<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Zeolites are microporous aluminosilicate/silicate crystalline materials with three-dimensional tetrahedral configuration. In this study, the degree of crystallinity of the synthesized Linde Type A (LTA) zeolite, which is the main indicator of its quality/purity is tried to be modeled. Effect of crystallization time, temperature, molar ratio of the synthesis gel on the relative crystallinity of the LTA zeolites is investigated using artificial neural networks. Our experimental observations and some data collected from literature have been used for adjusting the parameters of the proposed model and evaluating its performance. It has been observed that two-layer perceptron network with eight hidden neurons is the most accurate approach for the considered task. The designed model predicts the experimental datasets with a mean square error of 3.99 × 10<sup>-6</sup>, absolute average relative deviation of 8.69 %, and regression coefficient of 0.9596. The proposed model can decrease the required time and number of experiments to evaluate the extent of crystallinity of the LTA zeolites. </p></abstract>

Zeolites are microporous aluminosilicate/silicate crystalline materials with three-dimensional tetrahedral configuration. In this study, the degree of crystallinity of the synthesized Linde Type A (LTA) zeolite, which is the main indicator of its quality/purity is tried to be modeled. Effect of crystallization time, temperature, molar ratio of the synthesis gel on the relative crystallinity of the LTA zeolites is investigated using artificial neural networks. Our experimental observations and some data collected from literature have been used for adjusting the parameters of the proposed model and evaluating its performance. It has been observed that two-layer perceptron network with eight hidden neurons is the most accurate approach for the considered task. The designed model predicts the experimental datasets with a mean square error of 3.99 × 10-6, absolute average relative deviation of 8.69 %, and regression coefficient of 0.9596. The proposed model can decrease the required time and number of experiments to evaluate the extent of crystallinity of the LTA zeolites.

Prediction of degree of crystallinity for the LTA zeolite using artificial neural networks

Materials Science-Poland

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Zeolites are microporous aluminosilicate/silicate crystalline materials with three-dimensional tetrahedral configuration. In this study, the degree of...

Prediction of degree of crystallinity for the LTA zeolite using artificial neural networks

Publié en ligne: 31 oct. 2017

Pages: 486 - 495

Reçu: 08 oct. 2016

Accepté: 24 févr. 2017

DOI: https://doi.org/10.1515/msp-2017-0044

Mots clés
LTA zeolite, relative crystallinity, artificial intelligence approach, optimum topology

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Prediction of degree of crystallinity for the LTA zeolite using artificial neural networks

Publié en ligne: 31 oct. 2017

Pages: 486 - 495

Reçu: 08 oct. 2016

Accepté: 24 févr. 2017

DOI: https://doi.org/10.1515/msp-2017-0044

Mots clésLTA zeolite, relative crystallinity, artificial intelligence approach, optimum topology

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
LTA zeolite, relative crystallinity, artificial intelligence approach, optimum topology