Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm
Nowadays, due to stricter pollution standards, more attention has been focused on pollutants emitted from cars. As a very dangerous pollutant, NOx has always triggered the sensitivity of the related organizations. In the process of developing and designing the engine, estimating the amount of this p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023025112 |
| _version_ | 1797836975857729536 |
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| author | Mansour Keshavarzzadeh Rahim Zahedi Reza Eskandarpanah Sajad Qezelbigloo Siavash Gitifar Omid Noudeh Farahani Amir Mohammad Mirzaei |
| author_facet | Mansour Keshavarzzadeh Rahim Zahedi Reza Eskandarpanah Sajad Qezelbigloo Siavash Gitifar Omid Noudeh Farahani Amir Mohammad Mirzaei |
| author_sort | Mansour Keshavarzzadeh |
| collection | DOAJ |
| description | Nowadays, due to stricter pollution standards, more attention has been focused on pollutants emitted from cars. As a very dangerous pollutant, NOx has always triggered the sensitivity of the related organizations. In the process of developing and designing the engine, estimating the amount of this pollutant is of great importance to reduce future expenses. Calculating the amount of this pollutant has usually been complicated and prone to error. In the present paper, neural networks have been used to find the coefficients of correcting NOx calculation. The Zeldovich method calculated the value of NOx with 20% error. By applying the progressive neural network and correcting the equation coefficient, this value decreased. The related model has been validated with other fuel equivalence ratios. The neural network model has fitted the experimental points with a convergence ratio of 0.99 and a squared error of 0.0019. Finally, the value of NOx anticipated by the neural network has been calculated and validated according to empirical data by applying maximum genetic algorithm. The maximum point for the fuel composed of 20% hydrogen and 80% methane occurred in the equivalence ratio of 0.9; and the maximum point for the fuel composed of 40% hydrogen occurred in equivalence ratio of 0.92. The consistency of the model findings with the empirical data shows the potential of the neural network in anticipating the amount of NOx. |
| first_indexed | 2024-04-09T15:17:33Z |
| format | Article |
| id | doaj.art-c2c82c12f82b4eaf9e7bfd03c17e30e5 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-09T15:17:33Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-c2c82c12f82b4eaf9e7bfd03c17e30e52023-04-29T14:56:09ZengElsevierHeliyon2405-84402023-04-0194e15304Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithmMansour Keshavarzzadeh0Rahim Zahedi1Reza Eskandarpanah2Sajad Qezelbigloo3Siavash Gitifar4Omid Noudeh Farahani5Amir Mohammad Mirzaei6Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South AfricaDepartment of Renewable Energy and Environmental Engineering, University of Tehran, Tehran, Iran; Corresponding author.Department of Energy Systems Engineering, Islamic Azad University, Tehran, IranSchool of Automotive Engineering, Iran University of Science and Technology, Tehran, IranFaculty of Mechanical Engineering, Iran University of Science and Technology, Tehran, IranFaculty of Computer Engineering and Information Technology, Faran Mehr Danesh University, Tehran, IranFaculty of Materials Engineering, Tarbiat Modares University, Tehran, IranNowadays, due to stricter pollution standards, more attention has been focused on pollutants emitted from cars. As a very dangerous pollutant, NOx has always triggered the sensitivity of the related organizations. In the process of developing and designing the engine, estimating the amount of this pollutant is of great importance to reduce future expenses. Calculating the amount of this pollutant has usually been complicated and prone to error. In the present paper, neural networks have been used to find the coefficients of correcting NOx calculation. The Zeldovich method calculated the value of NOx with 20% error. By applying the progressive neural network and correcting the equation coefficient, this value decreased. The related model has been validated with other fuel equivalence ratios. The neural network model has fitted the experimental points with a convergence ratio of 0.99 and a squared error of 0.0019. Finally, the value of NOx anticipated by the neural network has been calculated and validated according to empirical data by applying maximum genetic algorithm. The maximum point for the fuel composed of 20% hydrogen and 80% methane occurred in the equivalence ratio of 0.9; and the maximum point for the fuel composed of 40% hydrogen occurred in equivalence ratio of 0.92. The consistency of the model findings with the empirical data shows the potential of the neural network in anticipating the amount of NOx.http://www.sciencedirect.com/science/article/pii/S2405844023025112Internal combustion engineNOxNeural networkGenetic algorithm |
| spellingShingle | Mansour Keshavarzzadeh Rahim Zahedi Reza Eskandarpanah Sajad Qezelbigloo Siavash Gitifar Omid Noudeh Farahani Amir Mohammad Mirzaei Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm Heliyon Internal combustion engine NOx Neural network Genetic algorithm |
| title | Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm |
| title_full | Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm |
| title_fullStr | Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm |
| title_full_unstemmed | Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm |
| title_short | Estimation of NOx pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm |
| title_sort | estimation of nox pollutants in a spark engine fueled by mixed methane and hydrogen using neural networks and genetic algorithm |
| topic | Internal combustion engine NOx Neural network Genetic algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023025112 |
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