A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching Process
Chilean mining is one of the main productive industries in the country. It plays a critical role in the development of Chile, so process planning is an essential task in achieving high performance. This task involves considering mineral resources and operating conditions to provide an optimal and re...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/11/7/1025 |
| _version_ | 1797528752108863488 |
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| author | Manuel Saldaña Purísima Neira Víctor Flores Pedro Robles Carlos Moraga |
| author_facet | Manuel Saldaña Purísima Neira Víctor Flores Pedro Robles Carlos Moraga |
| author_sort | Manuel Saldaña |
| collection | DOAJ |
| description | Chilean mining is one of the main productive industries in the country. It plays a critical role in the development of Chile, so process planning is an essential task in achieving high performance. This task involves considering mineral resources and operating conditions to provide an optimal and realistic copper extraction and processing strategy. Performing planning modes of operation requires a significant effort in information generation, analysis, and design. Once the operating mode plans have been made, it is essential to select the most appropriate one. In this context, an intelligent system that supports the planning and decision-making of the operating mode has the potential to improve the copper industry’s performance. In this work, a knowledge-based decision support system for managing the operating mode of the copper heap leaching process is presented. The domain was modeled using an ontology. The interdependence between the variables was encapsulated using a set of operation rules defined by experts in the domain and the process dynamics was modeled utilizing an inference engine (adjusted with data of the mineral feeding and operation rules coded) used to predict (through phenomenological models) the possible consequences of variations in mineral feeding. The work shows an intelligent approach to integrate and process operational data in mining sites, being a novel way to contribute to the decision-making process in complex environments. |
| first_indexed | 2024-03-10T10:03:00Z |
| format | Article |
| id | doaj.art-ca729eb4747a4cf68c0f8cd9bf4fef4c |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T10:03:00Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-ca729eb4747a4cf68c0f8cd9bf4fef4c2023-11-22T01:47:36ZengMDPI AGMetals2075-47012021-06-01117102510.3390/met11071025A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching ProcessManuel Saldaña0Purísima Neira1Víctor Flores2Pedro Robles3Carlos Moraga4Department of Computing and Systems Engineering, Universidad Católica del Norte, Antofagasta 1270709, ChileFaculty of Engineering and Architecture, Universidad Arturo Prat, Iquique 1110939, ChileDepartment of Computing and Systems Engineering, Universidad Católica del Norte, Antofagasta 1270709, ChileEscuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, ChileEscuela de Ingeniería Civil de Minas, Facultad de Ingeniería, Universidad de Talca, Curicó 3340000, ChileChilean mining is one of the main productive industries in the country. It plays a critical role in the development of Chile, so process planning is an essential task in achieving high performance. This task involves considering mineral resources and operating conditions to provide an optimal and realistic copper extraction and processing strategy. Performing planning modes of operation requires a significant effort in information generation, analysis, and design. Once the operating mode plans have been made, it is essential to select the most appropriate one. In this context, an intelligent system that supports the planning and decision-making of the operating mode has the potential to improve the copper industry’s performance. In this work, a knowledge-based decision support system for managing the operating mode of the copper heap leaching process is presented. The domain was modeled using an ontology. The interdependence between the variables was encapsulated using a set of operation rules defined by experts in the domain and the process dynamics was modeled utilizing an inference engine (adjusted with data of the mineral feeding and operation rules coded) used to predict (through phenomenological models) the possible consequences of variations in mineral feeding. The work shows an intelligent approach to integrate and process operational data in mining sites, being a novel way to contribute to the decision-making process in complex environments.https://www.mdpi.com/2075-4701/11/7/1025intelligent recommendation systemsheap leachingplanning modes of operation |
| spellingShingle | Manuel Saldaña Purísima Neira Víctor Flores Pedro Robles Carlos Moraga A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching Process Metals intelligent recommendation systems heap leaching planning modes of operation |
| title | A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching Process |
| title_full | A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching Process |
| title_fullStr | A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching Process |
| title_full_unstemmed | A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching Process |
| title_short | A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching Process |
| title_sort | decision support system for changes in operation modes of the copper heap leaching process |
| topic | intelligent recommendation systems heap leaching planning modes of operation |
| url | https://www.mdpi.com/2075-4701/11/7/1025 |
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