[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126941-en":3,"doc-seo-126941-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},126941,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning Applications in Fuels Research - Doctoral Thesis Abstract","Energy and climate crises require engineering solutions that reduce dependence on fossil fuels. Bio-fuels are highlighted as a practical alternative since they can be used with limited automotive redesign and may be more suitable for countries where electricity generation still relies on fossil fuels. This thesis demonstrates machine learning and chemical informatics approaches to accelerate renewable fuel development. The work focuses on regression for physical and chemical properties, interpretation of QSPR models, applicability domain and outlier detection, and inverse generative modeling with GAN and LSTM to propose novel knock-resistant structures.","Machine learning applications in fuels  \nresearch  \nSergey Anufriev  \nDepartment of Mechanical Engineering University College London  \nThis dissertation is submitted for the degree of Doctor of Philosophy  \nNovember 2023  \nDeclaration  \nI, Sergey Anufriev confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis  \nSergey Anufriev November 2023  \nAbstract  \nEnergy and climate crises both require engineering solutions to the dependence on fossil fuels. Bio-fuels can be a good alternative, because these fuels do not require significant change in the automotive design and can be used in poorer countries, where electricity production is fossil fuels based and electrical vehicles are not yet affordable.  \nThis research was done to demonstrate applications of machine learning and chemical informatics in fuel research in order to accelerate the development of the future renewable fuels. At the moment these applications are limited to regression models of fuels physical and chemical properties, which are tested and compared on different sets of test molecules. This is contradictory to chemical informatics research, where models compared use the same train and test sets of molecules.  \nThree major research directions were undertaken including interpretation of octane quantitative structure activity model, establishing fuel properties applicability domain and the inverse structure generative model. The aim of the first direction is to show that more insights apart from predictions can be taken from the fuel structure activity models. Literature review was taken to find suitable interpretation method. It was found that model and input agnostic method is best suited since it does not introduce bias from particular algorithm type and is chemically intuitive. The developed interpretation showed agreement with 6 known structure knock ignition relationships.  \nThe goal of the second direction goal was to find limitations on what fuel structure properties can be predicted given the available data-sets. This was achieved by finding a relationship between test compounds properties prediction errors and four similarity measures to the training set of compounds. The outlier detection algorithms successfully found such a trend, while the distance based approaches did not.  \nLastly, the generative modelling goal was to suggest new fuel like structures, which can not be found by intuition. Two models were investigated, a general adversarial (GAN) network and auto-regressive LSTM model. The later model generated structures which were not present in either of the generative modelling training set or the octane database, and showed some traits of highly knock resistant compounds.  \nImpact Statement  \nThis research focused on exploring applications of machine learning and chemical informatics in the future renewable fuels for spark ignition engines. Throughout the research, valuable scientific insights were gained. These included what fuel chemical structures can be predicted given their properties datasets, interpretations behind fuels quantitative structure activity models by methods used in drug discovery and the prototype fuels structure generation by generative machine learning models. These results can benefit the academic community and industrial companies as follows.  \nIn academic research, the common methods in studying fuels properties are experiments and molecular dynamics. Experimental work requires expertise, funding and time to conduct a study. Molecular dynamics methods despite being rigorous in terms of physics are computationally expensive and require the knowledge of intermediate combustion reactions. Future renewable fuels might come from sources not previously known. Consequently there could be too many potential structures to make experiments. This research helps to evaluate compounds before an experiment takes place, by finding","cbCaiobv2Z8zpG4E","https://ap.wps.com/l/cbCaiobv2Z8zpG4E","pdf",4050754,1,145,"English","en",105,"# Abstract\n# Impact Statement\n# Acknowledgements","[{\"question\":\"Why does the thesis focus on bio-fuels and renewable alternatives?\",\"answer\":\"It addresses energy and climate crises by reducing dependence on fossil fuels. Bio-fuels are positioned as feasible because they may require limited changes to automotive design and can support transport in regions where electricity is still fossil-based.\"},{\"question\":\"What limitations of existing machine learning setups does the thesis address?\",\"answer\":\"Current applications are described as being limited to regression models evaluated on different sets of test molecules. This differs from chemical informatics practice, where comparisons use the same train and test molecules sets.\"},{\"question\":\"How does the thesis approach fuel modeling beyond prediction?\",\"answer\":\"It includes interpretation of octane QSPR relationships, determination of applicability domains and outlier trends using similarity measures, and inverse structure generative modeling with GAN and LSTM to propose new fuel-like structures with knock-resistant traits.\"}]","Machine Learning Applications in Fuels Research - Doctoral Thesis Abstract | PDF",1785935792,365,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-applications-in-fuels-research-doctoral-thesis-abstract","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-applications-in-fuels-research-doctoral-thesis-abstract/126941/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does the thesis focus on bio-fuels and renewable alternatives?","Question",{"text":76,"@type":77},"It addresses energy and climate crises by reducing dependence on fossil fuels. Bio-fuels are positioned as feasible because they may require limited changes to automotive design and can support transport in regions where electricity is still fossil-based.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitations of existing machine learning setups does the thesis address?",{"text":81,"@type":77},"Current applications are described as being limited to regression models evaluated on different sets of test molecules. This differs from chemical informatics practice, where comparisons use the same train and test molecules sets.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the thesis approach fuel modeling beyond prediction?",{"text":85,"@type":77},"It includes interpretation of octane QSPR relationships, determination of applicability domains and outlier trends using similarity measures, and inverse structure generative modeling with GAN and LSTM to propose new fuel-like structures with knock-resistant traits.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]