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A theoretical and experimental study of glycerol steam reforming over Rh/ MgAl_2O_4 catalysts

机译：Rh / MgAl_2O_4催化剂上甘油蒸汽重整的理论和实验研究

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In this paper, the back propagation artificial neural network (ANN) modeling approach was used for investigating of the steam reforming reaction of glycerol over Rh/MgAl2O4 catalyst in a packed bed tubular reactor. The experimental tests were carried out in order to provide appropriate experimental data set for training the model as well as for evaluating the ANN prediction ability of the catalytic glycerol steam reforming process. In this regard, important parameters in steam reforming process such as gas hourly space velocity (GHSV) between 35,000 and 70,000 ml.h(-1) g(-1), reaction temperature from 300 degrees C to 600 degrees C, water to glycerol ratio (feed ratio) from 3 to 9 and time on stream from 20 to 300 min were studied from experimental as well as modeling outlook. The obtained ANN results were in very good agreement with empirical values. The ANN model predicted the experimental results of glycerol steam reforming with high correlation (R = 0.9995) and very low error (MSE = 1.2317 x 10(-4)) for training data.
机译：本文采用反向传播人工神经网络（ANN）建模方法研究填充床管式反应器中甘油在Rh / MgAl2O4催化剂上的水蒸气重整反应。进行实验测试是为了提供适当的实验数据集，以训练模型以及评估催化甘油蒸汽重整过程的ANN预测能力。在这方面，蒸汽重整过程中的重要参数例如35,000至70,000 ml.h（-1）g（-1）之间的气体时空速（GHSV），反应温度从300摄氏度到600摄氏度，水到甘油从实验和建模方面研究了3到9的比例（进料比）和20到300分钟的生产时间。所获得的人工神经网络结果与经验值非常吻合。 ANN模型预测了甘油蒸汽重整的实验结果，训练数据具有高相关性（R = 0.9995）和非常低的误差（MSE = 1.2317 x 10（-4））。

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