{"title":"通过卡车循环时间的分析和模拟改进油砂开采生产计划","authors":"E. Cervantes, S. Upadhyay, H. Askari-Nasab","doi":"10.15834/cimj.2019.1","DOIUrl":null,"url":null,"abstract":"A theoretical framework based on a detailed analysis of mine operations data from an oil sands mine in northern Alberta and the static simulation of truck cycle times is developed, verified and validated in this paper. Implementation of this framework provides better results than existing inhouse tools, which rely solely on manufacturer data, and thus aids in efficient equipment planning for life of mine plans. The use of this framework to modify existing productivity curve estimation methods currently in use at the mine site is also proposed. This method replaces “loaded flat haul” with “effective loaded flat haul” in the estimation of productivity. Validation of the model presents an over estimation of productivity by 4% against an underestimation of over 10% by the existing in-house method.","PeriodicalId":197002,"journal":{"name":"CIM Journal","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Improvements to production planning in oil sands mining through analysis and simulation of truck cycle times\",\"authors\":\"E. Cervantes, S. Upadhyay, H. Askari-Nasab\",\"doi\":\"10.15834/cimj.2019.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A theoretical framework based on a detailed analysis of mine operations data from an oil sands mine in northern Alberta and the static simulation of truck cycle times is developed, verified and validated in this paper. Implementation of this framework provides better results than existing inhouse tools, which rely solely on manufacturer data, and thus aids in efficient equipment planning for life of mine plans. The use of this framework to modify existing productivity curve estimation methods currently in use at the mine site is also proposed. This method replaces “loaded flat haul” with “effective loaded flat haul” in the estimation of productivity. Validation of the model presents an over estimation of productivity by 4% against an underestimation of over 10% by the existing in-house method.\",\"PeriodicalId\":197002,\"journal\":{\"name\":\"CIM Journal\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CIM Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15834/cimj.2019.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CIM Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15834/cimj.2019.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improvements to production planning in oil sands mining through analysis and simulation of truck cycle times
A theoretical framework based on a detailed analysis of mine operations data from an oil sands mine in northern Alberta and the static simulation of truck cycle times is developed, verified and validated in this paper. Implementation of this framework provides better results than existing inhouse tools, which rely solely on manufacturer data, and thus aids in efficient equipment planning for life of mine plans. The use of this framework to modify existing productivity curve estimation methods currently in use at the mine site is also proposed. This method replaces “loaded flat haul” with “effective loaded flat haul” in the estimation of productivity. Validation of the model presents an over estimation of productivity by 4% against an underestimation of over 10% by the existing in-house method.