Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/62472
Title: | Zeolite structure prediction with artificial neural networks | Authors: | Kevin Tangkas | Keywords: | DRNTU::Engineering::Materials::Functional materials | Issue Date: | 2015 | Abstract: | With zeolites consumption exceeding 3 million tons and hundreds of new zeolites structures are being synthesize, zeolites are an important part of the world in science and industry. Furthermore, there are still millions of hypothetical zeolites structures that are still not able to synthesized and waiting to be explored. Characterizations of zeolites still take a long time and involve complicated procedures. Hence, it is imperative to speed up the process of characterizing zeolites structure in order to advance zeolites science and technologies. With advancement of computational science, machine learning method will be explored here in order to expedite the characterization of zeolites. Artificial Neural Network will be utilized to build a prediction model that will predict the Framework Density of zeolites. This prediction model will only use simple inputs that can be easily obtained through chemical analysis of zeolites. The prediction models built produced promising results with relatively small error. The best model in this project was built with simple input of the Al/Si ratio and the type of element that present in the zeolite with Radial Basis Function Network algorithm in machine learning software called Waikato Environment for Knowledge Analysis (WEKA). The model’s Mean Absolute Error is just 0.3119 with Root Mean Squared Error of 0.5029. | URI: | http://hdl.handle.net/10356/62472 | Schools: | School of Materials Science and Engineering | Rights: | Nanyang Technological University | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | MSE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
62472 FYP+Report+(KEVIN).pdf Restricted Access | 1.98 MB | Adobe PDF | View/Open |
Page view(s) 50
544
Updated on Mar 27, 2024
Download(s) 50
22
Updated on Mar 27, 2024
Google ScholarTM
Check
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.