Development of New Spray Dust Suppression Materials in Metal Mines and Prediction of Algorithm Simulation Effect

Bin Peng

doi:10.4108/eetsis.6990

Authors

Bin Peng Hunan Institute of Technology

DOI:

https://doi.org/10.4108/eetsis.6990

Keywords:

Spray Dust Suppression, Computational Fluid Dynamics, Metal Mining, Algorithm Simulation

Abstract

PROBLEM: Dust contamination in metal mining poses substantial dangers to environmental quality and human health. Modern mining operations cannot use traditional spray dust suppression methods because they are poorly adapted to changing climate conditions, low efficient, and detrimental to the environment.

INTRODUCTION: Dust pollution seriously impacts the environment and human health in metal mine operations. Traditional spray dust suppression technology has many problems, such as limited effect, environmental impact, and poor climate adaptability.

OBJECTIVES: The purpose of this article is to develop a new type of spray dust suppression material and predict its dust suppression effect through algorithm simulation. Firstly, efficient and environmentally friendly dust-reducing materials were screened, and after evaluating the dust-reducing effect under laboratory conditions, the optimal material combination was determined.

METHODS: Using computational fluid dynamics (CFD), a numerical model of the spray process was constructed to simulate the dust suppression effect of different materials under different climatic conditions.

RESULTS: The results show that the highest dust reduction efficiency of the new spray dust reduction material is more than 4.3% higher than that of the traditional material, and it shows good stability.

CONCLUSION: The new spray dust control material and its effect prediction method studied in this article provide an effective solution for dust control in metal mines, which has important theoretical value and practical application prospects.

References

[1] Shan T, Li D, Yang Y. Preparation of magnetic porous materials and their application in electromagnetic dust removal systems. J Mater Metall. 2023;22(5):449-455.

[2] Bian S. Application of new filter materials in converter steelmaking dust removal system. Metall Mater. 2023;43(11):115-117.

[3] Fan X. Research and application of self-powered induction spray dust removal system in Jinbei Coal Industry. Energy Technol Manag. 2024;49(3):136-139.

[4] Yang H, Qi S, Tian L. Numerical simulation of influencing factors of spray dust removal efficiency based on Euler-Lagrange method. Min Saf Environ Prot. 2023;50(1):42-46.

[5] Li X, Knight RM, Hocter JS, et al. Effects of electrode materials and dimensions of an electrostatic spray scrubber on water droplet charging for dust removal. J Air Waste Manag Assoc. 2022;72(12):1442-1453.

[6] Vinuesa R, Brunton SL. Enhancing computational fluid dynamics with machine learning. Nat Comput Sci. 2022;2(6):358-366.

[7] Mani M, Dorgan AJ. A perspective on the state of aerospace computational fluid dynamics technology. Annu Rev Fluid Mech. 2023;55(1):431-457.

[8] Nandiyanto ABD, Ragadhita R, Aziz M. Involving particle technology in computational fluid dynamics research: A bibliometric analysis. CFD Lett. 2023;15(11):92-109.

[9] Sidik NAC, Al Husaeni DF, Nandiyanto ABD. Correlation between computational fluid dynamics (CFD) and nanotechnology. J Adv Res Micro Nano Eng. 2024;21(1):16-40.

[10] Szpicer A, Bińkowska W, Wojtasik-Kalinowska I, et al. Application of computational fluid dynamics simulations in food industry. Eur Food Res Technol. 2023;249(6):1411-1430.

[11] Harikumar N. Streamlining Geological Big Data Collection and Processing for Cloud Services. Journal of Current Science, 2021;9(04), ISSN NO: 9726-001X.

[12] Sreekar P. Cost-effective Cloud-Based Big Data Mining with K-means Clustering: An Analysis of Gaussian Data. International Journal of Engineering & Science Research, 202;10(1), 229-249.

[13] Raj K G. Cloud-based Early Acute Lymphoblastic Leukemia Detection Using Deep Learning based Improved YOLO V4. 2024 Second International Conference on Data Science and Information Systems (ICDSIS). 2024.

[14] Yang H, Qi N, Zhang X. Numerical simulation of the influencing factors of dust removal performance of a fine water mist spray dust removal device. J Saf Environ. 2023;23(9):3195-3203.

[15] Soodmand AM, Azimi B, Nejatbakhsh S, et al. A comprehensive review of computational fluid dynamics simulation studies in phase change materials: applications, materials, and geometries. J Therm Anal Calorim. 2023;148(20):10595-10644.

[16] Van Hoecke L, Boeye D, Gonzalez-Quiroga A, et al. Experimental methods in chemical engineering: computational fluid dynamics/finite volume method—CFD/FVM. Can J Chem Eng. 2023;101(2):545-561.

[17] Marcato A, Boccardo G, Marchisio D. From computational fluid dynamics to structure interpretation via neural networks: an application to flow and transport in porous media. Ind Eng Chem Res. 2022;61(24):8530-8541.

[18] Jaksch D, Givi P, Daley AJ, et al. Variational quantum algorithms for computational fluid dynamics. AIAA J. 2023;61(5):1885-1894.

[19] Bennati L, Vergara C, Giambruno V, et al. An image-based computational fluid dynamics study of mitral regurgitation in presence of prolapse. Cardiovasc Eng Technol. 2023;14(3):457-475.