Robust BOD_5 Soft Sensor Design Using Local Learning
In wastewater treatment plants, Its difficult to acquire online data of BOD5 (Biochemical Oxygen Demand for 5 days) due to its characteristic and unreliability of on-line sensors. Furthermore, although soft sensors models are widely used in wastewater treatment, only a few approaches for soft sensors models are designed to address the problems currently existing in the wastewater treatment. In such situations, there is a really need to develop a reliable, robust and real time soft sensor. To facilitate this soft sensor design, first, this paper presents a robust Principal Component Analysis (PCA), which combines PCA with statistical Jolliffe Parameters, to detect outliers and increases the robustness of soft sensors. Second, advanced local learning methods including Radial basis function (RBF) and Locally weighted projection regression (LWPR) are introduced to address the modeling issues. These strategies have the potentials to significantly improve the measurement of BOD5.
component RBF BOD_5 LWPR local learning wastewater treatment
YiQi Liu Daoping Huang Yan Li
College of Automation Science and Engineering South China University of Technology Guangzhou, China
国际会议
太原
英文
584-589
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）