ORIGINAL_ARTICLE
Effect of maceral content on tendency of spontaneous coal combustion using the R70 method
Spontaneous coal combustion (SCC) is one of the significant hazardous phenomena in underground coal mines. The tendency of spontaneous coal combustion is an intrinsic property, due to the presence of the maceral content. Unlike its importance, this matter has not been discussed in detail among the researchers. Therefore, it is necessary to investigate the effect of this parameter on SCC. Maceral content is defined by the original vegetation from which coal is formed. The present study examines the role of maceral content on SCC, based on 51 coal samples with different maceral contents. These samples were collected from several Iranian underground coal fields, and the R70 test was carried out on each coal sample. By examining the results and comparing the R70 values, it was found that with an increase in the vitrinite and liptinite contents and a decrease in the inertinite content, the coal samples showed to have more tendency of spontaneous combustion.
https://ijmge.ut.ac.ir/article_75617_9ff5e4f5e8d29067a82e6c6679a04861.pdf
2020-12-01
93
99
10.22059/ijmge.2019.269571.594766
Macerals Content
R70 Test Method
Spontaneous Combustion of Coal
Amir
Saffari
amirsaffari5710@yahoo.com
1
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Farhang
Sereshki
farhang@shahroodut.ac.ir
2
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Mohammad
Ataei
ataei@shahroodut.ac.ir
3
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
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49
ORIGINAL_ARTICLE
Rock physical modeling enhancement in hydrocarbon reservoirs using Choquet fuzzy integral fusion approach
Rock physics models are widely used in hydrocarbon reservoir studies. These models make it possible to simulate a reservoir more accurately and reduce the economic risk of oil and gas exploration. In the current study, two models of Self-Consistent Approximation followed by Gassmann (SCA-G) and Xu-Payne (X-P) were implemented on three wells of a carbonate reservoir in the southwest of Iran. Then, in order to increase the accuracy and improve the efficiency of the models, a fusion model of Choquet Fuzzy Integral (CFI) was applied as a new approach. The compressionalwave velocities were estimated using two models, i.e., SCA-G and X-P, and were then integrated using the CFI fusion model. Finally, by comparing the model results and the real well log data, the Choquet model was confirmed as a compatible model with proper results. The correlation coefficient (CC) and Root Mean Squared Error (RMSE) for the estimated velocities versus the actual values showed the reliability of the constructed models. For example, in one of the studied wells, the CC and RMSE values were 99.2 and 44 m/s, respectively, in support of the fusion model. This could be related to the optimization algorithms in the heart of the Choquet model that led to the optimization of the model parameters and also better results in the studied carbonate reservoir.
https://ijmge.ut.ac.ir/article_75807_e0987d6fd8be97f87a100d439554ae72.pdf
2020-12-01
101
108
10.22059/ijmge.2019.277343.594789
carbonate reservoirs
Data Fusion
Self-Consistent Approximation model
Rock Physics
Xu-Payne model
Hamid
Seifi
hm.seifi@gmail.com
1
Faculty of Mining Engineering., Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Behzad
Tokhmechi
tokhmechi@alumni.ut.ac.ir
2
Associate professor, Faculty of Mining Engineering., Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Ali
Moradzadeh
a_moradzadeh@ut.ac.ir
3
Professor, School of Mining, College of Engineering, University of Tehran, Tehran,Iran
AUTHOR
[1] Avseth, P., Mukerji, T., & Mavko, G. (2010). Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk. Sixth Edition, Cambridge university press, 356p.
1
[2] Lumley, D. E. (2001). Time-lapse seismic reservoir monitoring. Geophysics, 66 (1), 50-53.
2
[3] Hu, X., Hu, S., Jin, F., & Huang, S. (Eds.). (2017). Physics of petroleum reservoirs. Springer.
3
[4] Wang, Z., Wang, R., Schmitt D.R., Zhou, Y., Wang, F., (2017). Carbonate rock physics modelling at ultrasonic and seismic frequencies. 4th International Workshop on Rock Physics, Trondheim, Norway.
4
[5] Zhao, L., Nasser, M., & Han, D. H. (2013). Quantitative geophysical pore-type characterization and its geological implication in carbonate reservoirs. Geophysical Prospecting, 61 (4), 827-841.
5
[6] Ghon, G., Rankey, E. C., Baechle, G. T., Schlaich, M., Ali, S. H., Mokhtar, S., & Poppelreiter, M. C. (2018, June). Carbonate Reservoir Characterisation of an Isolated Platform Integrating Sequence Stratigraphy and Rock Physics in Centr. In 80th EAGE Conference and Exhibition 2018.
6
[7] Li, H., & Zhang, J. (2018). Well log and seismic data analysis for complex pore-structure carbonate reservoir using 3D rock physics templates. Journal of Applied Geophysics, 151, 175-183.
7
[8] Mavko, G., Mukerji, T., & Dvorkin, J. (2009). The rock physics handbook: Tools for seismic analysis of porous media. Second Edition, Cambridge university press,511p.
8
[9] Xu, S., & White, R. E. (1995). A new velocity model for clay‐sand mixtures 1. Geophysical prospecting, 43 (1), 91-118.
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[10] Xu, S., & Payne, M. A. (2009). Modeling elastic properties in carbonate rocks. The Leading Edge, 28 (1), 66-74.
