Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms
This study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of proce...
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MDPI AG
2025-06-01
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| author | Olga Shiryayeva Batyrbek Suleimenov Yelena Kulakova |
| author_facet | Olga Shiryayeva Batyrbek Suleimenov Yelena Kulakova |
| author_sort | Olga Shiryayeva |
| collection | DOAJ |
| description | This study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of process parameters under variable feed conditions. The method addresses ore composition fluctuations by integrating three components: Physical modeling of particle motion, regression analysis, and neural network-based prediction. The jig bed level and pulsation frequency are used as control variables, while the Cr<sub>2</sub>O<sub>3</sub> content in the feed (<inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>r</mi></mrow></msub></mrow></semantics></math></inline-formula>) is treated as a disturbance. A neural network predicts the Cr<sub>2</sub>O<sub>3</sub> content in the concentrate (<inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>c</mi></mrow></msub></mrow></semantics></math></inline-formula>) and in the tailings (<inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>t</mi></mrow></msub></mrow></semantics></math></inline-formula>), representing chromite-rich and gangue fractions, respectively. The optimization is performed using a constrained Interior-Point algorithm. The model demonstrates high predictive accuracy, with a mean squared error (MSE) below 0.01. The proposed control algorithm reduces chromium losses in tailings from 7.5% to 5.5%, while improving concentrate quality by 3–6%. A real-time human–machine interface (HMI) was developed in SIMATIC WinCC for process visualization and control. The hybrid framework can be adapted to other mineral processing systems by adjusting the model structure and retraining the neural network on new ore datasets. |
| format | Article |
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| institution | DOAJ |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-fd5a1137ae1341fcb3e30df34cdbabdd2025-08-20T02:47:21ZengMDPI AGTechnologies2227-70802025-06-0113726910.3390/technologies13070269Sustainable Mineral Processing Technologies Using Hybrid Intelligent AlgorithmsOlga Shiryayeva0Batyrbek Suleimenov1Yelena Kulakova2Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, KazakhstanInstitute of Automation and Information Technologies, Satbayev University, Almaty 050013, KazakhstanInstitute of Automation and Information Technologies, Satbayev University, Almaty 050013, KazakhstanThis study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of process parameters under variable feed conditions. The method addresses ore composition fluctuations by integrating three components: Physical modeling of particle motion, regression analysis, and neural network-based prediction. The jig bed level and pulsation frequency are used as control variables, while the Cr<sub>2</sub>O<sub>3</sub> content in the feed (<inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>r</mi></mrow></msub></mrow></semantics></math></inline-formula>) is treated as a disturbance. A neural network predicts the Cr<sub>2</sub>O<sub>3</sub> content in the concentrate (<inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>c</mi></mrow></msub></mrow></semantics></math></inline-formula>) and in the tailings (<inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>t</mi></mrow></msub></mrow></semantics></math></inline-formula>), representing chromite-rich and gangue fractions, respectively. The optimization is performed using a constrained Interior-Point algorithm. The model demonstrates high predictive accuracy, with a mean squared error (MSE) below 0.01. The proposed control algorithm reduces chromium losses in tailings from 7.5% to 5.5%, while improving concentrate quality by 3–6%. A real-time human–machine interface (HMI) was developed in SIMATIC WinCC for process visualization and control. The hybrid framework can be adapted to other mineral processing systems by adjusting the model structure and retraining the neural network on new ore datasets.https://www.mdpi.com/2227-7080/13/7/269sustainable mineral processinghybrid intelligent controlchromite ore beneficiationneural networksparticle motion modelingreal-time optimization |
| spellingShingle | Olga Shiryayeva Batyrbek Suleimenov Yelena Kulakova Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms Technologies sustainable mineral processing hybrid intelligent control chromite ore beneficiation neural networks particle motion modeling real-time optimization |
| title | Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms |
| title_full | Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms |
| title_fullStr | Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms |
| title_full_unstemmed | Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms |
| title_short | Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms |
| title_sort | sustainable mineral processing technologies using hybrid intelligent algorithms |
| topic | sustainable mineral processing hybrid intelligent control chromite ore beneficiation neural networks particle motion modeling real-time optimization |
| url | https://www.mdpi.com/2227-7080/13/7/269 |
| work_keys_str_mv | AT olgashiryayeva sustainablemineralprocessingtechnologiesusinghybridintelligentalgorithms AT batyrbeksuleimenov sustainablemineralprocessingtechnologiesusinghybridintelligentalgorithms AT yelenakulakova sustainablemineralprocessingtechnologiesusinghybridintelligentalgorithms |