{"title":"矿物加工厂溪流取样专家系统的开发","authors":"C Ketata , M.C Rockwell , D Riordan","doi":"10.1016/S0954-1810(00)00002-9","DOIUrl":null,"url":null,"abstract":"<div><p>Stream sampling is essential for the performance assessment of a mineral processing plant. This process generates errors that are caused by stream material heterogeneity and incorrect cutter features. To control the sampling process efficiently, it is very important to evaluate and minimize the sampling errors. The objective of this paper is to introduce two expert systems for stream sampling in mineral processing plants. The first one is intended to inspect the correctness of sampling operations. It is called Sampling Correctness Inspector (SCI). The second one is destined for the evaluation of sampling errors. It is named Sampling Error Evaluator (SEE). These expert systems are validated successfully.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2000-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(00)00002-9","citationCount":"9","resultStr":"{\"title\":\"Development of expert systems for stream sampling in mineral processing plants\",\"authors\":\"C Ketata , M.C Rockwell , D Riordan\",\"doi\":\"10.1016/S0954-1810(00)00002-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Stream sampling is essential for the performance assessment of a mineral processing plant. This process generates errors that are caused by stream material heterogeneity and incorrect cutter features. To control the sampling process efficiently, it is very important to evaluate and minimize the sampling errors. The objective of this paper is to introduce two expert systems for stream sampling in mineral processing plants. The first one is intended to inspect the correctness of sampling operations. It is called Sampling Correctness Inspector (SCI). The second one is destined for the evaluation of sampling errors. It is named Sampling Error Evaluator (SEE). These expert systems are validated successfully.</p></div>\",\"PeriodicalId\":100123,\"journal\":{\"name\":\"Artificial Intelligence in Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/S0954-1810(00)00002-9\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence in Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0954181000000029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0954181000000029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of expert systems for stream sampling in mineral processing plants
Stream sampling is essential for the performance assessment of a mineral processing plant. This process generates errors that are caused by stream material heterogeneity and incorrect cutter features. To control the sampling process efficiently, it is very important to evaluate and minimize the sampling errors. The objective of this paper is to introduce two expert systems for stream sampling in mineral processing plants. The first one is intended to inspect the correctness of sampling operations. It is called Sampling Correctness Inspector (SCI). The second one is destined for the evaluation of sampling errors. It is named Sampling Error Evaluator (SEE). These expert systems are validated successfully.
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