Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/571288
Title: | Metal Organic Frameworks Design and Screening for Separation of Hydrocarbons Using Molecular Simulations and Machine Learning |
Researcher: | Halder, Prosun |
Guide(s): | Singh, Jayant K |
Keywords: | Machine Learning Metal Organic Frameworks Molecular Simulation Monte Carlo Simulation |
University: | Indian Institute of Technology Kanpur |
Completed Date: | 2023 |
Abstract: | Adsorbent-based gas separation through pressure swing adsorption (PSA), temperature swing adsorption, and membrane-based separation are promising technologies replacing traditional industrial separation methods. The most studied nanoporous materials are metal-organic framework (MOF), covalent organic frameworks (COF), activated carbon, zeolite, zeolitic imidazolate framework (ZIF), and porous polymer network (PPN). The metal-organic framework (MOF) has immense relevance in the gas adsorption, separation, and energy storage applications, among the various adsorbents. MOF has attracted particular interest because of its easy synthesis, high surface area, and porosity. The discovery of new potential MOFs for a particular application is an ongoing research area. However, for computational study, MOFs can be constructed from modular molecular building blocks, typically metal clusters and organic linkers. These building blocks can be assembled to form an almost unlimited number of MOFs. Experimentally synthesizing the vast materials database is practically not viable to find a suitable material for a specific application. Similarly, a direct molecular simulation approach for each fictitious or experimentally realized material is an inefficient way to use computational resources and time. Therefore, a cost-effective screening technique is imminent to find the best MOF for a particular application. In this context, a hybrid approach combining machine learning algorithms with molecular simulations could effectively screen materials. It is well known that MOFs chemical and structural properties can directly affect gas adsorption, gas separation. In the first part of the talk, I will present an approach to identify the best MOF materials for the C2H6/C2H4 separation from the hypothetical metal-organic frameworks (h-MOF) database using molecular simulations coupled with machine learning algorithms. In particular, the emphasis will be given to the structural and chemical properties of the MOF in screening the materials |
Pagination: | xix, 120p |
URI: | http://hdl.handle.net/10603/571288 |
Appears in Departments: | CHEMICAL ENGINEERING |
Files in This Item:
File | Description | Size | Format | |
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01 tittle.pdf | Attached File | 69.08 kB | Adobe PDF | View/Open |
02 certificate.pdf | 84.87 kB | Adobe PDF | View/Open | |
02 declaration.pdf | 92.38 kB | Adobe PDF | View/Open | |
03 synopsis.pdf | 49.81 kB | Adobe PDF | View/Open | |
04 acknowledgement.pdf | 47.09 kB | Adobe PDF | View/Open | |
05 contents.pdf | 59.95 kB | Adobe PDF | View/Open | |
06 list of tables.pdf | 88.79 kB | Adobe PDF | View/Open | |
07 list of figures.pdf | 122.72 kB | Adobe PDF | View/Open | |
08 chapter 1.pdf | 1.66 MB | Adobe PDF | View/Open | |
09 chapter 2.pdf | 1.23 MB | Adobe PDF | View/Open | |
10 chapter 3.pdf | 5.77 MB | Adobe PDF | View/Open | |
11 chapter 4.pdf | 2.87 MB | Adobe PDF | View/Open | |
12 chapter 5.pdf | 2.91 MB | Adobe PDF | View/Open | |
13 chapter 6.pdf | 104.64 kB | Adobe PDF | View/Open | |
14 list of publications.pdf | 63.78 kB | Adobe PDF | View/Open | |
15 bibliograhy.pdf | 140.11 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 172.74 kB | Adobe PDF | View/Open |
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