Abstract
The current popular methods for decision making and project optimisation in mine ventilation contain a number of deficiencies as they are solely based on either subjective knowledge or objective information. This paper presents a new approach to rank the alternatives using G1-coefficient of variation method. The focus of this approach is to use combination weighing, which is able to compensate for the deficiencies in index single weighing method. In the case study, an evaluation index system was established to determine the evaluation value of each ventilation mode to select the best development face ventilation mode. The result shows that the proposed approach is able to rank the alternative development face ventilation mode reasonably, the combination weighing method had the advantages of using both subjective and objective weighing methods by taking into consideration of both the experience and practical knowledge, and any possible changes in a scenario. This approach provides a more reasonable and reliable procedure to analyze and evaluate different ventilation modes.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ren, T., Wang, Z.W., Graeme, C.: CFD modelling of ventilation and dust flow behaviour above an underground bin and the design of an innovative dust mitigation system. Tunn. Undergr. Space Technol. 41, 241–254 (2014)
Zhang, G., Li, L.X., Ji, H.G., Xiao, K.R., Yin, G.G., Song, L.: In situ investigation of gaseous pollution in the ramp of an underground gold mine. Indoor Built Environ. 23(2), 293–298 (2014)
Farshid, G.S., Abdulrahman, B., Farhad, F.: Application of local exhaust ventilation system and integrated collectors for control of air pollutants in mining company. Ind. Health 50, 450–457 (2012)
Cheng, J., Zhou, F., Yang, S.: A reliability allocation model and application in designing a mine ventilation system. Iran. J. Sci. Technol. Trans. Civil Eng. 38(C1), 61–73 (2014)
Jundika, C.K., Agus, P.S., Arun, S.M.: CFD simulation of methane dispersion and innovative methane management in underground mining faces. Appl. Math. Model. 38, 3467–3484 (2014)
Cheng, J.W., Yang, S.Q.: Data mining applications in evaluating mine ventilation system. Saf. Sci. 50, 918–922 (2012)
Maleki, B., Mozaffari, E.: A comparative study of the iterative numerical methods used in mine ventilation networks. Int. J. Adv. Comput. Sci. Appl. 7(6), 356–362 (2016)
Bascompta, M., Castanon, A.M., Sanmiquel, L., Oliva, J.: A GIS-based approach: influence of the ventilation layout to the environmental conditions in an underground mine. J. Environ. Manage. 182, 525–530 (2016)
Mirhedayatian, M., Jelodar, M.J., Adnani, S., Akbarnejad, M., Saen, R.F.: A new approach for prioritization in fuzzy AHP with an application for selecting the best tunnel ventilation system. Int. J. Adv. Manufact. Technol. 68, 2589–2599 (2013)
Sa, Z.Y., Wang, Y., Zhang, H.N., Song, H., Li, F.: Optimization of mine ventilation system based on grey system theory. In: 7th International Symposium on Safety Science and Technology, pp. 1683–1687. Hangzhou, China (2010)
Wu, L.Y., Yang, Y.Z.: Improved grey correlative method for risk assessment on mine ventilation system. In: 4th International Conference on Mechanical and Electrical Technology, pp. 2629–2633. Kuala Lumpur, Malaysia (2012)
Wang, H.: Study on reliability theory and method for mine ventilation system based on artificial neural network. Dissertation, Liaoning Technical University, Fuxing (2004) (in Chinese)
Karacan, C.Ö.: Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams. Int. J. Coal Geol. 72, 221–239 (2007)
Kozyrev, S.A., Osintseva, A.V.: Optimizing arrangement of air distribution controllers in mine ventilation system. J. Min. Sci. 48(5), 896–903 (2012)
Deng, L.J., Liu, J.: New approach for ventilation network graph drawing based on Sugiyama method and GA-SA algorithm. Comput. Model. New Technol. 18(8), 45–49 (2014)
Shriwas, M., Calizaya, F.: Application of genetic algorithms for solving multiple fan ventilation networks. In: Application of Computers and Operations Research in the Mineral Industry—Proceedings of the 37th International Symposium, pp. 488–498. Alaska, America (2015)
Zhang, M.: The research of speed control system based on intelligent PID controller to mine local ventilator. In: 2011 Second International Conference on Mechanic Automation and Control Engineering, pp. 858–861. Hohhot, China (2011)
Lowndes, I.S., Fogarty, T., Yang, Z.Y.: The application of genetic algorithms to optimise the performance of a mine ventilation network: the influence of coding method and population size. Soft. Comput. 9, 493–506 (2005)
Guo, Y.J.: Comprehensive Evaluation Theory and Method. Science Press, Beijing (2002). (in Chinese)
Zhuang, P., Li, Y.X.: Appraisement model and empirical study of enterprise investment risk based on G1-coefficient of variation.Soft. Science 25(10), 107–112 (2011). (in Chinese)
Gong, J., Hu, N.L., Cui, X., Wang, X.D.: Optimization of drifting ventilation method for high-altitude mine. Sci. Technol. Rev. 33(4), 56–60 (2015). (in Chinese)
Acknowledgements
The study was supported by the National Natural Science Foundation of China (51504286, 51374242), the Science and Technology Plan of Hunan province (2015RS4004) and China Postdoctoral Science Foundation (2015M572270).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Science Press and Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhiyong, Z., Kizil, M., Zhongwei, C., Jianhong, C. (2019). Selecting the Best Development Face Ventilation Scheme Using G1-Coefficient of Variation Method. In: Chang, X. (eds) Proceedings of the 11th International Mine Ventilation Congress. Springer, Singapore. https://doi.org/10.1007/978-981-13-1420-9_9
Download citation
DOI: https://doi.org/10.1007/978-981-13-1420-9_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1419-3
Online ISBN: 978-981-13-1420-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)