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유창규,( Peter A Vanrolleghem ),이인범 한국화학공학회 2007 화학공학의이론과응용 Vol.10 No.2
Due to increasing environmental constraints, efficient modeling and monitoring methods are becoming more and more important. Reliable process engineering tools for sustainable biological operation are necessary to maintain the system performance as close as possible to optimal conditions. The ultimate objective of this research is to suggest the integrated framework of modeling, process monitoring, control and optimization for a sustainable biological treatment operation. Under the proposed approach, process information obtained from statistical monitoring techniques is utilized to monitor the biological treatment process, to monitor a microbial population dynamics, to design the supervisory control, and finally to optimize the operating condition. Specially, we developed a new long-term monitoring technique by integrating process engineering data and microbiology tool, which can monitor and may manipulate the various microorganisms community to enrich the organisms distribution and maintain uniform sludge properties. Finally, a study to provide an integrated framework is attempted and has been applied to a pilot-scale sequencing batch reactor (SBR).
Parallel hybrid modeling methods for a full-scale cokes wastewater treatment plant
Lee, Dae Sung,Vanrolleghem, Peter A.,Park, Jong Moon Elsevier 2005 Journal of biotechnology Vol.115 No.3
<P><B>Abstract</B></P><P>Parallel hybrid modeling methods are applied to a full-scale cokes wastewater treatment plant. Within the hybrid model structure, a mechanistic model specifies the basic dynamics of the relevant process and a non-parametric model compensates for the inaccuracy of the mechanistic model. First, a simplified mechanistic model is developed based on Activated Sludge Model No. 1 and the specific process knowledge of the cokes wastewater treatment process. Then, the mechanistic model is combined with five different non-parametric models – feedforward back-propagation neural network, radial basis function network, linear partial least squares (PLS), quadratic PLS and neural network PLS (NNPLS) – in parallel configuration. These models are identified with the same data obtained from the plant operation to predict dynamic behavior of the process. The performance of each parallel hybrid model is compared based on their ease of model building, prediction accuracy and interpretability. For this application, the parallel hybrid model with NNPLS as non-parametric model gives better performance than other parallel hybrid models. In addition, the NNPLS model is used to analyze the behavior of the operation data in the reduced space and allows for fault detection and isolation.</P>
Lee, Dae Sung,Park, Jong Moon,Vanrolleghem, Peter A. Elsevier 2005 Journal of biotechnology Vol.116 No.2
<P><B>Abstract</B></P><P>In recent years, multiscale monitoring approaches, which combine principal component analysis (PCA) and multi-resolution analysis (MRA), have received considerable attention. These approaches are potentially very efficient for detecting and analyzing diverse ranges of faults and disturbances in chemical and biochemical processes. In this work, multiscale PCA is proposed for fault detection and diagnosis of batch processes. Using MRA, measurement data are decomposed into approximation and details at different scales. Adaptive multiway PCA (MPCA) models are developed to update the covariance structure at each scale to deal with changing process conditions. Process monitoring by a unifying adaptive multiscale MPCA involves combining only those scales where significant disturbances are detected. This multiscale approach facilitates diagnosis of the detected fault as it hints to the time-scale under which the fault affects the process. The proposed adaptive multiscale method is successfully applied to a pilot-scale sequencing batch reactor for biological wastewater treatment.</P>