested Citation

  • 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, 2021. "Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine," Sustainability, MDPI, vol. 13(16), pages 1-24, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9373-:d:618504
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    References listed on IDEAS

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    1. K. M. V. Ravi Teja & P. Issac Prasad & K. Vijaya Kumar Reddy & N. R. Banapurmath & Manzoore Elahi M. Soudagar & Nazia Hossain & Asif Afzal & C Ahamed Saleel, 2021. "Comparative Analysis of Performance, Emission, and Combustion Characteristics of a Common Rail Direct Injection Diesel Engine Powered with Three Different Biodiesel Blends," Energies, MDPI, vol. 14(18), pages 1-19, September.
    2. K. M. Akkoli & N. R. Banapurmath & Suresh G & Manzoore Elahi M. Soudagar & T. M. Yunus Khan & Maughal Ahmed Ali Baig & M. A. Mujtaba & Nazia Hossain & Kiran Shahapurkar & Ashraf Elfasakhany & Mishal A, 2021. "Effect of Producer Gas from Redgram Stalk and Combustion Chamber Types on the Emission and Performance Characteristics of Diesel Engine," Energies, MDPI, vol. 14(18), pages 1-17, September.
    3. Sharma, Prabhakar & Bora, Bhaskor J., 2023. "Modeling and optimization of a CI engine running on producer gas fortified with oxyhydrogen," Energy, Elsevier, vol. 270(C).
    4. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.

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