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Russia on the international steam coal market and exports to Poland

Katarzyna Stala-Szlugaj Zbigniew Grudziński
Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi Polskiej Akademii Nauk | 2019 | Nr 108 | 21-38 | DOI: 10.24425/znigsme.2019.130172
Keywords Russia international steam coal market export prices
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Abstract

The article presents an analysis of Russia’s participation in international steam coal trade, which has been its important participant for years. The research covered the years 2014–2018. The geographical location on two continents and the availability of coal deposits, favors its presence on both the Pacific and Atlantic markets. The article also discusses the main coal producers in Russia and the prices of Russian steam coal directed to the spot market. Due to the significant share of coal exports for the Russian economy, the focus was also on analyzing Russian seaports.

In recent years, Asian exports have dominated in Russian steam coal exports. The share of export to this market in the years 2014–2018 was in the range of 49–57% (60–87 million tons). Currently, three countries play an important role among Asian countries: South Korea, China and J apan. They purchased a total of 38–52 million tons of Russian coal. Although in the years under analysis Russia exported 52–67 million tons of steam coal to the European market, the share of this market dropped from almost half to around 40%. T he slow departure from coal energy contributes to reducing the share of recipients from this direction. Among European countries, in 2014 the main direction of export was Great Britain with 19% (24 million tons) of total export share. In 2018, exports fell to 9 million tons (5%).

Among European destinations for Russian coal, Poland’s share is growing in importance. In the years 2014–2018, steam coal exports to Poland varied in the range of 5.6–16.2 million tons. In the years 2014–2018 it changed in the range of 5.6–16.2 million tons. The dynamic growth achieved in the last three years is noteworthy. In relation to 2016, imports increased by 10.0 million tons and in 2018 amounted to as much as 16.1 million tons. The article also discusses the geographical structure of coal imports to Poland by railway border crossings and seaports.

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Authors and Affiliations

Katarzyna Stala-Szlugaj
Zbigniew Grudziński

Combined modelling for iron ore demand forecasting with intelligent optimization algorithms

Min Ren Jianyong Dai Wancheng Zhu Feng Dai
Gospodarka Surowcami Mineralnymi - Mineral Resources Management | 2021 | vol. 37 | No 1 | 21-38 | DOI: 10.24425/gsm.2021.136293
Keywords iron ore demand combined model intelligent optimization algorithm forecasting accuracy
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Abstract

The stable supply of iron ore resources is not only related to energy security, but also to a country’s sustainable development. The accurate forecast of iron ore demand is of great significance to the industrialization development of a country and even the world. Researchers have not yet reached a consensus about the methods of forecasting iron ore demand. Combining different algorithms and making full use of the advantages of each algorithm is an effective way to develop a prediction model with high accuracy, reliability and generalization performance. The traditional statistical and econometric techniques of the Holt–Winters (HW) non-seasonal exponential smoothing model and autoregressive integrated moving average (ARIMA) model can capture linear processes in data time series. The machine learning methods of support vector machine (SVM) and extreme learning machine (ELM) have the ability to obtain nonlinear features from data of iron ore demand. The advantages of the HW, ARIMA, SVM, and ELM methods are combined in various degrees by intelligent optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and simulated annealing (SA) algorithm. Then the combined forecast models are constructed. The contrastive results clearly show that how a high forecasting accuracy and an excellent robustness could be achieved by the particle swarm optimization algorithm combined model, it is more suitable for predicting data pertaining to the iron ore demand.
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Bibliography

