[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123832-en":3,"doc-seo-123832-105":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":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},123832,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","MACHINE LEARNING BASED PREDICTION MODELS FOR FLAMMABILITY CHARACTERISTICS IN THE CHEMICAL INDUSTRY - A Thesis - Master of Science","Flammability characteristics are essential for process safety in the chemical industry, where frequent changes demand timely decisions to prevent, mitigate, and prepare for incidents. This thesis presents machine-learning prediction models grounded in Quantitative Structure-Property Relationship (QSPR). Models are built using XGBoost with molecular descriptors generated through Mordred and RDKit, optimized via k-fold cross validation and hyperparameter tuning on published experimental data. Statistical evaluation shows R² values above 0.9, indicating strong predictive performance. Empirical formulas extend applicability to practical industrial needs, supporting both safety and production goals.","MACHINE LEARNING BASED PREDICTION MODELS FOR FLAMMABILITY  \nCHARACTERISTICS IN THE CHEMICAL INDUSTRY  \nA Thesis  \nby  \nCHI-YANG LI  \nSubmitted to the Graduate and Professional School of Texas A&M University  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nChair of Committee, Committee Members,  \nHead of Department,  \nQingsheng Wang Costas Kravaris Ka Wai Wong Victor Ugaz  \nDecember 2022  \nMajor Subject: Safety Engineering Copyright 2022 Chi-Yang Li  \nABSTRACT  \nThe prediction models based on Quantitative Structure-Property Relationship (QSPR) and machine learning have been widely applied in the research. Flammability characteristics are critical information for the chemical industry to prevent, mitigate, and prepare for the potential process safety incidents. In the chemical industry, changes happen often, and sometimes employees are required to make timely decisions to maintain the safety and avoid affecting production. Machine learning based models could serve as accessible references because of high accuracy and reliability on its predictions. Here we show that the models on the basis of QSPRand gradient boosting could have excellent predictions for flammability characteristics of concern. With the application of Xgboost, Mordred, and RDkit libraries in Python 3, k-fold cross validation, and published experimental data, we tune the combination of hyperparameters to obtain the best QSPR models. According to the statistical assessments, all the models’ R2 are higher than 0.9; thus, the evaluations indicate the good predictions. Moreover, with the use of empirical formulas, the applicability of the predictions on flammability characteristics is broadened to meet the needs in the chemical industry. With accessible, efficient, and reliable models to predict the changes, it makes the chemical industry be able to seek for the safety and production at the same time. Therefore, safety is no longer a choice to make, but a thing to do.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supported by a thesis committee consisting of Dr. Qingsheng Wang and Dr. Costas Kravaris ofthe Department of Chemical Engineering and Ka Wai Wong of the Department of Statistics.  \nAll other work conducted for the thesis (or) dissertation was completed by the student independently.  \nFunding Sources  \nGraduate study was supported by The Texas Public Education Grant for International  \nStudents.  \nACKNOWLEDGEMENT  \nI would like to thank my advisor, Dr. Qingsheng Wang, for his support and guidance. With his support, I could have an opportunity to work on a project from an international company to combine my research and the practices in the chemical industry at the same time. This is what I would like to do in my future professional career; it is amazing that I could experience it in advance with the guide from Dr. Wang. Furthermore, I also could obtain an opportunity to pursue my PhD degree with Dr. Wang to dig into profound knowledge and application in process safety field and chemical engineering field. I am looking forward about it.  \nI would like to thank my lovely girlfriend, Ms. Ya-Han Ho, for her support and accompany. Her full support means a lot to me. Even studying abroad is always a rigorous challenge for couples, she still encouraged me to pursue what I want to do. I will always try my best to keep the happiness and wellness for her.  \nI would like to thank my parents. Their health and support are always a critical point that I could focus on my academics and life in College Station with all my heart. Their giving and dedication will always be kept in my mind.  \nAt last, I also would like to thank all my friends. Studying abroad is never an easy thing. With the company of all my friends, this is also a good factor for me to maintain the work and life balance.  \nTABLE OF CONTENTS  \nPage  \nABSTRACT..................................................................................................","cbCaiivihx0vK8JV","https://ap.wps.com/l/cbCaiivihx0vK8JV","pdf",2554519,1,73,"English","en",105,"# ABSTRACT\n# CONCEPTUAL FRAMEWORK\n## Process Safety Management in the Chemical Industry\n## Prediction Models for Flammability Characteristics\n# PREDICTION MODELS\n## Introduction\n## Model Development (QSPR and Machine Learning)\n# THESIS STRUCTURE\n## Thesis Overview","[{\"question\":\"What problem do the proposed models address in the chemical industry?\",\"answer\":\"They aim to predict flammability characteristics that are critical for preventing, mitigating, and preparing for potential process safety incidents, enabling timely safety decisions as conditions change.\"},{\"question\":\"How are the prediction models constructed in the thesis?\",\"answer\":\"The work builds QSPR-based machine learning models, using XGBoost along with Mordred and RDKit descriptor libraries in Python, and optimizes hyperparameters through k-fold cross validation on published experimental data.\"},{\"question\":\"What evidence indicates that the models perform well?\",\"answer\":\"The statistical assessments report R² values higher than 0.9 across the models, showing reliable predictive accuracy for the flammability characteristics of interest.\"}]","MACHINE LEARNING BASED PREDICTION MODELS FOR FLAMMABILITY CHARACTERISTICS IN THE CHEMICAL INDUSTRY - A Thesis - Master of Science | PDF",1785818793,184,{"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},"machine-learning-based-prediction-models-for-flammability-characteristics-in-the-chemical-industry-a-thesis-master-of-science","",{"@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/machine-learning-based-prediction-models-for-flammability-characteristics-in-the-chemical-industry-a-thesis-master-of-science/123832/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem do the proposed models address in the chemical industry?","Question",{"text":75,"@type":76},"They aim to predict flammability characteristics that are critical for preventing, mitigating, and preparing for potential process safety incidents, enabling timely safety decisions as conditions change.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the prediction models constructed in the thesis?",{"text":80,"@type":76},"The work builds QSPR-based machine learning models, using XGBoost along with Mordred and RDKit descriptor libraries in Python, and optimizes hyperparameters through k-fold cross validation on published experimental data.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence indicates that the models perform well?",{"text":84,"@type":76},"The statistical assessments report R² values higher than 0.9 across the models, showing reliable predictive accuracy for the flammability characteristics of interest.","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"]