Research of Methods for Extracting Principal Components Responding to Sucrose Supersaturation Based Soft Sensors in Cane Sugar Process

Based on the soft sensor techniques, five external factors that affect the cane sugar crystallization process are used as auxiliary variables, i.e., the syrup Brix and temperature, vacuum, steam pressure and feed flow, while the sucrose supersaturation is considered as the key variable. Then a soft sensor method for the sucrose supersaturation is developed based on kernel partial least squares. However, the cane sugar crystallization process is very complex, with features of multiple nonlinearity among the auxiliary variables. Besides, the auxiliary variables obtaining from the on-line sensors involves lots of uncertain components because of the limits of the sensors themselves and the environmental influence in cane sugar process, which weakens the generalization capacity of the soft sensor. Therefore, a kernel partial least squares method is adopted to extract the principle components of the auxiliary variables, which improves the accuracy and generalization capacity of the soft sensor model for the sucrose supersaturation. The experiment results proved that the soft sensor values were close to the actual values where the maximal relative error was nearly 2.91 %. This method is of high performance. Copyright © 2014 IFSA Publishing, S. L.