search query: @instructor Saari, Juha / total: 6
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| Author: | Salazar MartÃnez, Arturo |
| Title: | Rougher flotation optimization using response surface methodology |
| Publication type: | Master's thesis |
| Publication year: | 2015 |
| Pages: | vi + 87 s. + liitt. 1 Language: eng |
| Department/School: | Materiaalitekniikan laitos |
| Main subject: | Process Systems Engineering (KE3004) |
| Supervisor: | Alopaeus, Ville |
| Instructor: | Saari, Juha ; Koistinen, Ari |
| Electronic version URL: | http://urn.fi/URN:NBN:fi:aalto-201503062018 |
| Location: | P1 Ark Aalto 2706 | Archive |
| Keywords: | copper flotation statistical modelling response Surface Methodology optimization |
| Abstract (eng): | The utilization of Response Surface Methodology as a way to model multiple responses of a process while changing multiple variables in one study, and its application for optimization of the process, has been successfully applied in the chemical and mining industries. A small amount of required tests combined with the capability to make predictions from the obtained models make the methodology attractive for the use in several stages of a concentrator plant: from design and sizing of equipment to continuous improvement on mature operations. This thesis was developed with the aim to evaluate the applicability of Response Surface Methodology as a tool to perform metallurgical process modelling and optimization on site operations. The proposed methodology was selected due to its flexibility in the selection of multiple variables and responses at once, which allowed gathering sufficient data from a total of 20 batch flotation experiments for copper rougher flotation where pH, collector and depressant dosages were varied to provoke changes in the responses of copper recovery and grade, iron and nickel recoveries. The obtained results were utilized for the generation of four regression models from which contour line and surface plots were generated for an optimization and determination of feasible region by superimposition of contours. The optimal set-points were determined at values for pH of 10.5, collector dosage of 26 g/t and depressant dosage of 65 g/t. The accuracy of the prediction for the optimal point was assessed with a series of three confirmation tests with such values kept constant. The result for copper recovery was 82.5%, deviating -2.4% from the predicted point. For copper grade the result was 9.16%, deviating +0.56% from prediction. For iron recovery the result was 6.4%, deviating 0.1% from prediction. For nickel recovery the result was 38.8%, with a deviation of +8.4 from the prediction. In comparison to base-case settings, the optimal point represents comparable relative costs of reagents with expected improvement in results with a particular focus on increased copper recovery by 2% on average. It was concluded that the methodology is worth exploring further in the different stages of process design, commissioning and working processes. It was demonstrated that with as few as 20 tests it was possible to obtain relevant models and their surface plots, which helped to make a decision for the selection of a new optimal, according to process requirements. It is advised that a future expansion in the methodology for future studies would be for the inclusion of variable screening. |
| ED: | 2015-03-08 |
INSSI record number: 50702
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