Leonid Kulakovskyi
NTUU, Kyiv Polytechnic Institute, Kyiv, Ukraine
Victor Rosen
NTUU, Kyiv Polytechnic Institute, Kyiv, Ukraine
Roshan Sharma
Telemark University College, Porsgrunn, Norway
Carlos F. Pfeiffer
Telemark University College, Porsgrunn, Norway
Bernt Lie
Telemark University College, Porsgrunn, Norway
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http://dx.doi.org/10.3384/ecp1511931Published in: Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden
Linköping Electronic Conference Proceedings 119:3, p. 31-41
Ukraine is an energy-dependent country and aims to reduce the import of natural gas, heat, and power in general. This implies extracting fuel from her own natural resources; one relevant and readily available energy carrier for such extraction is peat (bio mass). Currently, the operating regimes of the drying of peat are not energy efficient; these operating regime maps were developed in the 1970s and aimed only to get dry peat of required quality, without taking into account the cost of heat and electric energy in the drying process. The current quality of dried peat in the dryer does not always satisfy the necessary quality; e.g. parts of the peat may be insufficiently dried, or it may be over-dried. This affects the energy performance of peat briquette production.
To improve the quality and energy efficiency of the peat drying process, an analysis of the drying process is carried out: an empirical mathematical model of the drying process is developed using the GMDH principle [1, 2, 3], mapping input parameters and disturbances to output qualities based on available experimental data. Next, with known (measured) disturbances, optimal input parameters for the drying process are found. Changing the operational parameters too fast leads to insufficient drying or over-drying of parts of the peat. Thus, to avoid changing the operational conditions too fast, the operational conditions are classified into a number of classes corresponding to a certain range of values for the operational parameters. Finally, an iterative procedure for changing the input parameters from the past values to new values is introduced.
The resulting algorithm for finding the optimal operation of peat drying is based on mathematical models developed from experimental data, and aims to ensure improved quality and energy efficiency in the peat drying process.
Reference
[1] Ivakhnenko, A.G. (1971). “Polynomial Theory of Complex Systems”. IEEE Transactions on Systems, Man, and Cybernetics. Vol. SMC-1, No. 4, October 1971, pp. 364-378.
[2] Farlow, S.J. (1981). “The GMDH Algorithm of Ivakhnenko”. The American Statistician, Vol. 35, No. 4 (Nov. 1981), pp. 210-215.
[3] Anastasakis, L., and Mort, N. (2001). “The Development of Self-organization Techniques in Modelling: a Review of the Group Method of Data Handling (GMDH)”. Research Report No. 813, October 2001, Department of Automatic Control and Systems Engineering, The University of Sheffield, UK.
T. Aksyonova and I. Tetko. Robust polynomial neural networks in quantative-structure activity relationship studies. Systems Analysis Modelling Simulation, 43(10):1331–1339, 2003.
E. Altuhov and L. Kulakovskyi. Formuvannya faktornogo polya dlya eksperimental’nikh dosldzhen’ parovoi trubchatoi susharki torfu [formation of factor field for experimental research in steam tube dryer peat]. Vistnik NTUU "KP", seriya"Girnitstvo", (24):88–95, 2014.
B. Bohatov. Modelirovanie i optimizatsiya protsessov briketnogo proizvodstva [Simulation and optimization of briquette production processes]. Moskva, Nedra, 1976.
V. Borovikov. Nejronnye seti. Statistica Neural Networks [Neural networks. Statistica Neural Networks.]. Moskva, Gorjachaja linija-Telekom, 2008.
V. Cheberkus and A. Ivakhnenko. Multilayer algorithm for self organization of long term predictions (illustrated by the example of the lake baikal ecological system). Soviet Auto
matic Control, 13(4):22–38, 1980.
L. N. de Castro and F. J. Von Zuben. Artificial Immune Systems: Part I -Basic Theory and Applications. Universidade Estadual de Campinas, Dezembro de, Tech., 1999.
K. Forsman and O. Slätteke. On identification and control tuning of cylinder dryers. Proceedings Control Systems 2002, pages 298–302, 2002.
M. Gatih and V. Genshaft. Matematicheskie modeli protsessa sushki i upravleniya sushilkoj tsemag [mathematical models of the drying process and control of tsemag dryer]. Torfyanaya promyshlennost’, 16(2):18–27, 1980.
V. Gavrilov and V. Podinovskij. Optimizatsiya po posledovatel’no primenyaemym kriteriyam [Optimization of consistently applied criteria]. Moskva, Sov. radio, 1975.
V. Hnyeushev. Bryketuvannja torfu: Monografija, [Briquetting
peat Monograph]. NUWMNRU, Rivne, 2008.
A. Ivakhnenko. Polynomial theory of complex systems. Systems, Man and Cybernetics, IEEE Transactions, (5):364–378,
1971.
A. Ivakhnenko and A. Rogov. Inductive method permitting to choose model with least error and least bias allowing the solve interpolation tasks of artificial intelligence. Systems Analysis Modelling Simulation, 13(1):32–35, 2005.
A. Ivakhnenko and Yu. Yurachkovskij. Modelirovanie Slozhnykh Sistem po Eksperimental’nym Dannym [Modeling of Complex System from Expiremental Data].
G. Ivakhnenko. Heuristic self-organization in problems of engineering cybernetics. Automatica, 6(2):207–209, 1970.
D. Jouan-Rimbaud and R. De Maesschalck. The mahalanobis distance. Chemom. Intellig. Lab. Syst., 50:1–18, 2000.
N. Korol’ and I. Lishtvan. Osnovnye svojstva torfa i metody ikh opredeleniya [The basic properties of peat and methods for their determination]. Nauka i tehnika, Minsk, 2008.
L. Kulakovskyi and V. Rosen. Design the algorithm for constructing the field of factors of drying process of peat in the steam tube dryers. POWER ENGINEERING: Economics, Technique, Ecology, 3(34):64–70, 2013a.
L. Kulakovskyi and V. Rosen. Planuvannya virobnichikh eksperimentv dlya bagatofaktornogo dosldzhennya tekhnologi sushnnya torfu v parovikh trubchatikh susharkakh" [planning of multivariate production experiments to study the technology of peat drying in steam tube dryer]. Conference of Power and Control Systems, (3):34–40, 2013b.
R. Mueller and J. Cozad. Standardized discriminant coefficients: Which variance estimate is appropriate. /Journal of Educational and Behavioral Statistics, 13(4):313–318, 1988.
V. Naumovich. Iskusstvennaya sushka torfa [Artificial drying of peat]. Moskva, Nedra, 1984.
Ola Slätteke and Karl Åström. Modeling of a steam heated rotating cylinder a grey-box. American Control Conference 2005, pages 1449–1454, 2005.
A. Vojtishek. Osnovy metodov Monte-Karlo v algoritmakh i zadachakh. Chast’ 5 [Basics of Monte Carlo in algorithms and tasks. Part V]. Novosibirsk, izdvo NGU, 1999.
Leena Yliniemi. Advanced control of a rotary dryer. Department of Process Engineering, University of Oulu, 1999. ISBN 951-42-5280-2.
Yu. Yurachkovskij and I. Zaentsev. Modelirovanie Slozhnykh Sistem po Eksperimental’nym Dannym [Modeling of Complex System from Expiremental Data]. Moskva, Radio i Svyaz’, 1987.
