|تعداد مشاهده مقاله||9,873,475|
|تعداد دریافت فایل اصل مقاله||6,468,604|
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
|International Journal of Industrial Electronics Control and Optimization|
|مقاله 5، دوره 6، شماره 3، آذر 2023، صفحه 205-218 اصل مقاله (976.29 K)|
|نوع مقاله: Research Articles|
|شناسه دیجیتال (DOI): 10.22111/ieco.2023.45981.1488|
|Sadegh Kalantari1؛ Ali Madadi1؛ Mehdi Ramezani* 2؛ Abdolmotaleb Hajati3|
|1Department of Electrical Engineering, Tafresh University|
|2department of mathematics, Tafresh Univrsity|
|3Department of Mining Engineering,Arak University of Technology|
|Grinding in a ball mill is a process with high energy consumption; therefore, a slight improvement in its performance can lead to great economic benefit in the industry. The softness of the product of the grinding circuits prevents loss of energy in the subsequent processes. In addition, controlling the performance of a ball mill is a challenging issue due to its complex dynamic characteristics. The main 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 least squares method. As a result, by extracting useful data from the the acoustic signal, the optimal condition of the ball mill_ in terms of ground particle size and ball mill load (normal, low, high)_ can be achieved. In doing so, this goal, 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 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 the purposes of this plant. Also, the power spectrum is in the middle frequency band (frequency range of 300-700 Hz). According to the analysis and results, the proposed method will increase the efficiency of the studied ball mill.|
|Least squares؛ Quantum computation؛ Ball mill system identification؛ Acoustic signal؛ Ball mill control|
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