Abstract: This paper is a study of PTA solvent dehydration tower. The author used a statistical regression method and the technology of artificial neural network, and built a soft-sensing model of the PCA-BP neural network which made up the solvent dehydration tower products for export. In this paper included the following aspects: firstly, the author analyzed the main parameter of the artificial neural network based on the principles of PCA-BP modeling, and summed up a more effective method. And this method has been applied to achieve a satisfactory result. Secondly, the author added up the soft-sensing approaches to both the top and bottom products of solvent dehydration tower. Based on these approaches, the author chose a multiple linear regression (MLR) method in modeling the solvent dehydration tower products, and also inspected those models. The result showed the accuracy of those models has achieved the expected standard. Thirdly, the author studied the method based on the BP artificial neural network in modeling the solvent dehydration tower products, and tested the models through the data on the scene. At last, the author took a long view on statistical regression method and the technology of artificial neural network used in soft-sensing modeling, and then pointed out some ideas for further work.
Keywords: Solvent Dehydration Tower; Multiple Linear Regression (MLR); Soft-sensor; Principal Component Regression(PCR); Artificial Neural Network.