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

• Autorzy: Ghanbari S. , Vaferi B.

Identyfikatory

DOI

10.1515/msp-2017-0044

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

LTA zeolite; relative crystallinity; artificial intelligence approach; optimum topology

• Wydawca: De Gruyter
• Czasopismo: Materials Science Poland
• Rocznik: 2017
• Tom: Vol. 35, No. 3
• Strony: 486--495
• Opis fizyczny: Bibliogr. 49 poz., rys., tab.

Twórcy

autor

Ghanbari S.

• Department of Chemical Engineering, Malek-Ashtar University of Technology, Tehran, Iran

autor

Vaferi B.

• Department of Chemical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).