Improved Prediction of Oxide Content in Cement Raw Meal by Near-Infrared Spectroscopy Using Sequential Preprocessing through Orthogonalization (SPORT)

Zhang, Yong Zhen; Wang, Yina; Zhao, Zhi; Zhang, Lei; Xiao, Hang

doi:10.1080/00032719.2023.2266070

Cited by 1 publication

(1 citation statement)

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“…Using near-infrared spectroscopy can quickly and accurately determine the content of calcium carbonate, silicon dioxide, aluminum oxide, and ferric oxide of cement raw materials—the aforementioned four active ingredients. This provides timely guidance on the raw material batching process, ensuring low-cost and environmentally friendly operation of the production line ( Zhang et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Enhanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques

Zhang,

Yang,

Wang

et al. 2024

Front. Chem.

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The component analysis of raw meal is critical to the quality of cement. In recent years, near-infrared (NIR) has been emerged as an innovative and efficient analytical method to determine the oxide content of cement raw meal. This study aims to utilize NIR spectroscopy combined with machine learning and chemometrics to improve the prediction of oxide content in cement raw meal. The Savitzky-Golay convolution smoothing method is applied to eliminate noise interference for the analysis of calcium carbonate (CaCO3), silicon dioxide (SiO2), aluminum oxide (Al2O3), and ferric oxide (Fe2O3) in cement raw materials. Different wavelength selection techniques are used to perform a comprehensive analysis of the model, comparing the performance of several wavelength selection techniques. The back-propagation neural network regression model based on particle swarm optimization algorithm was also applied to optimize the extracted and screened feature wavelengths, and the model prediction performance was checked and evaluated using Rp and RMSE. In conclusion, the results indicate that NIR spectroscopy in combination with ML and chemometrics has great potential to effectively improve the prediction performance of oxide content in raw materials and highlight the importance of modeling and wavelength selection techniques. By enabling more accurate and efficient determination of oxide content in raw materials, NIR spectroscopy coupled with meta-modeling has the potential to revolutionize quality assurance practices in cement manufacturing.

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Section: Introductionmentioning

confidence: 99%