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The framework (axes) of an empirical chart can be established by considering the principal process variables.
The basis of a model-based diagram is a physical model that describes the functional relationships between the principal process variables (axes).
A linear relationship (p<0.01) between three principal process variables (applied voltage, working distance and polymer concentration) and fibre diameter was reliably established enabling a mathematical model to be developed to standardise the electrospinning process.
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The application of Principal Component Regression to the trajectories of the process variables (block-wise PCR) has given straightforward results without requiring a deep knowledge of the process.
The controller allows the incorporation of constraints on the process variables and is designed on the basis of multi-way principal component analysis (MPCA) of a batch data array rearranged by means of a regularized batch-wise unfolding.
The process information is extracted by the independent component analysis (ICA) and principal component analysis (PCA) one after another to obtain the Non-Gaussianity and Gaussianity rooted in process variables.
In the absence of a process model, principal component analysis (PCA) has been successfully used as a data-based FD technique for highly correlated process variables.
Anomaly detection based on principal component analysis (PCA) has been studied intensively and largely applied to multivariate processes with highly cross-correlated process variables; however conventional PCA-based methods often fail to detect small or moderate anomalies.
The Measurement of Process Variables.
Process variables viz.
The principal process of DNA degradation is through hydrolytic depurination.
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