{"title":"基于人工神经网络和Box-Behnken设计的mo1v 0.3 Te 0.23 NB 0.12 O X催化剂上丙烷选择性氧化制丙烯酸的建模与优化","authors":"Golshan Mazloom","doi":"10.3329/cerb.v21i1.47368","DOIUrl":null,"url":null,"abstract":"The prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for propane selective oxidation to acrylic acid (AA) over Mo1V0.3Te0.23Nb0.12Ox catalyst was investigated in this work. 15 experimental runs based on the Box-Behnken design (BBD) were employed to study the effects of temperature (380 to 500 °C), superficial velocity (33.3 to 66.7 mL (min gcat)-1), (O2)/(C3H8) ratio (1 to 3) and their interactions on propane conversion, AA selectivity and COx selectivity. The quadratic polynomial BBD equations and the feed-forward back propagation ANN models were developed based on the designed experimental data. Statistical analysis; coefficient of determination (R2), mean absolute error (MAE) and analysis of variance (ANOVA) illustrated that there was acceptable adjustment between BBD and ANN predicted responses as compared to experimental data. While, the ANN model showed a clear preference and generalization capability over BBD model in the case of experimental data set which were not used to training the models. In addition the optimum conditions were found to be temperature (461.7 °C), GHSV (51.9 mL (min gcat)-1) and (O2)/(C3H8) ratio (2.1) which were determined by desirability function approach. In these conditions, propane conversion of 15.2%, AA selectivity of 32% and COx selectivity of 44% which obtained experimentally were in reasonable agreement with predicted responses. \nChemical Engineering Research Bulletin 21(2019) 1-19","PeriodicalId":9756,"journal":{"name":"Chemical Engineering Research Bulletin","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Modeling and Optimization of Propane Selective Oxidation to Acrylic Acid Over Mo 1 V 0.3 Te 0.23 NB 0.12 O X Catalyst Using Artificial Neural Network and Box-Behnken Design\",\"authors\":\"Golshan Mazloom\",\"doi\":\"10.3329/cerb.v21i1.47368\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for propane selective oxidation to acrylic acid (AA) over Mo1V0.3Te0.23Nb0.12Ox catalyst was investigated in this work. 15 experimental runs based on the Box-Behnken design (BBD) were employed to study the effects of temperature (380 to 500 °C), superficial velocity (33.3 to 66.7 mL (min gcat)-1), (O2)/(C3H8) ratio (1 to 3) and their interactions on propane conversion, AA selectivity and COx selectivity. The quadratic polynomial BBD equations and the feed-forward back propagation ANN models were developed based on the designed experimental data. Statistical analysis; coefficient of determination (R2), mean absolute error (MAE) and analysis of variance (ANOVA) illustrated that there was acceptable adjustment between BBD and ANN predicted responses as compared to experimental data. While, the ANN model showed a clear preference and generalization capability over BBD model in the case of experimental data set which were not used to training the models. In addition the optimum conditions were found to be temperature (461.7 °C), GHSV (51.9 mL (min gcat)-1) and (O2)/(C3H8) ratio (2.1) which were determined by desirability function approach. In these conditions, propane conversion of 15.2%, AA selectivity of 32% and COx selectivity of 44% which obtained experimentally were in reasonable agreement with predicted responses. \\nChemical Engineering Research Bulletin 21(2019) 1-19\",\"PeriodicalId\":9756,\"journal\":{\"name\":\"Chemical Engineering Research Bulletin\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemical Engineering Research Bulletin\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3329/cerb.v21i1.47368\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemical Engineering Research Bulletin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3329/cerb.v21i1.47368","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
研究了响应面法(RSM)和人工神经网络(ANN)模型在Mo1V0.3Te0.23Nb0.12Ox催化剂上对丙烷选择性氧化制丙烯酸(AA)的预测能力。基于Box-Behnken设计(BBD)进行了15次实验,研究了温度(380 ~ 500℃)、表面流速(33.3 ~ 66.7 mL (min gcat)-1)、(O2)/(C3H8)比(1∶3)及其相互作用对丙烷转化率、AA选择性和COx选择性的影响。基于设计的实验数据,建立了二次多项式BBD方程和前馈反传播神经网络模型。统计分析;决定系数(R2)、平均绝对误差(MAE)和方差分析(ANOVA)表明,与实验数据相比,BBD和ANN预测反应之间存在可接受的调整。而在不使用实验数据集训练模型的情况下,ANN模型比BBD模型表现出明显的偏好和泛化能力。最佳条件为温度(461.7°C)、GHSV (51.9 mL (min gcat)-1)和(O2)/(C3H8)比(2.1)。在此条件下,丙烷转化率为15.2%,AA选择性为32%,COx选择性为44%,实验结果与预测结果基本一致。化工研究通报21(2019)1-19
Modeling and Optimization of Propane Selective Oxidation to Acrylic Acid Over Mo 1 V 0.3 Te 0.23 NB 0.12 O X Catalyst Using Artificial Neural Network and Box-Behnken Design
The prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for propane selective oxidation to acrylic acid (AA) over Mo1V0.3Te0.23Nb0.12Ox catalyst was investigated in this work. 15 experimental runs based on the Box-Behnken design (BBD) were employed to study the effects of temperature (380 to 500 °C), superficial velocity (33.3 to 66.7 mL (min gcat)-1), (O2)/(C3H8) ratio (1 to 3) and their interactions on propane conversion, AA selectivity and COx selectivity. The quadratic polynomial BBD equations and the feed-forward back propagation ANN models were developed based on the designed experimental data. Statistical analysis; coefficient of determination (R2), mean absolute error (MAE) and analysis of variance (ANOVA) illustrated that there was acceptable adjustment between BBD and ANN predicted responses as compared to experimental data. While, the ANN model showed a clear preference and generalization capability over BBD model in the case of experimental data set which were not used to training the models. In addition the optimum conditions were found to be temperature (461.7 °C), GHSV (51.9 mL (min gcat)-1) and (O2)/(C3H8) ratio (2.1) which were determined by desirability function approach. In these conditions, propane conversion of 15.2%, AA selectivity of 32% and COx selectivity of 44% which obtained experimentally were in reasonable agreement with predicted responses.
Chemical Engineering Research Bulletin 21(2019) 1-19