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TECHNOLOGY | POLYMER TESTING


processing variables) with those of the performance space. The data can be visualized in various ways (Figure 1). Principle component regression analysis and partial least squares (PLS) regression analysis relate the variables of the input and output spaces, with both types of analysis mapping the input variables onto the output variables. The results of these forward regression analyses can be inverted, which enables prediction of the input variables needed to obtain any specific balance of performance variables. MVDA is ideal for analysing big data sets


Above: Big Data is opening up new opportunities for process analysis but will require new data


analysis tools


the variables are actually complex and interrelated (correlated), multivariate data analysis (MVDA) methods are more appropriate in providing greater accuracy and a more thorough explanation of the variables’ relationships. At the Polymer Testing & Analysis North America


conference in September last year David Fiscus, Senior Chemist at ExxonMobil, shared an example of how MVDA was used to develop structure- process-property relationships for polyethylene blown films. A big dataset was developed and analysed using multivariate linear regression methods to solve the multitude of simultaneous equations relating the variables. The method of analysis was explained based on


traditional chemometric approaches. Statistical analysis removed differences in the measurement scales of the variables, which enables comparing variable effects on an equal basis. Principal component analysis (PCA) independently relates the variables composing the input space (for example, molecular structure, morphology, and


Figure 1: Principal component analysis (PCA) of machine and transverse direction ultimate properties of blown PE film


generated during routine operations involving many correlated variables, including correlated data from steps along the value chain (such as the resin manufacturer, compounder, fabricator and end user), says Fiscus. He notes that MVDA can be used to improve compound formulations and optimise compounding and processing operations. It can also be used to establish multivariate statistical process control protocols for those processes, and it can be used to analyse data sets generated using design-of-experiments methods. Various commercial multivariate software


packages are available. Now that large amounts of data can be easily collected and stored, and the computing power to analyse big data sets is available, MVDA is likely to be increasingly used.


Pellet analysis A new system from Sikora automates visual inspection and analysis of plastic materials in the lab. The Purity Concept V is an optical offline inspection and analysis system. As material on a tray moves through the inspection area, a colour camera records images for analysis. Contamination, such as black specks inside transparent pellets as well as on the surface of opaque and colored


Source: ExxonMobil 40 COMPOUNDING WORLD | January 2019 www.compoundingworld.com


PHOTO: SHUTTERSTOCK


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