Some Studies On Soft Sensor Technology And Their Applications To Industry Process

Soft sensor technology is one of the most important research directions in the area of process control. In this dissertation, several issues and the corresponding solutions about soft sensor technology are discussed based on the real industrial process and the main contributions are described as follows.1. A soft sensor modeling algorithm based on improved fuzzy neural network is presented. The normalized average output membership functions are defined as fuzzy basis functions for defuzzification calculation. In order to improve the property of convergence, some parameters of the fuzzy neural network are trained by Levenberg-Marquardt algorithm, and the others are trained by gradient descent algorithm. Finally, a soft sensor model of melt index in polymer reaction based on the proposed method is established, and the simulation results show that in contrast to the traditional fuzzy neural network the proposed method is not sensitive to initial parameters and possesses good convergence capability and prediction precision.2. A new hybrid learning algorithm is proposed to train the fuzzy neural network based on TSK fuzzy model. Firstly, fuzzy c-means algorithm is applied to initialize the parameters of the fuzzy neural network. Secondly, the parameters of the premise part of the fuzzy rule are learned by the gradient descent algorithm. Finally, the parameters of consequent part are learned by the partial least squares algorithm. The proposed hybrid method can automatically give appropriate initial parameters of the fuzzy neural network and prevent the fuzzy rule number from increasing for high-dimensional systems. The results of simulation and industrial application show that the hybrid learning algorithm has properties of fast convergence and high accuracy.3. A soft sensor modeling method based on hybrid model combining the simplified first principle model and data-driven model is proposed. Several simplified first principle models are used to improve performances of robustness and generalization. The data-driven model, which is either linear function or nonlinear function such as neural network, is adopted tocompensate the non-modeling part of the simplified first principle model. The proposed method is applied to develop a soft sensor for estimating 4-CBA concentration in practical PTA oxidation process. The simulation results by use of real industrial data show that the proposed method has such performances of high precision, strong robustness, easy maintenance and a few samples for modeling needed.4. The partial least squares algorithm with limited memory is applied to model the soft senor on-line to predict the average particle size for the PTA oxidation process. In general, the data window is not long in length to use partial least square algorithm to develop model, some useful information would be lost if old samples are discarded directly. An idea is proposed to introduce the useful information to the model by the variances and means of old samples. The results of simulation show that the soft sensor based on the proposed method has high precision and is suitable for time-varying system with samples which distribution is not uniform.5. Time alignment matching between the primary variables and the process variables is an important part of the soft sensor technology. In fact, the time alignment matching is used to specify parameters that determine how nominal dead times are computed during the input data time alignment process. Two practical methods based on maximum correlation coefficient and fuzzy curve are proposed to determine the dead time according to the data sampled from industrial process without any manually operation. The maximum correlation coefficient based method is suitable for linear systems or weak nonlinear systems, while the fuzzy curve based method is suitable for strong nonlinear systems. Both the simulation and the application to industry show that the two presented methods are meaningful to determine the dead times.