[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118950-en":3,"doc-seo-118950-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118950,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Predicting Adsorbent Performance for Carbon Capture using Machine Learning Models","Carbon capture is a promising pathway to mitigate climate change from anthropogenic sources, with adsorption emerging as an actively studied technology. This thesis develops machine-learning models to estimate adsorbent performance from widely reported adsorbent textural properties, addressing the challenge that data organization and testing conditions vary across publications. The work identifies which parameters can represent adsorbents using a general review of textural properties and a description of adsorbent types. Multiple ML approaches predict adsorption capacity directly and, in additional models, target isotherm parameters to compare results derived from modeled isotherms versus direct capacity predictions. A case study using an Aspen Adsorption bed model investigates modeled-bed behavior and highlights limited general-setting performance despite strong training results.","Predicting Adsorbent Performance for Carbon Capture using Machine Learning Models  \nBy  \nTerry Ming Yiu So  \nA thesis  \npresented to the University of Waterloo  \nin fulfillment of the  \nthesis requirement for the degree of  \nMaster of Applied Science  \nin  \nChemical Engineering  \nWaterloo, Ontario, Canada, 2023  \n© Terry Ming Yiu So, 2023  \nAuthor’s Declaration  \nI hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners.  \nI understand that my thesis may be made electronically available to the public.  \nAbstract  \nCarbon capture is a promising way to slow down climate change from anthropogenic sources. One of the carbon capture technologies that is being actively researched is adsorption. Given the increasing amount of literature that present novel ideas, being able to predict this information based on adsorbent textural properties is desirable. In this thesis, machine learning is used to construct a model to estimate adsorbent performance.  \nCurrently, many groups are researching novel adsorbents simultaneously. While beneficial for development, the organization of data varies between papers, as preparation and testing conditions affect the adsorbent performance. Determining the adsorbent data representative of theadsorbent is a difficult challenge, given the presentation and availability are varied. Thus, a section of the thesis focuses on determining which parameters are able to represent the adsorbents while being commonly reported in the literature. A general review of the textural properties is presented. The adsorbent types are also described in the literature review to capture the differences present in adsorbents.  \nModels trained using five different machine learning methods are examined in detail. The models use the adsorbent’s textural properties along with the testing conditions to estimate the adsorption capacity for an adsorbent. Additionally, the isotherm parameters are also targeted in additional models. Comparisons are performed between the directly predicted capacity and the capacity estimated from the modeled isotherm parameters; this is performed over the different machine learning methods used. A section of the thesis is dedicated to examining a sample adsorbent in more detail based on the ML model. An Aspen Adsorption bed model is used to investigate the effects of the differences between the model. The models exhibit limited performance in the general setting, despite the good training performance. The performance is approximate at a high level, but the requirements to capture the relationship between the adsorbentsand its performance is not readily available.  \nAcknowledgements  \nI thank my supervisor Professor Ali Elkamel for his support and supervision during the process of the degree. He has graciously provided a position where I am fortunate enough to work. In addition, I have worked in collaboration with CANMET at Natural Resources Canada (NRCAN) . They have provided their expertise and guidance that shaped the project. The collaboration works adjacent to this work with sections of each project drawing from the data set we have created.  \nTable of Contents  \nAuthor’s Declaration....................................................................................................................... ii  \nAbstract .......................................................................................................................................... iii  \nAcknowledgements ........................................................................................................................ iv  \nList of Figures ............................................................................................................................... vii  \nList of Tables ................................................................................................................................. ix  \nChapter 1","cbCaimJsAC30n1zd","https://ap.wps.com/l/cbCaimJsAC30n1zd","pdf",2412766,1,82,"English","en",105,"# Author’s Declaration\n# Abstract\n# Acknowledgements\n# List of Figures\n# List of Tables\n# Chapter 1: Introduction\n## Background\n## Objective\n## Outline\n# Chapter 2: Literature Review\n## Flue Gas\n## Carbon Capture Technologies\n## Adsorption Process\n## Adsorbent Types","[{\"question\":\"Why is predicting adsorbent performance important for carbon capture research?\",\"answer\":\"Predicting performance helps organize and leverage the growing volume of adsorption literature by estimating outcomes from adsorbent textural properties.\"},{\"question\":\"How does the thesis handle variation in adsorbent data across papers?\",\"answer\":\"It focuses on determining which parameters are commonly reported and able to represent adsorbents despite differences in preparation and testing conditions.\"},{\"question\":\"What comparison is performed between model outputs in the ML study?\",\"answer\":\"Models are evaluated by comparing adsorption capacity predicted directly with capacity estimated from modeled isotherm parameters across different machine learning methods.\"}]","Predicting Adsorbent Performance for Carbon Capture using Machine Learning Models | 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is predicting adsorbent performance important for carbon capture research?","Question",{"text":75,"@type":76},"Predicting performance helps organize and leverage the growing volume of adsorption literature by estimating outcomes from adsorbent textural properties.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis handle variation in adsorbent data across papers?",{"text":80,"@type":76},"It focuses on determining which parameters are commonly reported and able to represent adsorbents despite differences in preparation and testing conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"What comparison is performed between model outputs in the ML study?",{"text":84,"@type":76},"Models are evaluated by comparing adsorption capacity predicted directly with capacity estimated from modeled isotherm parameters across different machine learning 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