(148c) Energy Dispersive X-Ray Hyperspectral Image Analysis and Chemometrics for Catalyst Characterization
AIChE Annual Meeting
Thursday, November 18, 2021 - 9:10am to 9:45am
The application of Multivariate Image Analysis (MIA) techniques and Multivariate Curve Resolution (MCR) models becomes essential for the analysis of EDX hyperspectral images. This contribution proposes a modeling framework that permits segregating hyperspectral X-Ray images into simpler images (so-called Distribution Maps, DMâs), which can be directly related to each of the chemical compounds present in the mixture . From these DMâs, chemical-textural score images (SIâs) are further obtained. Finally, from all these DMâs and SIâs, different types of features, such as quantitative, morphological or textural can be extracted and combined into new data structures . This new source of information is used to build multivariate statistical models for process understanding and prediction purposes at a MIA image-based level. This approach allows us to study similarities and differences between and within types of catalysts -i.e. different samples of the same catalyst across batches, and across locations, and the potential effects on quality properties of interest. To illustrate the chemometric framework for catalyst characterization, STEM-EDX images of real industrial catalyst will be used.
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