Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network

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 Auto-associative 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.

The figure shows the procedure of establishing the EDAC-AHNN model for complex chemical process systems. It consists of two steps. Firstly, the Extension Data Attributes Classification (EDAC) method is used to make classification of the input data attributes. Then the subnets of the model are established with the classification result of the EDAC. After establishing the subnets, the main net is connected. It is an intelligent method via using artificial neural network (ANN) technology, which proves the capability of the artificial intelligence to model the complex and highly nonlinear relation of complex chemical processes.Figure optionsDownload as PowerPoint slide