Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust
This research has presented a three level-two factors full factorial experimental design that investigated the process parameterization of a centrifugal concentrator for the separation of a waste copper smelter dust (CSD). This was followed by a theoretical contribution involving the development of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-01-01
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| Series: | Cogent Engineering |
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| Online Access: | http://dx.doi.org/10.1080/23311916.2018.1551175 |
| _version_ | 1797706624849149952 |
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| author | Daniel Okanigbe Popoola Olawale Abimbola Popoola Adeleke Abraham Ayomoh Michael Kolesnikov Andrei |
| author_facet | Daniel Okanigbe Popoola Olawale Abimbola Popoola Adeleke Abraham Ayomoh Michael Kolesnikov Andrei |
| author_sort | Daniel Okanigbe |
| collection | DOAJ |
| description | This research has presented a three level-two factors full factorial experimental design that investigated the process parameterization of a centrifugal concentrator for the separation of a waste copper smelter dust (CSD). This was followed by a theoretical contribution involving the development of a scheme of predictive models premised on the concept of constrained interpolant models. These were used for the experimental trend, pattern investigation and furthermore to provide expressions that depicts optimal experimental conditions in this research. Based on the experimental outputs, it was observed that a maximum grade of about 35.02 wt% Cu was achieved at a Rotational Bowl Speed of 120G, Water Flow Rate of 3.0l/min and constant experimental flow rate of 1.48l/min with a Liquid to Solid Ratio of 0.5. Similarly, a minimum output of 14.58% SiO2 and 10.29% Al2O3 was achieved at same experimental conditions. This clearly depicts a trend geared towards optimum experimental conditions aimed at maximizing Cu output and minimizing SiO2 and Al2O3 impurities. The predicted outputs premised on the use of Matlab software are in good conformance with the experimental outputs with a high degree of accuracy and confidence level over 97% as shown in Table 1 and corresponding Figures. |
| first_indexed | 2024-03-12T05:54:05Z |
| format | Article |
| id | doaj.art-ff31269ebc6844949ac4308559f03b58 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T05:54:05Z |
| publishDate | 2018-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-ff31269ebc6844949ac4308559f03b582023-09-03T04:50:37ZengTaylor & Francis GroupCogent Engineering2331-19162018-01-015110.1080/23311916.2018.15511751551175Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dustDaniel Okanigbe0Popoola Olawale1Abimbola Popoola2Adeleke Abraham3Ayomoh Michael4Kolesnikov Andrei5Metallurgical and Materials Engineering, Tshwane University of Technology (TUT)Tshwane University of Technology (TUT)Metallurgical and Materials Engineering, Tshwane University of Technology (TUT)Obafemi Awolowo University (OAU)University of Pretoria (UP)Metallurgical and Materials Engineering, Tshwane University of Technology (TUT)This research has presented a three level-two factors full factorial experimental design that investigated the process parameterization of a centrifugal concentrator for the separation of a waste copper smelter dust (CSD). This was followed by a theoretical contribution involving the development of a scheme of predictive models premised on the concept of constrained interpolant models. These were used for the experimental trend, pattern investigation and furthermore to provide expressions that depicts optimal experimental conditions in this research. Based on the experimental outputs, it was observed that a maximum grade of about 35.02 wt% Cu was achieved at a Rotational Bowl Speed of 120G, Water Flow Rate of 3.0l/min and constant experimental flow rate of 1.48l/min with a Liquid to Solid Ratio of 0.5. Similarly, a minimum output of 14.58% SiO2 and 10.29% Al2O3 was achieved at same experimental conditions. This clearly depicts a trend geared towards optimum experimental conditions aimed at maximizing Cu output and minimizing SiO2 and Al2O3 impurities. The predicted outputs premised on the use of Matlab software are in good conformance with the experimental outputs with a high degree of accuracy and confidence level over 97% as shown in Table 1 and corresponding Figures.http://dx.doi.org/10.1080/23311916.2018.1551175waste copper smelter dustmathematical modelingoptimizationdensity separationclassification |
| spellingShingle | Daniel Okanigbe Popoola Olawale Abimbola Popoola Adeleke Abraham Ayomoh Michael Kolesnikov Andrei Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust Cogent Engineering waste copper smelter dust mathematical modeling optimization density separation classification |
| title | Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust |
| title_full | Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust |
| title_fullStr | Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust |
| title_full_unstemmed | Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust |
| title_short | Centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust |
| title_sort | centrifugal separation experimentation and optimum predictive model development for copper recovery from waste copper smelter dust |
| topic | waste copper smelter dust mathematical modeling optimization density separation classification |
| url | http://dx.doi.org/10.1080/23311916.2018.1551175 |
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