首页|Evaluation and application of machine learning principles to Zeolite LTA synthesis
Evaluation and application of machine learning principles to Zeolite LTA synthesis
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NSTL
A progressive machine learning methodology was utilised to not only identify the relationship between zeolite synthesis descriptors but also evaluate the potential for machine learning to predict the quantitative output of synthesis routes. The hypothesis was if statistics and machine learning principles are applied, then it may enable pre-evaluation and result in potential increases to zeolite yield and performance. Various machine learning algorithms were applied to zeolite LTA synthesis data; including linear regression, ridge regression, regression tree, random forest, XGBoost and artificial neural network models. Major findings included the use of input synthesis variables and the product yield for model training. Additionally, the use of both quantitative and qualitative X-ray diffraction (XRD) data was required to accurately determine product composition ("hybrid XRD" approach). Models, including linear regression, ridge regression and regression trees, returned R~2 values less than 0.5 indicating the complexity inherent with the problem. Embedded tree-based models, including random forest and XGBoost, resulted in testing accuracies equal to R~2 = 0.620 and R~2 = 0.700, respectively. An ANN model achieved the highest accuracy among all machine learning algorithms of R~2 = 0.84. Notably, this model was the most accurate because it exploited non-linear and complex relationships within a multidimensional and inter-correlated dataset such as that obtained from zeolite synthesis. Despite reaching an accuracy greater than 80%, the ANN model accuracy continued to increase by increasing the network size, indicating that advanced deep learning models should be considered as part of future work.
School of Mechanical Medical & Process Engineering, Faculty of Engineering, Queensland University of Technology (QUT), Brisbane, Queensland, 4000, Australia
School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Brisbane, Queensland, 4000, Australia
NSTL
