Multivariate Nonlinear Statistical Process Control of a Sequencing Batch Reactor

JOURNAL OF CHEMICAL ENGINEERING OF JAPAN

Chang Kyoo Yoo²  Kris Villez¹  In-Beum Lee²  Peter A. Vanrolleghem¹ 

[1] School of Environmental Science and Engineering, Department of Chemical Engineering, Pohang University of Science and Technology;BIOMATH: Department of Applied Mathematics, Biometrics and Process Control, Ghent University

关键词: Batch Monitoring;    Bioprocess;    Kernel Principal Component Analysis;    Multivariate Statistical Process Control;    Nonlinear Multivariate Analysis and Monitoring;    Sequencing Batch Reactor (SBR);    Wastewater Treatment Process (WWTP);   

DOI  :  10.1252/jcej.39.43

来源: Maruzen Company Ltd

【 摘 要 】

References(16)Cited-By(2)This research describes the application of a multivariate statistical process control method to a pilot-scale sequencing batch reactor (SBR) using a batchwise nonlinear monitoring technique for a denoising effect. Three-way batch data of normal batches are unfolded batch-wise and then a kernel principal component analysis (KPCA) is applied to capture the nonlinear dynamics within normal batch processes. The developed monitoring method was successfully applied to an 80-l sequencing batch reactor (SBR) for biological wastewater treatment, which is characterized by a variety of nonstationary and nonlinear characteristics. In the multivariate analysis and batch-wise monitoring, the developed nonlinear monitoring method can effectively capture the nonlinear relations within the batch process data and clearly showed the power of nonlinear process monitoring and denoising performance in comparison with linear methods.

【 授权许可】

Unknown