改进的局部LSSVM算法在明胶浓度软测量中的应用

Improved local LSSVM and application in soft sensor modeling of gelatin concentration

在明胶生产的提胶工艺中,胶液浓度是一个很重要的质量和控制参数,但是目前对明胶浓度的检测手段多为离线人工检测,不能实现在线实时测量。该文提出使用软测量的方法对明胶浓度进行在线测量,采用局部LSSVM建模方法进行软测量建模,并用PSO算法对模型参数进行优化。Matlab仿真结果表明,基于PSO优化的局部LSSVM的软测量方法预测性能由于全局建模方法,可以达到很好的预测精度和泛化能力,为实现胶液浓度的在线检测开辟了新途径。

The concentration of gelatin is a very important control parameters of gelatin production,but the equipment for monitoring concentration of gelatin on-line is so expensive and difficult to maintain that it is not suitable for application,and the off-line sampling and monitoring method with low accuracy is applied in general.Aiming to provide practical and affordable industrial-scale control technology of extraction,we developed a soft sensor to estimate concentration of gelatin on-line based on temperature and time in the process.Because of difference of every material,a local LSSVM modeling was applied instand of traditional global modeling method.The PSO is used to optimize parameters of LSSVM.The simulation results show that the model has effective generalization performance and higher precision.