2020
DOI: 10.3390/pr8091035
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Modeling of Spiral Wound Membranes for Gas Separations—Part II: Data Reconciliation for Online Monitoring

Abstract: The present work presents a methodology based on data reconciliation to monitormembrane separation processes reliably, online and in real time for the first time. The proposed methodology was implemented in accordance with the following steps: data acquisition; data pre-treatment; data characterization; data reconciliation; gross error detection; and critical evaluation of measured data with a soft sensor. The acquisition of data constituted the sloweststage of the monitoring process, as expected in real-time … Show more

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Cited by 10 publications
(6 citation statements)
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“…The acquisition and processing of data from the monitoring sensors of the machines under study were performed by collecting data from the supervisory system of the production plant where the turbogenerators are installed. This was done using the Python library "pandaspi" which serves as an interface between the Python language and the generator's supervisory system for raw data acquisition [32] in a data frame format. A data cleaning step was necessary to avoid some missing or unsampled values.…”
Section: Data Understandingmentioning
confidence: 99%
“…The acquisition and processing of data from the monitoring sensors of the machines under study were performed by collecting data from the supervisory system of the production plant where the turbogenerators are installed. This was done using the Python library "pandaspi" which serves as an interface between the Python language and the generator's supervisory system for raw data acquisition [32] in a data frame format. A data cleaning step was necessary to avoid some missing or unsampled values.…”
Section: Data Understandingmentioning
confidence: 99%
“…The nonlinearity of some regression methods can itself mitigate or eliminate this issue, and even in the context of linear methods, other strategies exist, including subset selection and penalization methods such as ridge regression and Lasso [29]. Data reconciliation techniques can also be employed, as they allow for the treatment of fluctuations and uncertainties in both X and Y [30][31][32][33]. The aim of these objections is not to diminish the importance of latent variable models but rather to highlight that their prevalence in the process monitoring literature may be partially attributed to historical biases.…”
Section: Multivariate Statisticsmentioning
confidence: 99%
“…Rautenbach et al, Scholtz et al, and Adewole et al showed that the dependence of permeability on the temperature can be described by an Arrhenius-type equation. Finally, a comprehensive approach for spiral-would permeators, including model proposal, data reconciliation from an industrial unit, and modeling considering the energy balance, has been recently presented.…”
Section: Introductionmentioning
confidence: 99%