{"title":"矿物加工中的人工智能与自然智能","authors":"S. G. Ozkan","doi":"10.37190/ppmp/167501","DOIUrl":null,"url":null,"abstract":"This article aims to introduce the terms NI-Natural Intelligence, AI-Artificial Intelligence, ML-Machine Learning, DL-Deep Learning, ES-Expert Systems and etc. used by modern digital world to mining and mineral processing and to show the main differences between them. As well known, each scientific and technological step in mineral industry creates huge amount of raw data and there is a serious necessity to firstly classify them. Afterwards experts should find alternative solutions in order to get optimal results by using those parameters and relations between them using special simulation software platforms. Development of these simulation models for such complex operations is not only time consuming and lacks real time applicability but also requires integration of multiple software platforms, intensive process knowledge and extensive model validation. An example case study is also demonstrated and the results are discussed within the article covering the main inferences, comments and decision during NI use for the experimental parameters used in a flotation related postgraduate study and compares with possible AI use.","PeriodicalId":49137,"journal":{"name":"Physicochemical Problems of Mineral Processing","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence versus natural intelligence in mineral processing\",\"authors\":\"S. G. Ozkan\",\"doi\":\"10.37190/ppmp/167501\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article aims to introduce the terms NI-Natural Intelligence, AI-Artificial Intelligence, ML-Machine Learning, DL-Deep Learning, ES-Expert Systems and etc. used by modern digital world to mining and mineral processing and to show the main differences between them. As well known, each scientific and technological step in mineral industry creates huge amount of raw data and there is a serious necessity to firstly classify them. Afterwards experts should find alternative solutions in order to get optimal results by using those parameters and relations between them using special simulation software platforms. Development of these simulation models for such complex operations is not only time consuming and lacks real time applicability but also requires integration of multiple software platforms, intensive process knowledge and extensive model validation. An example case study is also demonstrated and the results are discussed within the article covering the main inferences, comments and decision during NI use for the experimental parameters used in a flotation related postgraduate study and compares with possible AI use.\",\"PeriodicalId\":49137,\"journal\":{\"name\":\"Physicochemical Problems of Mineral Processing\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physicochemical Problems of Mineral Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.37190/ppmp/167501\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physicochemical Problems of Mineral Processing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.37190/ppmp/167501","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Artificial intelligence versus natural intelligence in mineral processing
This article aims to introduce the terms NI-Natural Intelligence, AI-Artificial Intelligence, ML-Machine Learning, DL-Deep Learning, ES-Expert Systems and etc. used by modern digital world to mining and mineral processing and to show the main differences between them. As well known, each scientific and technological step in mineral industry creates huge amount of raw data and there is a serious necessity to firstly classify them. Afterwards experts should find alternative solutions in order to get optimal results by using those parameters and relations between them using special simulation software platforms. Development of these simulation models for such complex operations is not only time consuming and lacks real time applicability but also requires integration of multiple software platforms, intensive process knowledge and extensive model validation. An example case study is also demonstrated and the results are discussed within the article covering the main inferences, comments and decision during NI use for the experimental parameters used in a flotation related postgraduate study and compares with possible AI use.
期刊介绍:
Physicochemical Problems of Mineral Processing is an international, open access journal which covers theoretical approaches and their practical applications in all aspects of mineral processing and extractive metallurgy.
Criteria for publication in the Physicochemical Problems of Mineral Processing journal are novelty, quality and current interest. Manuscripts which only make routine use of minor extensions to well established methodologies are not appropriate for the journal.
Topics of interest
Analytical techniques and applied mineralogy
Computer applications
Comminution, classification and sorting
Froth flotation
Solid-liquid separation
Gravity concentration
Magnetic and electric separation
Hydro and biohydrometallurgy
Extractive metallurgy
Recycling and mineral wastes
Environmental aspects of mineral processing
and other mineral processing related subjects.