Artificial intelligence versus natural intelligence in mineral processing

• Autorzy: Özkan Şafak Gökhan

Identyfikatory

DOI

10.37190/ppmp/167501

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

NI; natural intelligence; AI; artificial intelligence; ML; machine learning; DL; deep learning; ES; expert systems; mineral processing

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2023
• Tom: Vol. 59, iss. 5
• Strony: art. no. 167501
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Özkan Şafak Gökhan

• Turkish-German University, The Institute of the Graduate Studies in Science and Engineering, Department of Robotics and Intelligent Systems, 34820, Beykoz, Istanbul, Turkiye

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).