On the Method of Calculating the Correlation Between the Parameters that describe the Ultra Fine Griding Process Simulation and the Real Process Time

• Autorzy: Przybyszewski K. , Łukasiak K.
• Języki publikacji: EN

Abstrakty

EN

To simulate the ultra fine grinding process of solid matter composed of crystals, agglomerates or aggregates – a random process that bears the features of a physical chemistry collective process – we can use algorithms based on probability methods, as was proved in earlier publications [9, 10, 13]. These publications also discuss grinding simulations for uniform and non-uniform size distributions. In this paper, we describe the method of determining a correlation factor between the parameters that describe a simulation (the energy factor) and the real process time. This correlation is the most important piece of information required to define the kinetics of the process, as it allows to judge the real process by examining the simulation results.

Słowa kluczowe

EN

soft computing; simulation of physical chemistry processes; Monte Carlo method; size distribution of particles; grinding

• Wydawca: Społeczna Akademia Nauk w Łodzi
• Czasopismo: Journal of Applied Computer Science Methods
• Rocznik: 2009
• Tom: Vol. 1 No. 2
• Strony: 93--104
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Przybyszewski K.

• IT Institute, Academy of Management, Lodz, Poland

autor

Łukasiak K.

• Department of Knowledge Engineering and Computer Intelligence Academy of Humanities and Economics, Lodz, Poland

Bibliografia

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• 2. Austin L.G., 1971/72; A Review: Introduction to the mathematical description of grinding as a rate process, Powder Technology, Vol. 5, pp.1-17.
• 3. Austin L.G., Shoij K., Bhatia V.K., Jindal V., Savage K., Klimpel R.R., 1976; Some results on the description of size reductionas a rate process in various mills, Ind. Eng. Chem. Proc. Des. Develop., Vol. 15, No. 1, pp. 215-222.
• 4. Beke B., 1981; Fine grinding, Kovo, Budapest.
• 5. Bertoin J., Homogeneous fragmentation processes, 2001; Probab. Theory Relat. Fields, 121, pp.301-318.
• 6. Cadle R.D., 1965; Particle Size. Theory and Industrial Application, Reinhold, New York.
• 7. Hukki R.T., Reddy I.G., 1967; Dachema Monographien, 57, p. 313;
• 8. Kapteyn J. C., 1936; Skew Frequency Curves in Biology and Statistics, Grüntugen, 1906 [in:] Bennet J. G., Journal of the Institute of Fuel, London, October, pp. 22-54.
• 9. Łukasiak K., Przybyszewski K., 2006, Analysis of the relationship between probability of crystal destruction and its size for the ultrafine grinding processes, Automatyka, 3 (10), Wydawnictwo Akademickie AGH, Kraków, pp. 125-133 (in Polish)
• 10. Przybyszewski K., Łukasiak K., 2004; The ultra fine grinding of dyes as an example of modeling and simulation of the collective processes, [in:] Environmental Mechanics, Methods of Computer Science and Simulations, Part 2: Methods of Computer Science and Simulations, Społom Press Institute of Mathematical Modeling, Lviv, pp. 174-196
• 11. Przybyszewski K., 1988; The experimental investigations of the collective processes, Biul. Barwniki i Środki Pomocnicze, XXXII, 2, pp. 37-43 (in Polish)
• 12. Przybyszewski K., Łukasiak K., 2005, Normalization of the simulation parameters of the ultra fine grinding process [in], Rutkowska D. et al. (eds), Selected Problems of Computer Science, Akademicka Oficyna Wydawnicza EXIT, Warszawa, pp. 739-749 (in Polish)
• 13. Przybyszewski K., Łukasiak K., 2005, Simulation of the ultra fine grinding processes of the non one-size distribution materials, Automatyka, 3 (9), Wydawnictwo Akademickie AGH, Kraków, pp. 443-452 (in Polish)
• 14. Przybyszewski K., 1987, Model of the collective processes based on the study of the grinding and the mass crystallization, PhD Thesis, University of Lodz, Lodz (in Polish)