Nondestructive rapid and quantitative analysis for the curing process of silicone resin by near‐infrared spectra

Yang, Jian; Zhang, Tong; Yin, Yue; Jiang, Wen; Du, Yunzhe; Zhu, Xiaofei; Jiang, Bo; Huang, Yudong

doi:10.1002/app.48982

Cited by 2 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is a non-destructive, low-cost, non-pollutant, and easy-to-use technique. Calibration models can be constructed to monitor properties with fast dynamics, e.g., hydrolysis of acetic anhydride, to avoid destructive and time-consuming analytical methods, such as titration, to estimate residual double bonds and epoxy value and polymer molecular weight, , and to monitor microreactors (where the size of samples can be a concern). Besides that, the correlation between conversion and particle size in stabilizer-free dispersion polymerization is also a challenge.…”

Section: Resultsmentioning

confidence: 99%

Stabilizer-Free Dispersion Copolymerization Monitoring by In-Line NIR Spectroscopy

Giudici

2020

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Stabilizer-free dispersion copolymerization is a one-step technique to produce uniform polymer particles. Due to its fast kinetics and nucleation, assessing polymer conversion and particle size via off-line sampling can be a challenge. In addition, off-line analyses, e.g., gravimetry and dynamic light scattering, are time-consuming and can be sample-dependent. Near-infrared (NIR) spectroscopy is a fast and non-destructive technique to monitor polymerization reactions in-line. Stabilizer-free dispersion copolymerization between styrene and maleic anhydride was carried out via free-radical polymerization using isobutyl acetate and isopentyl acetate as solvents. Spectra data were collected in parallel with off-line analysis. Multivariate partial least squares calibration models were constructed to predict global conversion and particle size over time. The external validation shows good agreement between the models and experimental data. NIR can be extended to monitor systems with fast kinetics and to avoid destructive and time-consuming analytical methods.

show abstract

Section: Resultsmentioning

confidence: 99%

Stabilizer-Free Dispersion Copolymerization Monitoring by In-Line NIR Spectroscopy

Giudici

2020

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

Quantitative analysis on the oil content of oilfield wastewater based on a convolutional neural network model and ultraviolet transmission spectroscopy

Wang

Zhao

et al. 2023

Water Science and Technology

View full text Add to dashboard Cite

Oil content (OC) is one of the important evaluation indicators in oilfield wastewater (OW) treatment. The purpose of this study is to realize online real-time detection of OC in OW by combining ultraviolet spectrophotometry with the convolutional neural network (CNN). In this paper, 80 groups of OW transmission data were measured for model establishment. Three CNN models with different structures are established to generalize the super parametric optimization process of the model. Furthermore, as a common method used in spectroscopy, the synergy interval partial least squares (siPLS) model is built in order to compare its accuracy with the CNN model. The results indicated the CNN model has a better performance than siPLS, in which the CNN model numbered Model 3 has the lowest root mean square error (MSE) of prediction (RMSEP) of 1.606 mg/L. As a consequence, the CNN model can be used in the monitoring of OW. This article will guide a rapid analysis of the OC of OW.

show abstract

Nondestructive rapid and quantitative analysis for the curing process of silicone resin by near‐infrared spectra

Cited by 2 publications

References 35 publications

Stabilizer-Free Dispersion Copolymerization Monitoring by In-Line NIR Spectroscopy

Stabilizer-Free Dispersion Copolymerization Monitoring by In-Line NIR Spectroscopy

Quantitative analysis on the oil content of oilfield wastewater based on a convolutional neural network model and ultraviolet transmission spectroscopy

Contact Info

Product

Resources

About