AUTHOR=Zhang Yongzhen , Yang Zhenfa , Wang Yina , Ge Xinting , Zhang Jianfeng , Xiao Hang 

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

JOURNAL=Frontiers in Chemistry

VOLUME=12

YEAR=2024

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2024.1398984

DOI=10.3389/fchem.2024.1398984

ISSN=2296-2646

ABSTRACT=<p>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 (<inline-formula id="inf1"><mml:math id="m1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mi>C</mml:mi><mml:mi>a</mml:mi><mml:mi>C</mml:mi><mml:mi>O</mml:mi></mml:mrow><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>), silicon dioxide (<inline-formula id="inf2"><mml:math id="m2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mi>S</mml:mi><mml:mi>i</mml:mi><mml:mi>O</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>), aluminum oxide (<inline-formula id="inf3"><mml:math id="m3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mi>A</mml:mi><mml:mi>l</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>), and ferric oxide (<inline-formula id="inf4"><mml:math id="m4" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mi>F</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>) 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 <inline-formula id="inf5"><mml:math id="m5" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> 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.</p>