Yoo Chang Kyoo, Lee In-Beum, Vanrolleghem Peter A
BIOMATH, Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure Links 653, B-9000, Gent, Belgium.
Environ Monit Assess. 2006 Aug;119(1-3):349-66. doi: 10.1007/s10661-005-9030-7. Epub 2006 May 24.
This article describes the application of on-line nonlinear monitoring of a sequencing batch reactor (SBR). Three-way batch data of SBR are unfolded batch-wisely, and then a adaptive and nonlinear multivariate monitoring method is used to capture the nonlinear characteristics of normal batches. The approach is successfully applied to an 80 L SBR for biological wastewater treatment, where the SBR poses an interesting challenge in view of process monitoring since it is characterized by nonstationary, batchwise, multistage, and nonlinear dynamics. In on-line batch monitoring, the developed adaptive and nonlinear process monitoring method can effectively capture the nonlinear relationship among process variables of a biological process in a SBR. The results of this pilot-scale SBR monitoring system using simple on-line measurements clearly demonstrated that the adaptive and nonlinear monitoring technique showed lower false alarm rate and physically meaningful, that is, robust monitoring results.
本文介绍了序批式反应器（SBR）在线非线性监测的应用。SBR的三向批次数据按批次展开，然后采用自适应非线性多元监测方法来捕捉正常批次的非线性特征。该方法成功应用于处理生物废水的80升SBR，鉴于其具有非平稳、分批、多阶段和非线性动力学的特点，SBR在过程监测方面提出了有趣的挑战。在在线批次监测中，所开发的自适应非线性过程监测方法能够有效捕捉SBR中生物过程的过程变量之间的非线性关系。使用简单在线测量的该中试规模SBR监测系统的结果清楚地表明，自适应非线性监测技术显示出较低的误报率且具有实际意义，即监测结果稳健。
