Abstract:A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. The first step of this method is to build a multi-way PCA (MPCA) model using the historical process data. In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. This procedure is iterated twice. The first iteration provides an efficient and effective… Show more
“…Clustering methodology is based on calculating the degree of similarity using PCA and distance-similarity factors [13]. This methodology is a promising tool to efficiently interpret and analyze experimental data [14]. The objective of this study was to use RSM to find out the optimum formulation for developing a stable beverage emulsion based on WO by optimizing the content of WO and GA for the average droplet size (D 32 ), droplet specific surface area (SSA), polydispersity index (span), apparent viscosity, interfacial tension and opacity of the emulsion.…”
“…Clustering methodology is based on calculating the degree of similarity using PCA and distance-similarity factors [13]. This methodology is a promising tool to efficiently interpret and analyze experimental data [14]. The objective of this study was to use RSM to find out the optimum formulation for developing a stable beverage emulsion based on WO by optimizing the content of WO and GA for the average droplet size (D 32 ), droplet specific surface area (SSA), polydispersity index (span), apparent viscosity, interfacial tension and opacity of the emulsion.…”
“…The explicit use of PCA and FC for monitoring and controlling membrane fouling has not yet been reported in literature, but similar applications exist in various other fields, for instance for the monitoring and control of sequencing batch reactors and the detection of faults in conventional, continuous wastewater treatment plants (e.g. Aguado and Rosen, 2008;Marsili-Libelli, 2006;Villez et al, 2008). Hereby, especially the batch-wise PCA approaches appear interesting given the cyclic behaviour of the filtration process.…”
“…Statistical tools and artificial neural network are employed but interpretation of the results obtained is not always easy (Comas et al 2001;OliveiraEsquerre et al 2002;Goode et al 2007;Villez et al 2008).…”
Environmental monitoring of biological wastewater treatment plants (BWWTP) treating industrial effluents produces large amount of data. Frequent sampling is done in the influent and effluent but also in intermediate points. Samples are analyzed for classical and specific contaminants and physical-chemical parameters are monitored. In this paper data from a BWWTP treating the effluents of a coke and steel-processing factory are analyzed. Due to a complex situation, this BWWTP gave poor performances that did not match environmental regulations, meanwhile upgrading proved to be uneasy. Data analysis using principal component analyses (PCA) or kinetic modeling with a Haldane model was unsuccessful in handling these data, which was attributed to undetermined toxic effects. A new methodology is reported, that allowed to identify a kinetics for thiocyanate degradation and a relation between pH and toxic effects. This analysis of the plant data allowed to make hypothesis on the process control parameters and to recommend management modifications, allowing a further increase of the performances.
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