1. Al-Fattah, S.M. 2020. A new artificial intelligence GANNATS model predicts gasoline demand of Saudi Arabia. Journal of Petroleum Science and Engineering 194.
2. Al-Hnaity, B. and Abbod, M. 2016. Predicting Financial Time Series Data Using Hybrid Model. Intelligent Systems and Applications 650, pp. 19–41.
3. Bates, J.M. and Granger, C.W.J. 1969. The combination of forecasts. Journal of the Operational Research Society 20(4), pp. 451–468.
4. Bikcora et al. 2018 – Bikcora, C., Verheijen, L. and Weiland, S. 2018. Density forecasting of daily electricity demand with ARMA-GARCH, CAViaR, and CARE econometric models. Sustainable Energy Grids and Networks 13, pp. 148–156.
5. Box, G.E.P. and Jenkins, G.M. 1976. Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
6. Davies, N.J.P. and Petruccelli, J.D. 1988. An Automatic Procedure for Identification, Estimation and Forecasting Univariate Self Exiting Threshold Autoregressive Models. Journal of the Royal Statistical Society 37(2), pp. 199–204.
7. D’Amico et al. 2020 – D’Amico, A., Ciulla, G., Tupenaite, L. and Kaklauskas, A. 2020. Multiple criteria assessment of methods for forecasting building thermal energy demand. Energy and Buildings 224, 110220.
8. Eberhart, R. and Kennedy, J. 1995. A new optimizer using particle swarm theory. [In:] MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43.
9. Holland, J.M. 1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor.
10. Huang et al. 2006 – Huang, G.B., Zhu, Q.Y. and Siew, C.K. 2006. Extreme learning machine: theory and applications. Neurocomputing 70, pp. 489–501.
11. Jia, L.W. and Xu, D.Y. 2014. Analysis and Prediction of the Demand for Iron Ore: Using Panel, Grey, Co-Integration and ARIMA Models. Resources Science 36(7), pp. 1382–1391.
12. Kazemzadeh et al. 2020 – Kazemzadeh, M.R., Amjadian, A. and Amraee, T. 2020. A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting. Energy 204, 117948
13. Liu et al. 2016 – Liu, X.L., Moreno, B. and Garcia, A.S. 2016. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors. Energy 115, pp. 1042–1054.
14. Ma et al. 2013 – Ma, W.M., Zhu, X.X. and Wang, M.M. 2013. Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm. Resources Policy 38, pp. 613–620.
15. Mi et al. 2018 – Mi, J., Fan, L., Duan, X. and Qiu, Y. 2018. Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model. Mathematical Problems in Engineering 2018, pp. 1–11.
16. National Bureau of Statistics of China. Output of Industrial Products. [Online] https://data.stats.gov.cn/easyquery. htm?cn=C01&zb=A0E0H&sj=2019 [Accessed: 2020-12-30].
17. National Bureau of Statistics of China, 2018. Chinese Mining Yearbook. Beijing: China Statistics Press.
18. Song et al. 2018 – Song, J.J., Wang, J.Z. and Lu, H.Y.2018. A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting. Applied Energy 215, pp. 643–658.
19. Vapnik, V.N. 1995. The Nature of Statistical Learning Theory. New York: Springer.
20. Wang et al. 2018 – Wang, J., Luo, Y.Y., Tang, T.Y. and Peng, G. 2018. Modeling a combined forecast algorithm based on sequence patterns and near characteristics: An application for tourism demand forecasting. Chaos, Solitons and Fractals 108, pp. 136–147.
21. Wang et al. 2012 – Wang, J.J., Wang, J.Z., Zhang, Z.G. and Guo, S.P. 2012. Stock index forecasting based on a hybrid model. Omega-International Journal of Management Science 40, pp. 758–766.
22. Wang et al. 2010 – Wang, J.Z., Zhu, S.L., Zhang, W.Y. and Lu, H.Y. 2010. Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 35, pp. 1671–1678.
23. Wang et al. 2020 – Wang, Z.X., Zhao, Y.F. and He, L.Y. 2020. Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average. Applied Soft Computing 94.
24. Winters, P.R. 1960. Forecasting sales by exponentially weighted moving averages. Management Science 6(3), pp. 324–42.
25. Zhang et al. 2019 – Zhang, S.H., Wang, J.Y. and Guo, Z.H. 2019. Research on combined model based on multi- -objective optimization and application in time series forecast. Soft Computing 23, pp. 11493–11521.
26. Zhang et al. 2017 – Zhang, Y., Li, C. and Li, L. 2017. Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy 190, pp. 291–305.
27. Zhou et al. 2019 – Zhou, Z., Si, G.Q., Zheng, K., Xu, X., Qu, K. and Zhang, Y.B. 2019. CMBCF: A Cloud Model Based Hybrid Method for Combining Forecast. Applied Soft Computing 85, 105766.
28. Zhou, Z.H. 2016. Machine Learning. Beijing: Tsinghua University Press, 425 pp. ( in Chinese).

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Authors and Affiliations

Min Ren
1
Jianyong Dai
2
Wancheng Zhu
3
Feng Dai
3
ORCID: ORCID
  1. Northeastern University, Shenyang, China
  2. University of South China, Hengyang, China
  3. Northeastern University, Shenyang

Mathematical modelling of post-mining dislocations kinetics as a quasi-random process

Wiesław Piwowarski
Geodesy and Cartography | 2000 | vol. 49 | No 1 | 21-38
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Abstract

The paper presents the problem of determination of one- and multi-stage prognosis of post-mining surface dislocations. The finite and chronologically ordered vector of geodetic surveys is the describing variable herein. Completed surveys show that the analyzed process can be written as a composition of both deterministic process and singular one. Hence the quantitative description of the kinetics of the process of dislocation forming has been assigned to the class of the stochastic model. An adequate series sum in which time is the argument and random variables are the values makes up the formal definition of the model. The optimization of one-stage prognosis has been carried out for utility purposes. The Durbin-Levinson algorithm is the applied numerical procedure. The utility fragment of this is based on verification of the defined model for certain mining-geological conditions and surveying results. The obtained analytical representation and optimal prognosis of the kinetics of vertical dislocations correspondend to surveying results, which can be testified by adequate measures of the quality of description of the process.
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Authors and Affiliations

Wiesław Piwowarski

Rotating Dust Separator of High Efficiency

Tadeusz Knap
Archives of Environmental Protection | 2000 | vol. 26 | No 1 | 21-38
Keywords gas - cleaning - industrial chemistry flow - aerodynamics - physics dust extraction- mechanical working - mining
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Abstract

Paper brings a description of free motion of particles suspended in the atmospheric air and being under action of the gravitational field together with additional accelerations caused by their relative motion. Theoretical part of the paper presents physical background of the particles motion within rotating channels together with the simplified method allowing to determine efficiency of the dust separator. Experimental part of the paper describes the test stand layout together with details of the dust separator design and its principles of action. In the closing part final conclusions and suggested practical applications of the devices under investigation are presented.
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Authors and Affiliations

Tadeusz Knap

Numerical modelling of ozonation process with respect to bromate formation. Part I – Model development

Urszula Olsińska
Chemical and Process Engineering | 2019 | vol. 40 | No 1 | 21–38 | DOI: 10.24425/cpe.2018.124994
Keywords model ozonation bromate hydrodynamics kinetics
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Abstract

The paper focuses on the modelling of bromate formation. An axial dispersion model was proposed to integrate the non-ideal mixing, mass-transfer and a kinetic model that links ozone decomposition reactions fromthe Tomiyasu, Fukutomi and Gordon (TFG) ozone decaymodelwith direct and indirect bromide oxidation reactions, oxidation of natural organicmatter and its reactionswith aqueous bromine. To elucidate the role of ammonia an additional set of reactions leading to bromamine formation, oxidation and disproportionation was incorporated in the kinetic model. Sensitivity analysis was conducted to obtain information on reliability of the reaction rate constants used and to simplify the model.

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Authors and Affiliations

Urszula Olsińska

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