2004
DOI: 10.1016/j.cej.2004.05.011
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Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill

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Cited by 33 publications
(29 citation statements)
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“…Data quality is also a factor, with noisy signals and missing data as the common problems. The correlated structure and data quality considerations present in power plant monitoring records bear strong similarities to other application areas discussed in this section, such as chemical process control (Ma et al, 2009;Ahvenlampi and Kortela, 2005), manufacturing (Oliveira-Esquerre et al, 2004;Baharati et a1., 2004;Hisham et al, 2008) and medicine (Chan et a1., 2003). When monitoring a process which records a vast array of sensor data, individual analysis of each signal by a human operator is clearly not possible.…”
Section: Methods Selectionmentioning
confidence: 96%
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“…Data quality is also a factor, with noisy signals and missing data as the common problems. The correlated structure and data quality considerations present in power plant monitoring records bear strong similarities to other application areas discussed in this section, such as chemical process control (Ma et al, 2009;Ahvenlampi and Kortela, 2005), manufacturing (Oliveira-Esquerre et al, 2004;Baharati et a1., 2004;Hisham et al, 2008) and medicine (Chan et a1., 2003). When monitoring a process which records a vast array of sensor data, individual analysis of each signal by a human operator is clearly not possible.…”
Section: Methods Selectionmentioning
confidence: 96%
“…A high number of variables, with respect to the number of data samples, are also permissible in PLS, which can result in the modelling of noise for LLR (wise and Gallagher, 1996). In summary, PLS is capable of producing robust, effective models, despite operational data limitations, for example, imprecise measurements and missing data (Oliveira-Esquerre, 2004). The ability to predict dependent data values, especially in the case of product quality data, which is often measured infrequently, is useful in process monitoring (MacGregor, 2005).…”
Section: Multivariate Statistical Techniquesmentioning
confidence: 99%
“…Kang, Meea et al (2009) tried to identify pollution load characteristics of non-point pollution sources in a land growing cherry trees and Choi, Joung joo and Hyun, Kyoung hak (2005) surveyed amount of sanitary water in small rented apartment houses. While such efforts were demonstrated in studies at both home and abroad to estimate amount of pollution load and observe the characteristics (Akratos et al 2008;Meea Kang et al 2010;Mahapatra et al 2012;Oliveira-Esquerre et al 2004;Verma and Singh 2013;Udeigwe and Wang 2010;Yeung and Yung 1999) they were limited in covering the entire metropolitan region for a more detailed and wide survey. To address this limitation, a study was carried out with the goal to establish standards for estimating basic unit and BOD of sanitary sewage that are aligned with local characteristics.…”
Section: Introductionmentioning
confidence: 97%
“…Linear steady-state and dynamic models have been constructed and their results are presented in a companion paper [8]. Here, functional-link (FLN) and multilayer perceptron (MLP) neural networks are used as nonlinear modeling techniques for BOD prediction.…”
Section: Introductionmentioning
confidence: 99%