Keshavarzzadeh Mansour, Zahedi Rahim, Eskandarpanah Reza, Qezelbigloo Sajad, Gitifar Siavash, Farahani Omid Noudeh, Mirzaei Amir Mohammad
Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa.
Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran, Iran.
Heliyon. 2023 Apr 7;9(4):e15304. doi: 10.1016/j.heliyon.2023.e15304. eCollection 2023 Apr.
Nowadays, due to stricter pollution standards, more attention has been focused on pollutants emitted from cars. As a very dangerous pollutant, NO 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 NO calculation. The Zeldovich method calculated the value of NO 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 NO 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 NO.
如今，由于污染标准更加严格，汽车排放的污染物受到了更多关注。作为一种非常危险的污染物，一氧化氮一直引发相关组织的关注。在发动机的开发和设计过程中，估算这种污染物的排放量对于降低未来成本至关重要。计算这种污染物的排放量通常很复杂且容易出错。在本文中，神经网络已被用于寻找校正一氧化氮计算的系数。泽尔多维奇方法计算出的一氧化氮值误差为20%。通过应用渐进神经网络并校正方程系数，该值降低了。相关模型已用其他燃料当量比进行了验证。神经网络模型以0.99的收敛率和0.0019的平方误差拟合了实验点。最后，通过应用最大遗传算法，根据经验数据计算并验证了神经网络预测的一氧化氮值。由20%氢气和80%甲烷组成的燃料的最大值出现在当量比为0.9时；由40%氢气组成的燃料的最大值出现在当量比为0.92时。模型结果与经验数据的一致性表明了神经网络在预测一氧化氮排放量方面的潜力。
