1. bookVolume 13 (2013): Issue 1 (February 2013)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

Determination of Optimal Technological Parameters of a Compaction Process: Case Study

Published Online: 09 Feb 2013
Volume & Issue: Volume 13 (2013) - Issue 1 (February 2013)
Page range: 12 - 19
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

Pelletizing as a complicated compaction process is under continuous improvement. One of the problems - determination of optimal technological parameters to attain a sufficiently high density of pellets - is solved in this paper. The statistical model of density depending on four technological factors is built based on data obtained through a central composite design. Canonical analysis is used to find the stationary point, and as the received point is a saddle point, the optimal setting is determined by means of ridge analysis. Special attention is paid to the uncertainty associated with the indirect measurement of the pellet density. Substantial differences in the density exist between pellets created under the same conditions, and especially the type-A uncertainty must be taken into consideration

Keywords

[1] DIN 51731:1996 Testing of solid fuels - compressed untreated wood, requirements and testing. Berlin, Germany: Deutsches Institut für NormungSearch in Google Scholar

[2] DIN PLUS: 2002 Certification Scheme. Wood pellets for use in small furnaces. Berlin, Germany. DIN CERTCO - Gesellschaft für Konformitätsbewertung mbHSearch in Google Scholar

[3] Mani, S., Tabil, L. G., Sokhansanj, S. (2006). Effects of compressive force, particle size and moisture content on mechanical properties of biomass pellets from grasses. Biomass and Bioenergy 30 (7), 648-65410.1016/j.biombioe.2005.01.004Search in Google Scholar

[4] Križan, P., Šooš, Ľ., Matúš, M. (2010). Optimalisation of briquetting machine pressing chamber geometry. Machine Design. s. 19-24Search in Google Scholar

[5] Svátek, M. (2010). Vplyv vybraných parametrov nakvalitu výliskov. (In Slovak). Unpublished doctoral dissertation, Slovak University of Technology, Bratislava, Slovak Republic.Search in Google Scholar

[6] Svátek, M., Križan, P., Šooš, Ľ., Kureková, E. (2009). Education of parameter impact on final briquettes quality. In Proceedings of the 7th International Conference on Measurement. Smolenice, Slovak Republic.Search in Google Scholar

[7] Križan, P., Šooš, Ľ., Matúš, M., Svátek, M., Vukelič, D. (2010). Evaluation of measured data from research of parameters impact on final briquettes density. Aplimat - Journal of Applied Mathematics 3 (3), 68-76Search in Google Scholar

[8] Kosarevsky, S.V., Latypov, V.N. (2012): Practical Procedure for Position Tolerance Uncertainty Determination via Monte-Carlo Error Propagation. Measurement Science Review 12 (1), 1-7.10.2478/v10048-012-0001-1Search in Google Scholar

[9] Zhang, F., Qu, X. (2012). Fusion Estimation of Point Sets from Multiple Stations of Spherical Coordinate Instruments Utilizing Uncertainty Estimation Based on Monte Carlo. Measurement Science Review 12 (2), 40-45.10.2478/v10048-012-0009-6Search in Google Scholar

[10] Likeš, J. (1968). Navrhování průmyslovýchexperimentů. (in Czech) Praha: SNTL.Search in Google Scholar

[11] Myers, R.H., Montgomery, D.C. (2002). ResponseSurface Methodology: Process and ProductOptimization Using Designed Experiments. New York: John Wiley & Sons.Search in Google Scholar

[12] Wu, C.F.J., Hamada, M. (2000): Experiments:Planning, Analysis, and Parameter DesignOptimization. New York: John Wiley & Sons.Search in Google Scholar

[13] Ahmadi, M., Vahabzadeh, F., Bonakdarpour, B., Mofarrah, E., Mehranian, M. (2005). Application of the central composite design and response surface methodology to the advanced treatment of olive oil processing wastewater using Fenton‘s peroxidation. Journal of Hazardous Materials 123 (1-3), 187-19510.1016/j.jhazmat.2005.03.042Search in Google Scholar

[14] Barwick, V.J., Ellison, S.L.R., Lucking, Ch.L., Burn, M.J. (2001). Experimental studies of uncertainties associated with chromatographic techniques. Journalof Chromatography A 918, 267-276.10.1016/S0021-9673(01)00679-3Search in Google Scholar

[15] Park, S.H., Kim, H.J., Cho, J.I. (2008). Optimal Central Composite Designs for Fitting Second Order Response Surface linear Regression Models. <www.springerlink.com/index/vx742p4850n7h274.pdf>10.1007/978-3-7908-2064-5_17Search in Google Scholar

[16] Rigas, F., Panteleos, P., Laoudis, C. (2000). Central Composite Design in a Refinery’s Wastewater Treatment by Air Flotation. Global Nest: the Int. J. 2 (3), 245-253.Search in Google Scholar

[17] Varesio, E., Gauvrit, J.Y., Longeray, R., Lantéri, P., Veuthey, J.L. (1997). Central composite design in the chiral analysis of amphetamines by capillary electrophoresis. Electrophoresis 18 (6), 931-937.10.1002/elps.11501806139221880Search in Google Scholar

[18] ISO GUM (1995). Guide to the expression ofuncertainty in measurement, BIPM, IEC, IFCC, ISO, IUPAC and OIML, ISBN 92-67-10188-9, Second EditionSearch in Google Scholar

[19] Palenčár, R (2006). Rozdelenie pravdepodobnosti chýb meradiel. (in Slovak) Metrológia a skúšobníctvo 11 (2), 9-12.Search in Google Scholar

[20] Welch, B.L. (1947): The Generalization of Student‘s Problem when Several Different Population Variances are Involved. Biometrika 34, 28.10.2307/2332510Search in Google Scholar

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