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Number of results: 7
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Abstract

In this paper are presented results of study fusion characteristics of the biomass ashes from the hydrolyzed lignin and the ashes from the coke breeze. The hydrolyzed lignin ashes were compared with the coke breeze ashes i.e. with a fossil fuel. These ashes were prepared in muffle furnace at the temperature of 550°C (hydrolyzed lignin) and 850°C (coke breeze). Biomass (the hydrolyzed lignin) represents the new fuels for sintering process and an attractive way to decrease CO2 emissions from the energy production. The characterization methods were the following: standard fuel characterization analyses, chemical and mineralogical composition of the ashes and phase analyses of the ashes of biomass and the coke breeze. These ashes were prepared by the same method. Characterisation of the ashes samples was conducted by means of X-ray fluorescence (XRF), X-ray diffraction (XRD) and scanning electron microscopy (SEM). Quantitative analysis of the crystalline and amorphous phases in each of the ash samples were carried out using the Rietveld method. The dominant phase of the ash from the coke breeze was mullite (Al6Si2O13). SiO2 is the dominant phase of the ash from the hydrolyzed lignin.

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Authors and Affiliations

M. Fröhlichová
R. Findorák
J. Legemza
M. Džupková
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Abstract

Iron ore blending in an open-pit mine is an important means to ensure ore grade balance and resource recycling in iron mine industrial production. With the comprehensive recovery and utilisation of resource mining, the multi-source and multi-target ore blending method has become one of the focuses of the mining industry. Scientific and reasonable ore blending can effectively reduce the transportation cost of the enterprise. It can also ensure that the ore grade, washability index and iron carbonate content meet the requirements of the concentrator and significantly improve the comprehensive utilisation rate and economic benefits of the ore. An ore blending method for open-pit iron ore is proposed in this paper. The blending method is realised by establishing the ore blending model. This model aims to achieve maximum ore output and the shortest transportation distance, ore washability index, total iron grade, ferrous iron grade and iron carbonate content after the ore blending meets the requirements. This method can meet the situation of a single mine to a single concentrator and that of a single mine to multiple concentrators. According to the results of ore blending, we can know the bottleneck of current production. Through targeted optimisation management, we can tap the production potential of an open-pit mine.
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Authors and Affiliations

Jiang Yao
1
Chunhui Liu
2
Guichen Huang
2
Kai Xu
2
Qingbo Yuan
2

  1. Northeastern University, College of Resources and Civil Engineering, Shenyang, Liaoning 110819, China
  2. Ansteel Group GUANBAOSHAN Mining Co., Ltd, Anshan, Liaoning 114000, China
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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
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Abstract

Steel and cast-iron products, due to their low price and beneficial properties, are the most widely used among metals; their consumption has become an indicator of the economic development of countries. The characteristics of iron raw materials, in relation to current metallurgical requirements, are presented in the present this article. The globalization of the trade and development of steelmaking technologies have caused significant changes in the quality of raw materials in the last half-century forcing improvements in processing technologies. In many countries, standard concentrates (at least 60% Fe) are almost twice as rich as those processed in the mid-20th century. Methods of quality assessment have been improved and quality standards tightened.

The quality requirements for the most important raw materials ‒ iron ores and concentrates, steel scrap, major alloy metals, coking coal, and coke, as well as gas and other energy media ‒ are reviewed in the present paper. Particular attention is paid to the quality testing methodology. The quality of many raw materials is evaluated multi-parametrically: both chemical and physical characteristics are important. Lower-quality parameters in raw materials equate to significantly lower prices obtained by suppliers in the market.

The markets for these raw materials are diversified and governed by separate sets of newly introduced rules. Price benchmarks (e.g. for standard Australian metallurgical coal) or indices (for iron concentrates) apply. Some raw materials are quoted within the framework of the commodity market system (certain alloying components and steel scrap). The abandonment of the long-established system of multi-annual contracts has led to wide fluctuations in prices, which have reached a scale similar to that of other metals.

