Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal
In the past two decades, the mining sector has increasingly embraced simulation and modelling techniques for decision-making processes. This adoption has facilitated enhanced process control and optimisation, enabling access to valuable data such as precise granulometry measurements, improved recove...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Minerals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-163X/13/11/1450 |
| _version_ | 1797458234135543808 |
|---|---|
| author | Nelson Herrera María Sinche Gonzalez Jarkko Okkonen Raul Mollehuara |
| author_facet | Nelson Herrera María Sinche Gonzalez Jarkko Okkonen Raul Mollehuara |
| author_sort | Nelson Herrera |
| collection | DOAJ |
| description | In the past two decades, the mining sector has increasingly embraced simulation and modelling techniques for decision-making processes. This adoption has facilitated enhanced process control and optimisation, enabling access to valuable data such as precise granulometry measurements, improved recovery rates, and the ability to forecast outcomes. Soft computing techniques, such as artificial neural networks and fuzzy algorithms, have emerged as viable alternatives to traditional statistical approaches, where the complex and non-linear nature of the mineral processing stages requires careful selection. This research examines the up-to-date use of soft computing techniques within the mining sector, with a specific emphasis on comminution, flotation, and pyrometallurgical and hydrometallurgical processes, and the selection of soft computing techniques and strategies for identifying key variables. From this, a soft computing approach is presented to enhance the monitoring and prediction accuracy for mineral waste disposal, specifically focusing on tailings and spent heap leaching spoils database treatment. However, the accessibility and quality of data are crucial for the long-term application of soft computing technology in the mining industry. Further research is needed to explore the full potential of soft computing techniques and to address specific challenges in mining and mineral processing. |
| first_indexed | 2024-03-09T16:35:05Z |
| format | Article |
| id | doaj.art-49705edd8c294e7398d908503fce0e2d |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-03-09T16:35:05Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-49705edd8c294e7398d908503fce0e2d2023-11-24T14:57:44ZengMDPI AGMinerals2075-163X2023-11-011311145010.3390/min13111450Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste DisposalNelson Herrera0María Sinche Gonzalez1Jarkko Okkonen2Raul Mollehuara3Oulu Mining School, University of Oulu, P.O. Box 3000, 90570 Oulu, FinlandOulu Mining School, University of Oulu, P.O. Box 3000, 90570 Oulu, FinlandGeological Survey of Finland GTK, Vuorimiehentie 5, P.O. Box 96, FI-02151 Espoo, FinlandOulu Mining School, University of Oulu, P.O. Box 3000, 90570 Oulu, FinlandIn the past two decades, the mining sector has increasingly embraced simulation and modelling techniques for decision-making processes. This adoption has facilitated enhanced process control and optimisation, enabling access to valuable data such as precise granulometry measurements, improved recovery rates, and the ability to forecast outcomes. Soft computing techniques, such as artificial neural networks and fuzzy algorithms, have emerged as viable alternatives to traditional statistical approaches, where the complex and non-linear nature of the mineral processing stages requires careful selection. This research examines the up-to-date use of soft computing techniques within the mining sector, with a specific emphasis on comminution, flotation, and pyrometallurgical and hydrometallurgical processes, and the selection of soft computing techniques and strategies for identifying key variables. From this, a soft computing approach is presented to enhance the monitoring and prediction accuracy for mineral waste disposal, specifically focusing on tailings and spent heap leaching spoils database treatment. However, the accessibility and quality of data are crucial for the long-term application of soft computing technology in the mining industry. Further research is needed to explore the full potential of soft computing techniques and to address specific challenges in mining and mineral processing.https://www.mdpi.com/2075-163X/13/11/1450mineral extractionsoft computingprocess controlprediction accuracyartificial neural networksexpert systems |
| spellingShingle | Nelson Herrera María Sinche Gonzalez Jarkko Okkonen Raul Mollehuara Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal Minerals mineral extraction soft computing process control prediction accuracy artificial neural networks expert systems |
| title | Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal |
| title_full | Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal |
| title_fullStr | Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal |
| title_full_unstemmed | Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal |
| title_short | Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal |
| title_sort | soft computing application in mining mineral processing and metallurgy with an approach to using it in mineral waste disposal |
| topic | mineral extraction soft computing process control prediction accuracy artificial neural networks expert systems |
| url | https://www.mdpi.com/2075-163X/13/11/1450 |
| work_keys_str_mv | AT nelsonherrera softcomputingapplicationinminingmineralprocessingandmetallurgywithanapproachtousingitinmineralwastedisposal AT mariasinchegonzalez softcomputingapplicationinminingmineralprocessingandmetallurgywithanapproachtousingitinmineralwastedisposal AT jarkkookkonen softcomputingapplicationinminingmineralprocessingandmetallurgywithanapproachtousingitinmineralwastedisposal AT raulmollehuara softcomputingapplicationinminingmineralprocessingandmetallurgywithanapproachtousingitinmineralwastedisposal |