摘要
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.
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.
基金
Supported by the National Natural Science Foundation of China(61074153)
