Controlling the Ground Particle Size and Ball Mill Load Based on Acoustic Signal, Quantum Computation Basis, and Least Squares Regression, Case Study: Lakan Lead-Zinc Processing Plant.

Grinding in a ball mill is a process with high energy consumption; therefore, a slight improvement in its performance can lead to significant economic benefits in the industry. The softness of the product of the grinding circuits prevents loss of energy in the subsequent processes. In addition, controlling the mill's performance is challenging due to its complex dynamic characteristics. The primary purpose of this article is to use the ground particle size diagram and acoustic signal in ball mill control, and model their relationship based on the least squares method. As a result, by extracting valuable data from the acoustic signal, the optimal condition of the ball mill_ in terms of ground particle size and ball mill load (standard, low, high)_ can be achieved. In doing so, in this article, innovative ideas such as adaptive quantum basis, sparse representation, SVD, and PCA-based methods were used. The proposed method has been practically implemented on the ball mill of the Lakan lead-zinc processing plant. Also, a prototype of the device was built. The test results show that the optimal load for the studied ball mill is 10t/h. In this case, the ground particle size is 110-120 microns, which is ideal for this plant. Also, the power spectrum is in the middle-frequency band (300-700 Hz). According to the analysis and results, the proposed method will increase the efficiency of the studied ball mill. [ABSTRACT FROM AUTHOR]

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