[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127990-en":3,"doc-seo-127990-105":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},127990,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Advancing Responsible Mining - Machine Learning and MWD Data for Blast Optimization in Quarry Operations","The PhD project advances blasting operations in studied quarries by applying data analysis and machine learning to improve blasting efficiency, environmental sustainability, and economic performance. Using drilling and blasting workflows at Brønnøy Kalk marble and Elkem Tana quartzite, the work targets production efficiency and ore quality. Field studies and measurement-while-drilling (MWD) data support predictive models for disturbed-zone extent, ore quality, and lithology. A dedicated MWD data rod-change effect filtering algorithm enhances data accuracy and generality across drilling methods, enabling more precise, responsible quarry optimization.","Doctoral theses at NTNU, 2025:111  \nOzge Akyildiz  \nAdvancing Responsible Mining: Machine Learning and MWD Data for Blast Optimization in Quarry Operations  \nDoctora l thesis  \nNT NU  \nNorwegian University of Science and Technology Thesis for the Degree of Ph ilosophiae Doctor  \nFaculty of Engineering Department of Geosciences  \nOzge Akyildiz  \nAdvancing Responsible Mining: Machine Learning and MWD Data for Blast Optimization in Quarry Operations  \nThesis for the Degree of Philosophiae Doctor Trondheim, March 2025  \nNorwegian University of Science and Technology Faculty of Engineering  \nDepartment of Geosciences  \nNTNU  \nNorwegian University of Science and Technology Thesis for the Degree of Philosophiae Doctor Faculty of Engineering  \nDepartment of Geosciences  \n© Ozge Akyildiz  \nISBN 978-82-326-8820-3 (printed ver.)  \nISBN 978-82-326-8819-7 (electronic ver.) ISSN 1503-8181 (printed ver.)  \nISSN 2703-8084 (online ver.) Doctoral theses at NTNU, 2025:111 Printed by NTNU Grafisk senter  \nAbstract  \nThe PhD project aims to bring substantial advancements to blasting operations in the studied quarries through the application of data analysis and machine learning techniques. This approach will provide significant improvements in blasting efficiency, environmental sustainability and economic performance and identify new applications for mining operations at Brønnøy Kalk marble and Elkem Tana quartzite quarry. Each quarry utilizes drilling and blasting methods for ore extraction. Despite differences in the types of ore produced and the key performance indicators for the quarries, the primary goal remains consistent: to enhance production efficiency and improve ore quality.  \nThe initial steps involve conducting field studies to gather site-specific information and collecting measurement while drilling (MWD) data from drill machines to build predictive models of the rock properties. These models are fundamental for improving product quality and optimizing blast design, ultimately increasing the production rate in the mines. By integrating these models into the blasting process, the aim is to predict critical parameters such as the extent of the disturbed zone, ore quality, and lithology. Accurate prediction of the disturbed zone allows for improved control of fragmentation, minimizing overbreak and underbreak, which ensures the structural stability of surrounding rock. Predicting ore quality helps in selectively targeting high-grade material, reducing dilution, and maximizing overall resource recovery. Similarly, determining lithology helps in guaranteeing that the extracted ore in the quartzite mine maintains the highest level of purity. Incorporating lithological predictions into blast design ensures that variations in rock strength, density, and structure are accounted for, enabling more efficient and effective blasting operations.  \nThe project also includes the MWD data rod change effect filtering algorithm, which provides a complete approach for all drilling methods and hole types, regardless of in the hole (ITH) or top hammer drilling and blast holes in quarry mining or grouting holes in tunnel construction. The goal is to enhance data accuracy by eliminating irrelevant or noisy data that may obscure key patterns and insights. The approach ensures that the data used for the analysis and model development is of high quality, which is essential for making precise predictions regarding rock properties and optimizing blasting operations.  \nIn summary, incorporating machine learning and mathematical models into mineral resource management enhances both the efficiency and sustainability of mining operations, while supporting the broader objectives of sustainable development goals set by the United Nations. These models encourage a more responsible approach to resource extraction, ensuring that mineral resources are used in ways that benefit both the economy and the environment.  \nii  \nAcknowledgements  \nIn this acknowledgment section,","cbCainq7it6cxvjL","https://ap.wps.com/l/cbCainq7it6cxvjL","pdf",26158782,1,175,"English","en",105,"# Abstract\n## Predictive modeling using MWD data\n## Disturbed zone, ore quality, and lithology prediction\n## MWD data filtering algorithm\n## Summary and sustainable mining impact\n# Acknowledgements\n## Supervisors and collaborators\n## Industry support and co-author contributions\n## Colleagues, family, and dedication\n# Courses taken during the PhD education","[{\"question\":\"What is the main goal of the PhD project in quarry blasting?\",\"answer\":\"The project aims to significantly improve blasting operations by using data analysis and machine learning to raise efficiency, environmental sustainability, and economic performance while enhancing production efficiency and ore quality.\"},{\"question\":\"How does measurement while drilling (MWD) data support the proposed models?\",\"answer\":\"MWD data collected from drill machines, combined with field studies, is used to build predictive models of rock properties. These models help predict key parameters used in blast optimization.\"},{\"question\":\"What role does the disturbed zone prediction play in blasting control?\",\"answer\":\"Accurate prediction of the disturbed zone enables better control of fragmentation, reducing overbreak and underbreak and improving the structural stability of surrounding rock.\"}]","Advancing Responsible Mining - Machine Learning and MWD Data for Blast Optimization in Quarry Operations | PDF",1785943695,441,{"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},"advancing-responsible-mining-machine-learning-and-mwd-data-for-blast-optimization-in-quarry-operations","",{"@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/advancing-responsible-mining-machine-learning-and-mwd-data-for-blast-optimization-in-quarry-operations/127990/",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","2026-08-23","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},"What is the main goal of the PhD project in quarry blasting?","Question",{"text":76,"@type":77},"The project aims to significantly improve blasting operations by using data analysis and machine learning to raise efficiency, environmental sustainability, and economic performance while enhancing production efficiency and ore quality.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does measurement while drilling (MWD) data support the proposed models?",{"text":81,"@type":77},"MWD data collected from drill machines, combined with field studies, is used to build predictive models of rock properties. These models help predict key parameters used in blast optimization.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does the disturbed zone prediction play in blasting control?",{"text":85,"@type":77},"Accurate prediction of the disturbed zone enables better control of fragmentation, reducing overbreak and underbreak and improving the structural stability of surrounding rock.","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"]