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首页> 外文期刊>Journal of industrial and engineering chemistry >Comparison of RSM and ANN for the investigation of linear alkylbenzene synthesis over H_(14)[NaP5W_(30)O_(110)]/SiO2 catalyst
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Comparison of RSM and ANN for the investigation of linear alkylbenzene synthesis over H_(14)[NaP5W_(30)O_(110)]/SiO2 catalyst

机译:H_(14)[NaP5W_(30)O_(110)] / SiO2催化剂上线性烷基苯合成的RSM与ANN的比较

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摘要

Design of experiments (DOE) and artificial neural networks (ANNs) were successfully applied for studying the operating parameters of benzene alkylation with 1-decene over H_(14)[NaP5W_(30)O_(110)]/SiO2 catalyst. In this reaction catalyst loading, catalyst weight percent and benzene to 1-decene molar ratio (Bz/C_(10)) were chosen as independent variables in experimental design. Prediction of 1-decene conversion and 2-phenyldecane selectivity was performed applying response surface method (RSM) and ANN models. Final selected multi-layer (3-6-2-2) ANN model resulted a coefficient of determination (R~2) of 0.95 for 1-decene conversion and 0.99 for 2-phenyldecane selectivity, while the R~2 of RSM was 0.93 and 0.92 for these two parameters.
机译:实验设计(DOE)和人工神经网络(ANN)成功地用于研究在H_(14)[NaP5W_(30)O_(110)] / SiO2催化剂上1-癸烯进行苯烷基化的操作参数。在该反应催化剂负载量中,在实验设计中选择催化剂重量百分比和苯与1-癸烯的摩尔比(Bz / C_(10))作为自变量。使用响应面法(RSM)和ANN模型对1-癸烯转化率和2-苯基癸烷选择性进行了预测。最终选择的多层(3-6-2-2)ANN模型得出1-癸烯转化率的测定系数(R〜2)为0.95,2-苯基癸烷选择性为0.99,而RSM的R〜2为0.93而这两个参数为0.92。

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