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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 | ||
| نویسندگان | ||
| 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 | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Least squares؛ Quantum computation؛ Ball mill system identification؛ Acoustic signal؛ Ball mill control | ||
| مراجع | ||
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[1] S. Singh, S. K. Singh, P. K. Harijan, S. K. Yadav, and A. Kumar, “Investigation on the effect of Fe impurity pickups during ball milling and Ni dispersion on the microwave absorption performance of ball milled Fe impurities-Ni/SiC composites,” Journal of Materials Science: Materials in Electronics, Jul. 2022.
[2] A. Hajati, S. Shafaei, & M. Noaparast, “A novel approach to optimize grinding circuits-modelling strategy to monitor ball mill particle size distribution data at Lakan plant, ” IJST, Transactions of Mechanical Engineering, vol. 35, pp.85-100, 2011. [3] A. M. Tsirlin and A. I. Balunov, “Optimal Control of Technological Processes,” Processes, vol. 11, no. 6, pp. 1835-1835, Jun. 2023. [4] Y. Li, “Modelling tumbling ball milling based on DEM simulation and machine learning,” (Doctoral dissertation, UNSW Sydney), 2023. [5] K. S. Cho, S. Kim, and Young Hun Lee, “Correlation between Acoustic Intensity and Ground Particle Size in Alumina Ball Mill Process,” Journal of The Korean Ceramic Society, vol. 55, no. 3, pp. 275–284, May 2018. [6] J. Lv, Z. Wang, and S. Ma, “Calculation method and its application for energy consumption of ball mills in ceramic industry based on power feature deployment,” Advances in Applied Ceramics, vol. 119, no. 4, pp. 183–194, Feb. 2020. [7] Halit Sübütay and İlyas Şavklıyıldız, “Effect of HighEnergy Ball Milling in Ternary Material System of (Mg-SnNa),” Crystals, vol. 13, no. 8, pp. 1230–1230, Aug. 2023. [8] Shashi Prakash Dwivedi et al., “Effect of ball-milling process parameters on mechanical properties of Al/Al2O3/collagen powder composite using statistical approach,” vol. 15, pp. 2918–2932, Nov. 2021. [9] J. Tang, J. Qiao, Z. Liu, X. Zhou, G. Yu, and J. Zhao, “Mechanism characteristic analysis and soft measuring method review for ball mill load based on mechanical vibration and acoustic signals in the grinding process,” vol. 128, pp. 294–311, Nov. 2018. [10] J. Shi, G. Si, and Y. Zhang,“Application of Fractional Fourier Transform for Prediction of Ball Mill Loads Using Acoustic Signals,” IEEE Access, Jan. 2019. [11] X. Wang, K. Sun, H. Zhang, X. Wan, and C. Yang, “Mill Load Identification Method for Ball milling Process Based on Grinding Signal,” IFAC-PapersOnLine, vol. 54, no. 21, pp. 7–12, Jan. 2021. [12] Y. Li, J. Bao, T. Chen, A. Yu, and R. Yang, “Prediction of ball milling performance by a convolutional neural network model and transfer learning,” Powder Technology, vol. 403, p. 117409, May 2022. [13] S. Kalantari, M. Ramezani, A. Madadi, and V. V. Estrela, “Reduction AWGN from Digital Images Using a New Local Optimal Low-Rank Approximation Method,” Smart innovation, systems and technologies, Dec. 2020. [14] C. Tian, M. Zheng, W. Zuo, B. Zhang, Y. Zhang, and D. Zhang, “Multi-stage image denoising with the wavelet transform,” arXiv (Cornell University), Sep. 2022. [15] S. Kalantari, M. Ramezani, and A. Madadi, “Introducing a New Hybrid Adaptive Local Optimal Low Rank Approximation Method for Denoising Images,” International Journal of Industrial Electronics Control and Optimization, vol. 3, no. 2, pp. 173–185, May 2020. [16] S. Kalantari, A. Madadi, & M. Ramezani, “Reconstruction of Geological Images Based on an Adaptive Spatial Domain Filter: An Example to Introduce Quantum Computation to Geosciences,” International Journal of Mining and Geo-Engineering, vol. 57, no. 2, pp. 183-194, 2023. [17] Muhammad Sahimi and Pejman Tahmasebi, “The Potential of Quantum Computing for Geoscience,” Transport in Porous Media, vol. 145, no. 2, pp. 367–387, Sep. 2022. [18] S. Dutta, A. Basarab, Bertrand Georgeot, and D. Kouamé, “Quantum Mechanics-Based Signal and Image Representation: Application to Denoising,” IEEE open journal of signal processing, vol. 2, pp. 190–206, Jan. 2021. [19] P. W. Anderson, “Absence of Diffusion in Certain Random Lattices,” Physical Review, vol. 109, no. 5, pp. 1492–1505, Mar. 1958. [20] S. Kalantari and M. J. Abdollahifard, “Optimizationbased multiple-point geostatistics: A sparse way,” Computers & Geosciences, vol. 95, pp. 85–98, Oct. 2016. [21] H. Liu, J. Zhang, and R. Xiong, “CAS: Correlation Adaptive Sparse Modeling for Image Denoising,” IEEE transactions on computational imaging, vol. 7, pp. 638–647, Jan. 2021. [22] E. Rajaby and S. M. Sayedi, “A structured review of sparse fast Fourier transform algorithms,” Digital Signal Processing, vol. 123, p. 103403, Apr. 2022.
[23] B. Hasan, B. M. S., & A. Abdulazeez, “A review of principal component analysis algorithm for dimensionality reduction,” Journal of Soft Computing and Data Mining, vol. 2, pp. 20-30, 2021. [24] Z. Raisi, & J. Zelek, “Investigation of Deep Learning Optimization Algorithms in Scene Text Detection,” International Journal of Industrial Electronics Control and Optimization, 2023. [25] J. P. Norton, “An introduction to identification,” Courier Corporation, 2009. [26] C. J. Miller, S. N. Smith, and M. Pugatch, “Experimental and quasi-experimental designs in implementation research,” Psychiatry Research, vol. 283, no. 112452, Jun. 2019. [27] J. Hamill, G. E. Caldwell, and T. R. Derrick, “Reconstructing Digital Signals Using Shannon’s Sampling Theorem,” Journal of Applied Biomechanics, vol. 13, no. 2, pp. 226–238, May 1997. | ||
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آمار تعداد مشاهده مقاله: 95 تعداد دریافت فایل اصل مقاله: 57 |
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