Fuel cell systems (FCSs) have been widely used for niche applications in the market. Furthermore, the research community has worked on using FCSs for different sectors, such as transportation, stationary power generation, marine and maritime, aerospace, military and defense, telecommunications, and material handling. The reformation of various fuels, such as methanol, methane, and diesel can be utilized to generate hydrogen for FCSs. This study introduces an advanced convolutional neural network (CNN) model designed to accurately forecast hydrogen yield and carbon monoxide volume percentages during the reformation processes of methane, methanol, and diesel. Moreover, the CNN model has been tailored to accurately estimate methane conversion rates in methane reforming processes. The proposed CNN models are created by combining the 3D-CNN and 2D-CNN models. The Keras Tuner approach in Python is employed in this study to find the ideal values for different hyperparameters such as batch size, learning rate, time steps, and optimization method selection. The accuracy of the proposed CNN model is evaluated by using the root mean square error (RMSE), mean absolute percentage error (MAE), mean absolute error (MAE), and R2. The results indicate that the proposed CNN model is better than other artificial intelligence (AI) techniques and standard CNN for performance estimation of reforming processes of methane, diesel, and methanol. The results also show that the suggested CNN model can be used to accurately estimate critical output parameters for reforming various fuels. The proposed method performs better in CO prediction than the support vector machine (SVM), with an R2 of 0.9989 against 0.9827. This novel methodology not only improves performance estimation for reforming processes but also provides a valuable tool for accurately estimating output parameters across various fuel types.

中文翻译：

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用于燃料电池系统优化和甲烷、甲醇和柴油重整效率的先进卷积神经网络建模


燃料电池系统（FCS）已广泛应用于市场上的利基应用。此外，研究界还致力于将FCS应用于不同领域，例如运输、固定发电、海洋和海事、航空航天、军事和国防、电信和物料搬运。甲醇、甲烷和柴油等各种燃料的重整可用于生产燃料电池系统所需的氢气。本研究引入了先进的卷积神经网络 (CNN) 模型，旨在准确预测甲烷、甲醇和柴油重整过程中的氢气产量和一氧化碳体积百分比。此外，CNN 模型经过专门设计，可以准确估计甲烷重整过程中的甲烷转化率。所提出的 CNN 模型是通过结合 3D-CNN 和 2D-​​CNN 模型创建的。本研究采用 Python 中的 Keras Tuner 方法来查找不同超参数（例如批量大小、学习率、时间步长和优化方法选择）的理想值。使用均方根误差 (RMSE)、平均绝对百分比误差 (MAE)、平均绝对误差 (MAE) 和 R2 来评估所提出的 CNN 模型的准确性。结果表明，所提出的 CNN 模型在甲烷、柴油和甲醇重整过程的性能估计方面优于其他人工智能 (AI) 技术和标准 CNN。结果还表明，所提出的 CNN 模型可用于准确估计重整各种燃料的关键输出参数。所提出的方法在 CO 预测方面比支持向量机 (SVM) 表现更好，R2 为 0.9989，而 R2 为 0.9827。 这种新颖的方法不仅改进了重整过程的性能估计，而且还为准确估计各种燃料类型的输出参数提供了宝贵的工具。