[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119848-en":3,"doc-seo-119848-105":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},119848,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting The Homogeneous Ignition Delay Time Of Gasoline Fuel Blends Using Machine Learning - Master’s Thesis","Transportation emissions reductions require optimizing combustion strategies and identifying renewable gasoline fuel surrogates. Ignition delay time (IDT) is a key parameter for internal combustion engine efficiency and environmental impact, yet it is difficult to study because of broad magnitude ranges, strong nonlinearity, and the negative temperature coefficient region. Chemical kinetic simulations with detailed reactions are computationally demanding. This thesis uses machine learning to predict IDT of toluene primary reference fuel using Cantera-derived data, training and validating four algorithms with Sobol and linear sequence datasets to improve sampling coverage and predictive accuracy.","Master’s Programme in Advanced Energy Solution  \nPredicting The Homogeneous Ignition Delay Time Of Gasoline Fuel Blends Using Machine Learning  \nLicheng Liu  \nMaster’s thesis 2023  \nCopyright ©2023 Licheng Liu  \n\n| Author Licheng Liu |\n| --- |\n| Title of thesis Predicting The Homogeneous Ignition Delay Time Of Gasoline Fuel Blends Using Machine Learning |\n| Programme Advanced energy solution |\n| Major Energy conversion process |\n| Thesis supervisor Prof. Santasalo-Aarnio Annukka |\n| Thesis advisor(s) Dr ToldyArpad |\n| Date 10.08.2023 Number of pages 55 Language English |\n| Abstract\u003Cbr>The transportation sector contributes 25% of energy consumption worldwide in 2022. Some optimization approaches and strategies in the transportation sector need to be done to decrease carbon emissions globally. According to EU Commission, electric vehicles will replace CO2-emitting cars by 2035 in Europe, while during the vehicles transition period, traditional vehicles will dominate the market for a certain amount of time. Therefore, optimizing the combustion engine and finding renewable gasoline fuel surrogates have become essential in recent years. The ignition delay time (IDT) is a crucial parameter for optimizing internal combustion engines, particularly from the energy efficiency perspective and environmental impacts. Investigation of IDT can contribute to the selection of gasoline fuel blends and the development of renewable gasoline fuel surrogates. However, the ignition delay time presents challenges due to its multiple magnitudes range, non-linear relationships, and negative temperature coefficient region. Additionally, chemical kinetic simulations demand significant computational resources due to complex reactions and components.\u003Cbr>This study aimstouse machine learning to predict the IDT of toluene primary reference fuel. The model developed in this study will overcome the limitations of the experimental method and chemical kinetic simulation as an alternative. Two linear sequence and Sobol sequence datasets were constructed based on the chemical kinetic simulation data obtained from Cantera. This study validated, trained, and tested four machine learning algorithms, including random forest, extra tree, support vector regressor, and gradient boosting.\u003Cbr>The results show that using the Sobol sequence with better sample filling properties, the gradient boosting model is the most accurate and effective model to predict the IDTof toluene primary reference fuel. Notably, the validation comparison results among experimental values from other literature, predictions in this machine learning model, and simulation results from the chemical kinetic model indicate that the main challenge to predict IDT lies in the chemical kinetic mechanism limitations rather than machine learning approaches weakness in this study. |\n| Keywords machine learning, ignition delay time, Sobol sequence, combustion |\n\nTable of contents  \nPreface and acknowledgements ................................................................... 5  \nSymbols and abbreviations .......................................................................... 6  \nSymbols .................................................................................................... 6  \nOperators ................................................................................................. 6  \nAbbreviations ........................................................................................... 6  \n1 Introduction.......................................................................................... 8  \n1.1 Thesis structure .............................................................................. 9  \n2 Background......................................................................................... 10  \n2.1 IDT and significance of IDT .......................................................... 10  \n2.2 Experimental IDT measurement method.......................................11  \n2.3 Chemi","cbCaivKDpHrLoMmW","https://ap.wps.com/l/cbCaivKDpHrLoMmW","pdf",1703086,1,55,"English","en",105,"# Preface and acknowledgements\n# Symbols and abbreviations\n## Symbols\n## Operators\n## Abbreviations\n# Introduction\n## Thesis structure\n# Background\n## IDT and significance of IDT\n## Experimental IDT measurement method\n## Chemical kinetics simulation of IDT\n## Machine learning\n### Linear model\n### Decision tree\n### Ensemble methods\n### Support vector regressor\n### Multilayer perceptron\n# Literature review\n## Experimental IDT research\n## Chemical kinetic IDT research\n## ML and its applications in the fuel and combustion field\n# Methodology\n## Dataset\n## Pre-processing\n## Cross-validation\n# Results and Discussion\n# Conclusions\n# Future research\n# References","[{\"question\":\"Why is ignition delay time (IDT) important in combustion optimization?\",\"answer\":\"IDT is crucial for optimizing internal combustion engines by influencing energy efficiency and environmental impacts. Studying IDT also supports selection of gasoline fuel blends and development of renewable surrogate fuels.\"},{\"question\":\"What data and model-building approach does the thesis use to predict IDT?\",\"answer\":\"The thesis constructs two datasets (linear and Sobol sequences) from chemical kinetic simulation data generated with Cantera. It then validates, trains, and tests four machine learning algorithms: random forest, extra tree, support vector regressor, and gradient boosting.\"},{\"question\":\"What is the most effective model and dataset combination found in the results?\",\"answer\":\"Using the Sobol sequence, the gradient boosting model achieves the highest accuracy and effectiveness for predicting the IDT of the toluene primary reference fuel. Validation comparisons suggest that limitations mainly stem from chemical kinetic mechanisms rather than machine learning weaknesses in this study.\"}]","Predicting The Homogeneous Ignition Delay Time Of Gasoline Fuel Blends Using Machine Learning - Master’s Thesis | PDF",1785726638,139,{"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},"predicting-the-homogeneous-ignition-delay-time-of-gasoline-fuel-blends-using-machine-learning-masters-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/predicting-the-homogeneous-ignition-delay-time-of-gasoline-fuel-blends-using-machine-learning-masters-thesis/119848/",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},"Why is ignition delay time (IDT) important in combustion optimization?","Question",{"text":75,"@type":76},"IDT is crucial for optimizing internal combustion engines by influencing energy efficiency and environmental impacts. Studying IDT also supports selection of gasoline fuel blends and development of renewable surrogate fuels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and model-building approach does the thesis use to predict IDT?",{"text":80,"@type":76},"The thesis constructs two datasets (linear and Sobol sequences) from chemical kinetic simulation data generated with Cantera. It then validates, trains, and tests four machine learning algorithms: random forest, extra tree, support vector regressor, and gradient boosting.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the most effective model and dataset combination found in the results?",{"text":84,"@type":76},"Using the Sobol sequence, the gradient boosting model achieves the highest accuracy and effectiveness for predicting the IDT of the toluene primary reference fuel. 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