Zeolite structure prediction with artificial neural networks
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 w...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/10356/62472 |
| _version_ | 1811690567647625216 |
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| author | Kevin Tangkas |
| author2 | Su Haibin |
| author_facet | Su Haibin Kevin Tangkas |
| author_sort | Kevin Tangkas |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:06:03Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/62472 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:06:03Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/624722023-03-04T15:38:18Z Zeolite structure prediction with artificial neural networks Kevin Tangkas Su Haibin School of Materials Science and Engineering DRNTU::Engineering::Materials::Functional materials 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. Bachelor of Engineering (Materials Engineering) 2015-04-08T03:29:05Z 2015-04-08T03:29:05Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/62472 en Nanyang Technological University 43 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Materials::Functional materials Kevin Tangkas Zeolite structure prediction with artificial neural networks |
| title | Zeolite structure prediction with artificial neural networks |
| title_full | Zeolite structure prediction with artificial neural networks |
| title_fullStr | Zeolite structure prediction with artificial neural networks |
| title_full_unstemmed | Zeolite structure prediction with artificial neural networks |
| title_short | Zeolite structure prediction with artificial neural networks |
| title_sort | zeolite structure prediction with artificial neural networks |
| topic | DRNTU::Engineering::Materials::Functional materials |
| url | http://hdl.handle.net/10356/62472 |
| work_keys_str_mv | AT kevintangkas zeolitestructurepredictionwithartificialneuralnetworks |