Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms
Shiryayeva O. Suleimenov B. Kulakova Y.
July 2025Multidisciplinary Digital Publishing Institute (MDPI)
Technologies
2025#13Issue 7
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 Cr2O3 content in the feed ((Formula presented.)) is treated as a disturbance. A neural network predicts the Cr2O3 content in the concentrate ((Formula presented.)) and in the tailings ((Formula presented.)), 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.
chromite ore beneficiation , hybrid intelligent control , neural networks , particle motion modeling , real-time optimization , sustainable mineral processing
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Institute of Automation and Information Technologies, Satbayev University, Almaty, 050013, Kazakhstan
Institute of Automation and Information Technologies
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