A Soft Sensor Based on Kernel PCA and Composite Kernel Support Vector Regression for a Flotation Circuit

摘要：

A soft sensor was developed to estimate the concentrate grade and recovery rate of a flotation circuit. The algorithm uses kernel principal component analysis (KPCA) and composite kernel support vector regression (CK-SVR) to perform the estimation. Firstly, the flotation prior knowledge and KPCA are employed to reduce the dimension of input vector of CK-SVR. Then, considering that the characteristics of kernels have great impacts on learning and predictive results of SVR, a composite kernel SVR modeling method based on polynomial kernel and RBF kernel is adopted which hyperparameters are adaptively evolved by the particle swarm optimization (PSO) algorithm. Simulations using real operating data show that the soft sensor provides the necessary accuracy for a flotation circuit.

关键词： flotation circuit KPCA CK-SVR PSO soft sensor

作者: Huizhi Yang Min Huang

作者单位: Zhongshan Institute University of Electronic Science and Technology of China Zhongshan,China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 375-378

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）