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Authors and Affiliations

Mariusz Krzak
Andrzej Paulo
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Abstract

Backbreak is an undesirable phenomenon in blasting operations, which can bedefined as the undesirable destruction of rock behind the last row of explosive holes. To prevent and reduce its adverse effects, it is necessary to accurately predict backbreak in the blasting process. For this purpose, the data obtained from 66 blasting operations in Gol-e-Gohar iron ore mine No. 1 considering blast pattern design Parameters and geologic were collected. The Pearson correlation results showed that the parameters of the hole height, burden, spacing, specific powder, number of holes, and the uniaxial compressive strength had a significant effect on the backbreak. In this study, a multilayer perceptron artificial neural network with the 6-12-1 architecture and six multiple linear and nonlinear statistical models were used to predict the backbreakin the blasting operations. The results of this study demonstrated that the prediction rate of backbreak using the artificial neural network model with R2 = 0.798 and the rates of MAD, MSE, RMSE and, MAPE were0.79, 0.93, 0.97 and, 11.63, respectively, showed fewer minor error compared to statistical models. Based on the sensitivity analysis results, the most important parameters affecting the backbreak, including the hole height, distance between the holes in the same row, the row spacing of the holes, had the most significant effect on the backbreak, and the uniaxial compressive strength showed the lowest impact on it.
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Authors and Affiliations

Abbas Khajouei Sirjani
1
ORCID: ORCID
Farhang Sereshki
1
ORCID: ORCID
Mohammad Ataei
1
ORCID: ORCID
Mohammad Amiri Hosseini
2
ORCID: ORCID

  1. Shahrood University of Technology, Iran
  2. Technology Management and Research of Gol-e-gohar, Iran
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Abstract

Based on the analysis of the LIDAR terrain Digital Elevation Model (DEM), traces of opencast and underground mining of iron ore mining were located and classified. They occur in the zone of ore-bearing deposits outcropping on the north-eastern and north-western bounds of the Holy Cross Mountains. The DEM of an area covered by thirty-six (36) standard sheets of the Detailed Geological Map of Poland on a scale of 1:50,000 was thoroughly explored with remote sensing standards. Four types of ore recovery shafts with accompanying waste heaps were classified. The acquired data on the extent of former mining areas, covered with varying shafts and barren rock heaps could make a basis for distinguishing, according to historical data and in cooperation with archaeologists, the historical development stages of today’s steel industry. According to general knowledge, the iron industry in Europe instigate dates from the Roman times, in the Ist century BC to the IVth century AD, throughout the earlier and the late medieval times, up to the most recent the 1970ties. The usefulness of the LIDAR method has already been amazingly confirmed in archaeological researches worldwide. Many discoveries of ling forgotten, even large entities resulting from human activities in Asia and Central America especially were discovered owed to the LIDAR DEM. Also, traces of human settlements from various historical periods were discovered that way in Poland. The applicability of DEM based on LIDAR data is, in geological studies of surficial geodynamic processes and in geological mapping in Poland, rather contested.

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Authors and Affiliations

Zygmunt Heliasz
Stanisław Ostaficzuk
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Abstract

Mining activities from exploration to final material handling up to shipment pass through various stages where environmental pollution results. Mining method can and should be selected in such a way that their impact on individuals and environmental to be minimized. Until now, different mining specialists have carried out many studies on mining method selection. Unfortunately neither of previous approaches takes into account of the environmental consideration and methodology for assessment of environmental impacts criterion. This paper discusses environmental impacts of mining operations associated with different mining methods. For this purpose, the Folchi approach was modified for environmental impact assessment which associates the mining methods inherently and developed of a procedure to assist a selecting of mining method. Firstly, the general and explanatory information about effects of mining on the environmental pollution are given in the paper. Moreover field and purposes of the study are introduced. The paper presents an environmental assessment for different mining methods. And, secondly, the impacts of each mining methods on environment are focused and discussed. Finally, some concluding remarks are made and the related applications for the mining method selection are discussed by using in a case study. As the main advantage, this new algorithm takes several environmental issues and their interaction takes into consideration for environmental assessment of a mining method selection.

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Authors and Affiliations

F. Samimi Namin
K. Shahriar
A. Bascetin

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