Preview

Proceedings of the Voronezh State University of Engineering Technologies

Advanced search

Multicriteria model of the process of crushing rock

https://doi.org/10.20914/2310-1202-2018-4-111-115

Abstract

The article deals with the modernization and adjustment of the fine chalk grinding process. The crushing process is an energy-consuming procedure, annually spent about 5% of all energy produced on Earth, including the energy of internal combustion engines. This indicates its great importance. In addition to the cost of electricity, large expenses go to repair the equipment. The greatest replacements are made on the main working parts of machines. In the course of substitutions a lot of time is spent, in order not to spend this rather important resource, it is necessary to approach this procedure from a scientific point of view. The organization and conduct of research on the replacement of the main working parts of crushers and mills will increase the productivity of the main equipment, improve the quality of the finished product and reduce production costs in terms of energy saving. Modernization and adjustment of technological equipment in order to improve the production process of fine chalk significantly increase the service life of the main equipment. For this purpose, it is proposed to conduct an active experiment. Before carrying out the experiment, it is necessary to set the model. The classical regression analysis is based on the assumption that the model type is a priori specified with accuracy to the parameters, and that an experiment has already been implemented that supplies the initial data for the regression construction. Hence, the problem is to choose the best method of data processing. In this paper, we propose a fundamentally new approach-automatic evaluation of the model options on a set of indicators, the calculation of which is based on a set of pareto-optimal variants of the model.The proposed method made it possible to identify two best alternatives out of 16384. Obviously, this approach can be easily modified for any other set of regression model quality criteria.

About the Authors

Yu. V. Bugaev
Voronezh state university of engineering technologies
Russian Federation
Dr. Sci. (Phys.-Math.), professor, higher mathematics and information technology department, Revolution Av., 19 Voronezh, 394036, Russia


L. A. Korobova
Voronezh state university of engineering technologies
Cand. Sci. (Engin.), associate professor, higher mathematics and information technology department, Revolution Av., 19 Voronezh, 394036, Russia


I. S. Tolstova
Voronezh state university of engineering technologies
senior lecturer, higher mathematics and information technology department, Revolution Av., 19 Voronezh, 394036, Russia


Yu. A. Demina
Voronezh state university of engineering technologies
student, higher mathematics and information technology department, Revolution Av., 19 Voronezh, 394036, Russia


References

1. Draper N., Smith G. Prikladnoj regressionnyj analiz. Kniga 2 [Applied Regression Analysis. Book 2]. Moscow, Finance and Statistics, 1987. 351 p. (in Russian)

2. Furnival G.M., Wilson R.W. Regressijn dy leaps and bounds. Technometrics. 1974. no. 16. pp. 499–511.

3. Allen D.M. The prediction sum of squares as a criterion for selecting predictor variables. University of Kentucky, Department of Statistics, Technical Report. 1971. no. 23.

4. Hartman K., Letsky E., Scheffer V. et al. Planirovanie ehksperimentov v issledovanii tekhnologicheskih processov [Planning of experiments in the study of technological processes]. Moscow, Mir, 1977. 552 p. (in Russian)

5. Korobova L.A., Tolstova I.S., Lihushin A.P., Demina Yu.A. Algoritm vybora drobil'nogo oborudovaniya dlya izmel'cheniya mela [Modeling of energy-information processes: a collection of materials of the IV and V International Scientific and Practical Internet Conferences]. 2017. pp. 263–267. (in Russian)

6. Korobova L.A., Tolstova I.S., Demina Yu.A. Adjustment of technological equipment. Alleya nauki [Alley of science]. 2018. vol. 3. no. 8 (24). pp. 728–732. (in Russian)

7. Kuriakose S., Shunmugam M.S. Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. Journal of materials processing technology. 2005. vol. 170. no. 1–2. pp. 133–141.

8. Queipo N.V., Haftka R.T., Shyy W., Goel T. et al. Surrogate-based analysis and optimization. Progress in aerospace sciences. 2005. vol. 41. no. 1. pp. 1–28.

9. Amanifard N., Nariman-Zadeh N., Borji M., Khalkhali A. et al. Modelling and Pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms. Energy Conversion and Management. 2008. vol. 49. no. 2. pp. 311-325. doi: 10.1016/j.enconman.2007.06.002

10. Zhang Y.P., Zhang Y.J., Gong W.J., Gopalan A.I. et al. Rapid separation of Sudan dyes by reverse-phase high performance liquid chromatography through statistically designed experiments. Journal of Chromatography A. 2005. vol. 1098. no. 1–2. pp. 183–187. doi: 10.1016/j.chroma.2005.10.024

11. Tarapata Z. Selected multicriteria shortest path problems: An analysis of complexity, models and adaptation of standard algorithms. International Journal of Applied Mathematics and Computer Science. 2007. vol. 17. no. 2. pp. 269–287.


Review

For citations:


Bugaev Yu.V., Korobova L.A., Tolstova I.S., Demina Yu.A. Multicriteria model of the process of crushing rock. Proceedings of the Voronezh State University of Engineering Technologies. 2018;80(4):111-115. (In Russ.) https://doi.org/10.20914/2310-1202-2018-4-111-115

Views: 544


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2226-910X (Print)
ISSN 2310-1202 (Online)