[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121017-en":3,"doc-seo-121017-105":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},121017,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","APPLICATION OF MACHINE LEARNING IN HYDROGEN PRODUCTION VIA THE PROCESS OF SORBENT ENHANCED STEAM METHANE REFORMING - PhD Thesis","This PhD thesis explores the use of machine learning and computational methods to model process conditions and screen materials within sorbent enhanced steam methane reforming (SESMR) for carbon-abated hydrogen production. Hydrogen is positioned as a clean energy carrier, while current production remains tied to fossil fuels, limiting sustainability. The study addresses this challenge through machine learning, thermodynamic simulations, theoretical modelling, and new methodologies for low‑carbon hydrogen production, developing surrogate models, applying QSPR with inductive transfer learning, and screening MOFs for hydrogen storage.","PAULA NKULIKIYINKA  \nAPPLICATION OF MACHINE LEARNING IN HYDROGEN PRODUCTION VIA THE PROCESS OF SORBENT ENHANCED STEAM METHANE REFORMING  \nSCHOOL OF WATER, ENERGY AND ENVIRONMENT PhD in Energy and Power  \nPhD  \nAcademic Year: 2019-2023  \nSupervisor: Dr Peter T. Clough  \nAssociate Supervisors: Prof. Vasilije Manovic and Dr Stuart T.  \nWagland  \nSCHOOL OF WATER, ENERGY AND ENVIRONMENT  \nEnergy and Power  \nPhD  \nAcademic Year 2019-2023  \nPAULA NKULIKIYINKA  \nAPPLICATION OF MACHINE LEARNING IN HYDROGEN PRODUCTION VIA THE PROCESS OF SORBENT ENHANCED STEAM METHANE REFORMING  \nSupervisor: Dr Peter T. Clough  \nAssociate Supervisors: Prof. Vasilije Manovic and Dr Stuart T.  \nWagland  \nJune 2023  \nThis thesis is submitted in partial fulfilment of the requirements for  \nthe degree of PhD.  \n© Cranfield University 2023. All rights reserved. No part of this publication may be reproduced without the written permission of the  \ncopyright owner.  \nAcademic Integrity Declaration  \nI declare that:  \n• the thesis submitted has been written by me alone.  \n• the thesis submitted has not been previously submitted to this university or any other.  \n• that all content, including primary and/or secondary data, is true to the best of my knowledge.  \n• that all quotations and references have been duly acknowledged according to the requirements of academic research.  \nI understand that to knowingly submit work in violation of the above statement will be considered by examiners as academic misconduct.  \nAbstract  \nThis thesis is focused on the exploration of the use of machine learning and computational methods for modelling process conditions and for materials screening within the process of sorbent enhanced steam methane reforming (SESMR) for carbon-abated hydrogen production. Hydrogen is a clean, abundant and versatile energy carrier that can be used for a wide range of applications. However, the production of hydrogen is still largely dependent on fossil fuels, which presents a significant challenge for achieving a truly sustainable energy system.  \nThe purpose of this study is to address this challenge by exploring novel approaches to hydrogen production , namely using machine learning, thermodynamic simulations, theoretical modelling, and the proposal of new methodologies and materials for low-carbon hydrogen production.  \nThree main areas of work were conducted within this thesis, which include 1) two surrogate models have been developed and used to predict and estimate variables that would otherwise be difficult direct measured. ; 2) applying machine learning , namely quantitative structure–property relationship analysis (QSPR) has been employed in the exploration of combined sorbent catalyst material (CSCM) for SE-SMR; and 3) applying machine learning to screen suitable metal organic frameworks (MOFs) for the storage of the produced blue hydrogen.  \nFirstly, a surrogate model, was developed which was done by firstly simulating the model in Aspen Plus, applying a sensitivity analysis to gather a large dataset , then applying two multiple linear regression model, to observe the accuracy of predicting the gas concentration outputs. Two models were successfully developed with both models were accurate with high R2 values, all above 98% .  \nSecondly, the novel approach of QSPR with inductive transfer learning and datamining , was applied to develop two large databases of sorbent and catalyst properties, respectively. Then the developed machine learning models from these databases were applied , to predict the optimal conditions and precursor  \nmaterials for the highest performing CSCM, in terms of last cycle capacity and methane conversion. Lastly, a similar approach was applied for the screening of MOFs for the storage of hydrogen by using multiple linear regression , simple geometric descriptors, and patterns in data to identify a better performing MOF than the currently reported experimental MOFs in literature.  \nKeywords:  \nCarbon capture and stora","cbCaisdMSUOWRUXF","https://ap.wps.com/l/cbCaisdMSUOWRUXF","pdf",6817228,1,287,"English","en",105,"# Abstract\n## Problem background\n## Study purpose and methods\n## Surrogate modelling\n## QSPR and catalyst/sorbent screening\n## MOF screening for hydrogen storage","[{\"question\":\"What problem does this thesis focus on in hydrogen production?\",\"answer\":\"It targets the challenge that hydrogen production still relies largely on fossil fuels, which hinders a truly sustainable energy system. The work aims to support carbon-abated hydrogen production using SESMR.\"},{\"question\":\"Which machine learning approaches are used in the thesis?\",\"answer\":\"The thesis develops surrogate models using simulation data and multiple linear regression, applies QSPR with inductive transfer learning and data mining to study sorbent catalyst materials, and uses machine learning for MOF screening for hydrogen storage.\"},{\"question\":\"What is the role of MOFs in this research?\",\"answer\":\"Machine learning is used to screen suitable metal organic frameworks (MOFs) for storing the produced blue hydrogen. The approach seeks MOFs with improved performance compared with reported experimental MOFs in the literature.\"}]","APPLICATION OF MACHINE LEARNING IN HYDROGEN PRODUCTION VIA THE PROCESS OF SORBENT ENHANCED STEAM METHANE REFORMING - PhD Thesis | PDF",1785733326,723,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-in-hydrogen-production-via-the-process-of-sorbent-enhanced-steam-methane-reforming-phd-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-in-hydrogen-production-via-the-process-of-sorbent-enhanced-steam-methane-reforming-phd-thesis/121017/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this thesis focus on in hydrogen production?","Question",{"text":75,"@type":76},"It targets the challenge that hydrogen production still relies largely on fossil fuels, which hinders a truly sustainable energy system. The work aims to support carbon-abated hydrogen production using SESMR.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approaches are used in the thesis?",{"text":80,"@type":76},"The thesis develops surrogate models using simulation data and multiple linear regression, applies QSPR with inductive transfer learning and data mining to study sorbent catalyst materials, and uses machine learning for MOF screening for hydrogen storage.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the role of MOFs in this research?",{"text":84,"@type":76},"Machine learning is used to screen suitable metal organic frameworks (MOFs) for storing the produced blue hydrogen. The approach seeks MOFs with improved performance compared with reported experimental MOFs in the literature.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]