ATR FTIR spectroscopy for in situ measurement of supersaturation

Dunuwila, Dilum D.; Berglund, Kris A.

doi:10.1016/s0022-0248(97)00119-x

Cited by 123 publications

(101 citation statements)

References 7 publications

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“…There was no speculation. However, this may have caused readers to wonder, since the results do not agree with the common belief ŽNyvlt, 1976;Mullin, 1993;Moore, 1994;Dunuwila and . Berglund, 1997 that natural cooling, which proceeds very rapidly at the beginning of a run, causes enormous nucleation and eventually yields widespread product CSD.…”

Section: Proposed Mechanismsmentioning

confidence: 91%

“…This cooling policy, however, has never been believed to be appropriate for optimal operation of batch crystallizer, that is, for obtaining large-size or desired-size product crystals of Ž grown seeds, because of enormous nucleation Nyvlt, 1976;. Mullin, 1993;Moore, 1994;Dunuwila and Berglund, 1997 . However, according to a previous study by Jagadesh et al Ž . 1996 , using a laboratory-scale well-mixed crystallizer of 12.2 L equipped with a draft tube and a turbine stirrer, large monodispersed crystals can be produced even under natural cooling conditions if enough seeds are loaded at the beginning of the run.…”

Section: Introductionmentioning

confidence: 99%

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Scaleup experiments on seeded batch cooling crystallization of potassium alum

et al. 1999

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Section: Proposed Mechanismsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Scaleup experiments on seeded batch cooling crystallization of potassium alum

et al. 1999

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“…It was proposed that these tuning parameters are determined by using cross-validation [23] [27]. Although the polynomial is typically taken to be second order by appealing to the established findings in the literature [24][28] [29], the application of cross-validation to attain the other tuning parameters is a time-consuming process.…”

Section: Continuous Piecewise Direct Standardizationmentioning

confidence: 99%

“…For example, there are three tuning factors in CPDS to be determined: the width of the band in ) ( k t Q (2w+1), the number of latent variables in the PLS modelling algorithm to obtain ) ( k t Q , and the order of the polynomial function, whilst in LSS, the number of latent factors retained in the SVD algorithm and the order of the polynomial function require to be determined. Although the polynomial function is typically taken to be second order by appealing to the established findings in the literature [24][28] [29], the cross-validation of the other tuning parameters is still a time-consuming process.…”

Section: Implementation Complexitymentioning

confidence: 99%

The impact of temperature variations on spectroscopic calibration modelling: a comparative study

Chen

Martin

2007

Journal of Chemometrics

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Abstract:Temperature fluctuations can have a significant impact on the repeatability of spectral measurements and as a consequence can adversely affect the resulting calibration model. More specifically, when test samples measured at temperatures unseen in the training data set are presented to the model, degraded predictive performance can materialize. Current methods for addressing the temperature variations in a calibration model can be categorized into two classescalibration model based approaches, and spectra standardization methodologies. This paper presents a comparative study on a number of strategies reported in the literature including partial least squares (PLS), continuous piecewise direct standardization (CPDS) and loading space standardization (LSS), in terms of the practical applicability of the algorithms, their implementation complexity, and their predictive performance. It was observed from the study that the global modelling approach, where latent variables are initially extracted from the spectra using PLS, and then augmented with temperature as the independent variable, achieved the best predictive performance. In addition, the two spectra standardization methods, CPDS and LSS, did not provide consistently enhanced performance over the conventional global modelling approach, despite the additional effort in terms of standardizing the spectra across different temperatures. Considering the algorithmic complexity and resulting calibration accuracy, it is concluded that the global modelling (with temperature) approach should be first considered for the development of a calibration model where temperature variations are known to affect the fundamental data, prior to investigating the more powerful spectra standardization approaches.

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“…In the past, a significant effort has been devoted to the development of on-line analytical techniques for monitoring deterministic operating variables including the concentration using attenuated total ref lectance Fourier transform infrared spectroscopy (ATR-FTIR) [1][2][3] , to monitor the crystallographic structure using X-ray diffraction (XRD) and Raman spectroscopy 4,5) , and the crystal size distribution (CSD) using laser diffraction 5) , ultrasonic spectroscopy 6) and light scattering techniques [7][8][9] . In contrast, the literature on monitoring crystal morphology is scarce 10) .…”

Section: Introductionmentioning

confidence: 99%