To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets ...To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks(AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method,the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification(EDAC) is adopted. Soft sensor using AHNN based on EDAC(EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid(PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.展开更多
Poly(ethylene terephthalate)(PET)was synthesized by the in-situ polymerization method using layered double hydrotalcite(LDH)as the catalyst,and the thermal and flame retardation properties of PET were investigated as ...Poly(ethylene terephthalate)(PET)was synthesized by the in-situ polymerization method using layered double hydrotalcite(LDH)as the catalyst,and the thermal and flame retardation properties of PET were investigated as required.As identified by differential scanning calorimetry(DSC)and thermogravimetric(TGA)analysis,the crystallization rate and thermal degradation temperature of the as-prepared PET sample were enhanced compared with commercial PET sample.It was confirmed from the fire-resistant property study that the LDH can be used as an efficient flame-retardant besides functioning as a catalyst in the transesterification/polycondensation process for PET synthesis.展开更多
基金Supported by the National Natural Science Foundation of China(61074153)
文摘To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks(AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method,the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification(EDAC) is adopted. Soft sensor using AHNN based on EDAC(EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid(PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.
文摘Poly(ethylene terephthalate)(PET)was synthesized by the in-situ polymerization method using layered double hydrotalcite(LDH)as the catalyst,and the thermal and flame retardation properties of PET were investigated as required.As identified by differential scanning calorimetry(DSC)and thermogravimetric(TGA)analysis,the crystallization rate and thermal degradation temperature of the as-prepared PET sample were enhanced compared with commercial PET sample.It was confirmed from the fire-resistant property study that the LDH can be used as an efficient flame-retardant besides functioning as a catalyst in the transesterification/polycondensation process for PET synthesis.