Modelling and diagnostics of batch processes and analogous kinetic experiments

Wold, Svante; Kettaneh, Nouna; Fridén, Håkan; Holmberg, Andrea

doi:10.1016/s0169-7439(98)00162-2

Cited by 358 publications

(303 citation statements)

References 9 publications

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“…This method originated from statistical batch process control [71], and was later used for metabolomics experiments [72]. This method, however, assumes that measurements for different subjects at the same time-point are comparable.…”

Section: Data Analysis Methods To Deal With This Typementioning

confidence: 99%

Symbiosis of chemometrics and metabolomics: past, present, and future

Greef

Smilde

2005

Journal of Chemometrics

120

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Section: Data Analysis Methods To Deal With This Typementioning

confidence: 99%

Symbiosis of chemometrics and metabolomics: past, present, and future

Greef

Smilde

2005

Journal of Chemometrics

120

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“…Wold et al proposed a different approach by first applying variable-wise unfolding which maintains the variable direction and merges the batch and time directions followed by normalizing each column through mean-centering and scaling to unit variance [15]. As illustrated in Fig.…”

Section: Mpcav-variable-wise Unfolded Multi-way Pcamentioning

confidence: 99%

Dynamic model-based fault diagnosis for (bio)chemical batch processes

Kerkhof

Gins

Vanlaer

et al. 2012

Computers & Chemical Engineering

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“…The choice of chart type depends on the examiner's preference. When detecting deviations from the normal batch trajectory in the Hotelling's T 2 plot, it is possible to identify the monitored variable(s) responsible for this difference through the computation of contribution plots (Wold et al, 1998). A contribution plot displays the differences between the outlying observation and average observation for each monitored variable in the model.…”

Section: Real-time Batch Progress Evaluationmentioning

confidence: 99%

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements

Burggraeve

Kerkhof²,

Hellings³

et al. 2011

European Journal of Pharmaceutical Sciences

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Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively flowabililty as Y-matrix, were developed. The scores of the 4 test batches were used to examine the predictive ability of the model.

show abstract

Modelling and diagnostics of batch processes and analogous kinetic experiments

Cited by 358 publications

References 9 publications

Symbiosis of chemometrics and metabolomics: past, present, and future

Symbiosis of chemometrics and metabolomics: past, present, and future

Dynamic model-based fault diagnosis for (bio)chemical batch processes

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements

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