1. bookVolume 35 (2017): Issue 3 (October 2017)
Journal Details
License
Format
Journal
eISSN
2083-134X
First Published
16 Apr 2011
Publication timeframe
4 times per year
Languages
English
Open Access

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

Published Online: 31 Oct 2017
Volume & Issue: Volume 35 (2017) - Issue 3 (October 2017)
Page range: 486 - 495
Received: 08 Oct 2016
Accepted: 24 Feb 2017
Journal Details
License
Format
Journal
eISSN
2083-134X
First Published
16 Apr 2011
Publication timeframe
4 times per year
Languages
English
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.

Keywords

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