Abstract Catalytic reforming in the presence of metal-acid bifunctional catalysts is a widely used reaction in refinery industry to improve some properties of products like temperature performance of diesel and octane number of gasoline. So the ability of the prediction of Iso-C7 selectivity during n-heptane hyroconversion is a key issue. In this study, a data set which was collected from previous publications are put in an artificial neural network-multi layer perceptron (ANN-MLP) model. Properties used as input parameters are: temperature, pressure, WHSV (weight hourly space velocity), catalysts acidity and pore volume of the catalysts, and Iso-C7 selectivity used as the output parameter. Based on results, the MLP-ANN has great ability to estimate n-heptane hydroconversion. Root mean squared error (RMSE) and R-squared (R2) error were calculated for training, test and total set of data. For training set, test set and total set RMSE are 97915, 5.1607, and 3.9441, respectively and corresponding R2 are 0.97915, 0.9334, and 0.9746, respectively.

中文翻译：

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使用稳健技术估计正庚烷在催化剂上的升级

摘要 金属酸双功能催化剂催化重整是炼油工业广泛应用的反应，以改善柴油的温度性能和汽油的辛烷值等产品性能。因此，在正庚烷氢转化过程中预测 Iso-C7 选择性的能力是一个关键问题。在这项研究中，从以前的出版物中收集的数据集被放入人工神经网络-多层感知器 (ANN-MLP) 模型中。用作输入参数的特性是：温度、压力、WHSV（重时空速）、催化剂酸度和催化剂的孔体积，以及用作输出参数的 Iso-C7 选择性。根据结果​​，MLP-ANN 具有很好的估计正庚烷加氢转化率的能力。计算训练、测试和总数据集的均方根误差 (RMSE) 和 R 平方 (R2) 误差。训练集、测试集和总集RMSE分别为97915、5.1607和3.9441，对应的R2分别为0.97915、0.9334和0.9746。