Process analytical chemistry and chemometrics, Bruce Kowalski's legacy at The Dow Chemical Company

Pell, Randy J.; Seasholtz, Mary Beth; Beebe, Kenneth R.; Koch, Mel

doi:10.1002/cem.2535

Cited by 12 publications

(6 citation statements)

References 109 publications

(114 reference statements)

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“…The results showed that spectral preprocessing should be performed based on an understanding of the physics and chemistry of the material. 59,60…”

Section: Resultsmentioning

confidence: 99%

“…The results showed that spectral preprocessing should be performed based on an understanding of the physics and chemistry of the material. 59,60 The right side of Table 4 shows the results of models performed from 1 to 30 film layers as calibration set and with a prediction set that varied from 31 to 50 film layers. The authors anticipated that the larger number of films in the validation set would increase the error.…”

Section: Prediction Of Number Of Filmsmentioning

confidence: 99%

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Study of near infrared chemometric models with low heterogeneity films: The role of optical sampling and spectral preprocessing on partial least squares errors

Ortega-Zúñiga

Reyes-Maldonado

Méndez

et al. 2017

Journal of Near Infrared Spectroscopy

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This study is focused on understanding absorption and scattering effects and their impact on the errors observed in near infrared calibration models developed using partial least squares regression able to predict the number of polypropylene film layers stacked together. The films provided a system with reduced heterogeneity to study the sources of error due to light scattering, selection of spectral preprocessing and spectral region on near infrared calibrations. Near infrared spectra were acquired using two experimental setups with the integrating sphere module of the FT-NIR spectrometer. The first setup consisted in stacking the polymer films to determine the penetration of the near infrared radiation. The second setup was a variation using a reflective surface on the top of the films in transflection mode, increasing the pathlength and therefore transmission and absorption of the material. The estimation of the penetration of radiation was performed using talc placed over the polymers films. The narrow bands of talc, related to first and second overtones of the O-H stretching mode, were used to estimate the near infrared sampling depth into polymer film layers, which ranged from 2.95 to 3.12 mm. Partial least square calibrations developed with up to 30 film layers were accurate with bias values that were not significantly different from zero at the 95% confidence level. Statistical errors were calculated for seven near infrared regions using different spectral preprocessing and the confidence interval of the bias showed that optical sampling is unbiased and there is an absence of systematic error by the near infrared method. A calibration model developed with 50 film layers presented high statistical errors and bias different from zero, indicating a sampling error due the depth of penetration of near infrared radiation. The impact of number of samples in the calibration and validation set was also evaluated. The results showed that bias was significant when the number of samples was less than 11. This finding highlights the lack of systematic error in the near infrared method, as long as the number of samples in the calibration set is representative of the variation to be modelled by a partial least squares regression and the sampling error is reduced.

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“…The results showed that spectral preprocessing should be performed based on an understanding of the physics and chemistry of the material. 59,60…”

Section: Resultsmentioning

confidence: 99%

Section: Prediction Of Number Of Filmsmentioning

confidence: 99%

Study of near infrared chemometric models with low heterogeneity films: The role of optical sampling and spectral preprocessing on partial least squares errors

Ortega-Zúñiga

Reyes-Maldonado

Méndez

et al. 2017

Journal of Near Infrared Spectroscopy

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“…The online NIR oxide concentration is shown in green. After an initial increase in unreacted oxide concentration due to a low temperature (red line), the unreacted oxide level is maintained at a constant level as the reactor level increases (black line) and the reactor pressure The implementation of an online FTNIR instrumentation and models at Dow [26] enabled reduction in offgrade by providing real-time data to operations, as well as the ability to manage the quality assurance of product storage tanks using a statistical approach, which in some cases eliminates the need for interim storage. Additional cost savings is achieved through significant reduction in reactor sampling and associated lab testing, which is compounded by the multiple tests performed on each reactor sample.…”

Section: Polyol/polyglycolmentioning

confidence: 99%

“…The implementation of an online FTNIR instrumentation and models at Dow enabled reduction in off‐grade by providing real‐time data to operations, as well as the ability to manage the quality assurance of product storage tanks using a statistical approach, which in some cases eliminates the need for interim storage. Additional cost savings is achieved through significant reduction in reactor sampling and associated lab testing, which is compounded by the multiple tests performed on each reactor sample.…”

Section: Polyol/polyglycolmentioning

confidence: 99%

Use of Online Spectroscopy to Control Polymerization in Industrial Processes

Colegrove

Deshpande

Harner

et al. 2017

Macro Reaction Engineering

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Online analyzers enable manufacturers to operate modern polymerization processes as safely, reliably, and profitably as possible. The most critical application of this technology involves monitoring the reactor itself to determine the molecular weight and comonomer content of the polymer produced. Polymer process streams introduce unique challenges for online analyzers because the process chemistry, sample transport, and stream conditions are harsher, prone to fouling, and pose unique safety hazards. Spectroscopy lends itself very well to online analysis of polymers because the data it provides is information rich and can be deployed using fiber optics for remote sampling of gases, liquids, and solids. Additionally, it is relatively fast and reliable if well designed and installed properly. The combination of spectroscopy, fiber optics, and chemometrics gives manufacturers the ability to use modern spectrometers and statistical models to analyze and monitor diverse types of polymerization processes. Several examples are provided in this article, including application to polyethylene, polycarbonate, polyurethane, and polystyrene processes.

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“…Another approach is to consider all the data together and use multivariate statistical process control (MSPC) (also known as chemometrics) for process monitoring. While this is well‐regarded technology, [ 4,5 ] one of the challenges has been how to deploy MSPC methods to those not trained in chemometrics. This paper discusses how to deploy both EMI and MSPC to those not trained in multivariate methods or data science.…”

Section: Introductionmentioning

confidence: 99%

Maximizing returns from enterprise manufacturing intelligence and multivariate statistical process control

Seasholtz¹,

Crowley²,

Schmidt³

et al. 2021

J Adv Manuf & Process

Self Cite

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This paper addresses challenges related to deploying analytics in the manufacturing environment. It discusses how to blend univariate and multivariate analyses into a deployment that can be successfully used by those not trained in Data Science. Enterprise manufacturing intelligence (EMI) has found great value in the chemical industry for aiding in timely decision making for improved plant reliability. It typically involves the use of control charts of multiple variables; that is, a univariate approach to data analysis. Another approach is to consider the data all together in a multivariate model, resulting in multivariate statistical process control (MSPC). These two approaches are complementary. Discussed in this report are guidelines for maximizing returns from EMI and MSPC deployments, including (1) considerations when setting up the MSPC model and ( 2) examples for how to interpret MSPC alerts, especially aimed at users who are not trained in multivariate data analysis.

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Process analytical chemistry and chemometrics, Bruce Kowalski's legacy at The Dow Chemical Company

Cited by 12 publications

References 109 publications

Study of near infrared chemometric models with low heterogeneity films: The role of optical sampling and spectral preprocessing on partial least squares errors

Study of near infrared chemometric models with low heterogeneity films: The role of optical sampling and spectral preprocessing on partial least squares errors

Use of Online Spectroscopy to Control Polymerization in Industrial Processes

Maximizing returns from enterprise manufacturing intelligence and multivariate statistical process control

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