Robust multicomponent IR-to-concentration model regression

Griffin, Daniel J.; Grover, Martha A.; Kawajiri, Yoshiaki; Rousseau, Ronald W.

doi:10.1016/j.ces.2014.04.013

Cited by 15 publications

(14 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the FTIR signals depend on various conditions such as probe alignment, contact of liquid and probe, and background absorbance. 52 To handle this problem, the peak at 1800 cm −1 , which is not affected by the concentration of paracetamol and ethanol, was used as a reference peak. The concentrations of ethanol and paracetamol were given by normalizing two peaks, at 1048 and 1667 cm −1 , by that of the reference peak.…”

Section: Calibration For Solution Concentrationmentioning

confidence: 99%

Modeling of Nucleation, Growth, Dissolution, and Disappearance of Paracetamol in Ethanol Solution for Unseeded Batch Cooling Crystallization with Temperature-Cycling Strategy

Kim¹,

Kawajiri²,

Rousseau³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

A population balance model (PBM) is developed for unseeded batch crystallization, with temperature-cycling strategies to control the crystal size distribution. The model is able to predict the evolution of crystal size distributions of crystallizing paracetamol from ethanol solutions considering the characteristics of primary nucleation, secondary nucleation, growth, dissolution, and disappearance of crystals. Process analytical technology (PAT) tools were employed to collect solute concentration data and crystal size distribution data. This model employs a boundary condition to describe the disappearance of crystals in temperature-cycling strategies where the temperature is increased and decreased repeatedly. As a result, the obtained model can describe the evolution of crystal size distribution when repetition of cooling and heating is carried out. Moreover, this model can be applied to investigate phenomena that are challenging to explain with experimental data alone, thereby we can gain insight and optimize the operation of the process.

show abstract

Section: Calibration For Solution Concentrationmentioning

confidence: 99%

Modeling of Nucleation, Growth, Dissolution, and Disappearance of Paracetamol in Ethanol Solution for Unseeded Batch Cooling Crystallization with Temperature-Cycling Strategy

Kim¹,

Kawajiri²,

Rousseau³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In general, accurate and robust predictions at the price of lengthy calibrations is typical of supervised learning approaches, not only of PLS. For example, the SVM approach investigated by Griffin et al 6 required appropriate training with tens of samples taken at different conditions. Finally, PLS does not allow direct inference of the spectra of the pure species from the data, because the technique is designed to exploit so-called latent variables that best explain the variance between the input and output data.…”

Section: Modelingmentioning

confidence: 99%

“…Spectroscopic techniques, such as infrared (IR) in the form of attenuated total reflection-Fourier transform IR (ATR-FTIR) and Raman, are commonly used to analyze and monitor the composition of solutions and slurries. The standard approach to obtain quantitative information from these techniques usually relies on time-consuming calibration procedures, − which also need carefully designed sets of experiments with known species and concentrations to estimate model parameters. Additionally, if the species present in the mixture change, a new calibration typically becomes necessary, which may halt or delay processing.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Multicomponent Ionic Mixtures Using Blind Source Separation: A Processing Case Study

Maggioni

Kocevska

Grover

et al. 2019

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

Management and remediation of complex nuclear waste solutions require identification and quantification of multiple species. Some of the species forming the solution are unknown, and they can be different from vessel to vessel, thus limiting the utility of standard calibration approaches. To cope with such limited information, we propose a procedure based on blind source separation (BSS) techniques, in particular independent component analysis and multivariate curve resolution, with a one-point calibration library. Here we show the applicability and reliability of our procedure for online measurements of aqueous ionic solutions by proposing an automatic procedure to identify the number of species in the mixture, estimate the spectra of the pure species, and label the spectra with respect to a library of reference components. We test our procedure against simulated and experimental data for mixtures with six species (water plus five sodium salts) for the case of Raman and attenuated total reflection-Fourier transform infrared spectroscopy.

show abstract

“…Current approaches to measuring the composition of multicomponent mixtures include developing spectra-to-composition chemometrics models, which need to be trained with calibration data. , The calibration data may be sensitive to process fluctuations, such as changes in the temperature and pH. Another challenge, specifically associated with complex mixtures having many constituents, is the size of the calibration data set.…”

Section: Introductionmentioning

confidence: 99%

Spectroscopic Quantification of Target Species in a Complex Mixture Using Blind Source Separation and Partial Least-Squares Regression: A Case Study on Hanford Waste

Kocevska

Maggioni

Rousseau

et al. 2021

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

One of the challenges associated with multicomponent mixture analysis using chemometrics models is collecting calibration data. Depending upon the number of constituents, the size of the calibration set can be quite large. In some cases, the mixtures may contain numerous species, but only a small subset is central to the process for which quantification is being undertaken. For example, nuclear waste at the Hanford site contains a large number of radioactive and non-radioactive species, which complicates remediation efforts. However, only the concentrations of a few target species may need to be quantified in real time to facilitate operation of the cleanup process. In this paper, we introduce a preprocessing procedure that reduces the need for extensive model calibration. The preprocessing framework uses blind source separation (BSS) to identify the independent components in the mixture, which is followed by a correlation to classify them as either target species (part of the critical quality attributes that need to be measured during waste processing) or non-target species. The classification is used to preprocess the original mixture data: the signals of the target components are retained, while those of the non-target components are removed. Since the preprocessed spectra only contain the target components, the spectra-to-concentration regression model can be trained with a smaller calibration set. The approach is tested for Raman and infrared spectroscopy using simulated and experimental data sets based on simulant mixtures of nuclear waste.

show abstract

Robust multicomponent IR-to-concentration model regression

Cited by 15 publications

References 35 publications

Modeling of Nucleation, Growth, Dissolution, and Disappearance of Paracetamol in Ethanol Solution for Unseeded Batch Cooling Crystallization with Temperature-Cycling Strategy

Modeling of Nucleation, Growth, Dissolution, and Disappearance of Paracetamol in Ethanol Solution for Unseeded Batch Cooling Crystallization with Temperature-Cycling Strategy

Analysis of Multicomponent Ionic Mixtures Using Blind Source Separation: A Processing Case Study

Spectroscopic Quantification of Target Species in a Complex Mixture Using Blind Source Separation and Partial Least-Squares Regression: A Case Study on Hanford Waste

Contact Info

Product

Resources

About