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[11] Nishizawa, O. (1982). Seismic velocity anisotropy in a medium containing oriented cracks. Journal of Physics of the Earth, 30 (4), 331-347.
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[12] Berryman, J. G. (1980). Long‐wavelength propagation in composite elastic media II. Ellipsoidal inclusions. The Journal of the Acoustical Society of America, 68 (6), 1820-1831.
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[13] Eberli, G. P., Baechle, G. T., Anselmetti, F. S., & Incze, M. L. (2003). Factors controlling elastic properties in carbonate sediments and rocks. The Leading Edge, 22 (7), 654-660.
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[14] Bashah, N. S. I., & Pierson, B. J. (2011, January). Quantification of pore structure in a miocene carbonate build-up of Central Luconia, sarawak and its relationship to sonic velocity. In International Petroleum Technology Conference. International Petroleum Technology Conference, Thailand.
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[15] Lubis, L. A., & Harith, Z. Z. T. (2014). Pore type classification on carbonate reservoir in offshore Sarawak using rock physics model and rock digital images. In IOP Conference Series: Earth and Environmental Science (Vol. 19, No. 1, p. 012003). IOP Publishing.
15
[16] Hall, D., & Llinas, J. (2001). Multisensor data fusion. CRC press LLC.
16
[17] Abdulaheem, A., Sabakhy, E., Ahmed, M., Vantala, A., Raharja, P. D., & Korvin, G., (2007). Estimation of permeability from wireline logs in a middle eastern carbonate reservoir using fuzzy logic. In SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers
17
[18] Cuddy, S. J., (2000). Litho-facies and permeability prediction from 108 H. Seifi et al. / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 101-108 electrical logs using fuzzy logic. SPE Reservoir Evaluation & Engineering, 3 (04), 319-324.
18
[19] Valet, L., Mauris, G., Bolon, P., & Keskes, N., (2001). Seismic image segmentation by fuzzy fusion of attributes. IEEE Transactions on Instrumentation and Measurement, 50 (4), 1014-1018.
19
[20] Guo, H. X., Zhu, K. J., Gao, S. W., Li, Y., & Zhou, J. J., (2009). Extracting fuzzy rules based on fusion of soft computing in oil exploration management. Expert Systems with Applications, 36 (2), 2081-2087.
20
[21] Ziyong, Z., Hangyu, Y., & Xiaodan, G., (2017). Fuzzy fusion of geological and geophysical data for mapping hydrocarbon potential based on GIS. Petroleum Geoscience, petgeo2016-100.
21
[22] Hajian, A., & Styles, P., (2018). Applications of Fuzzy Logic in Geophysics. In Application of Soft Computing and Intelligent Methods in Geophysics (pp. 301-371). Springer, Cham.
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[23] Zimmerman, R. W. (1990). Compressibility of sandstones (Vol. 29). Elsevier,183p.
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[25] Berryman, J. G. (1995). Mixture theories for rock properties. Rock physics and phase relations: A handbook of physical constants, American Geophysical Union, 3, 205-228.
25
[26] Misaghi, A., Negahban, S., Landrø, M., & Javaherian, A. (2010). A comparison of rock physics models for fluid substitution in carbonate rocks. Exploration Geophysics, 41 (2), 146-154.
26
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33
[34] Ayub, M., (2009). Choquet and Sugeno Integrals. MSc. Thesis, Blekinge Institute of Technology, Sweden, 80p.
34
ORIGINAL_ARTICLE
Prediction of suction caissons behavior in cohesive soils using computational intelligence methods
Compared to drag anchors, suction caissons (Q) in clays often provide a cost-effective alternative for jacket structures, catenary, tension leg moorings, and taut leg. In this research, two computational approaches are proposed for predicting the uplift capacity of Q in clays. The proposed approaches are based on the combinations of adaptive network-based fuzzy inference system (ANFIS) models (ANFIS-subtractive clustering (ANFIS-SC) and ANFIS-fuzzy c-means (ANFIS-FC)) with metaheuristic techniques (ant colony optimization (ACO) or particle swarm optimization (PSO)). In these approaches, the PSO and ACO algorithms are employed to enhance the accuracy of ANFIS models. In order to develop hybrid models, a comprehensive database from open-source literature is used to train and test the proposed models. In these models, d (diameter of caisson), L (embedded length), D (depth), Su (undrained shear strength of soil), θ (inclined angle), and Tk (load rate parameter) were used as the input parameters. The performance of all models was evaluated by comparing performance indexes, i.e., means squared error and squared correlation coefficient. As a result, PSO and ACO can be used as reliable algorithms to enhance the accuracy of ANFIS models. Moreover, it was found that the ANFIS– subtractive clustering-ACO model provides better results in comparison with other developed hybrid models.
https://ijmge.ut.ac.ir/article_75879_2d944fde843f7c9758e38be3d8eee645.pdf
2020-12-01
109
116
10.22059/ijmge.2019.279269.594798
ANFIS
metaheuristic techniques
subtractive clustering method
fuzzy c-means clustering method
suction caissons capacity
Hadi
Fattahi
h.fattahi@arakut.ac.ir
1
Department of Earth Sciences Engineering, Arak University of Technology, Arak, Iran
LEAD_AUTHOR
Hosnie
Nazari
iron.azar1@gmail.com
2
Department of Earth Sciences Engineering, Arak University of Technology, Arak, Iran
AUTHOR
[1] Alavi, A. H., Aminian, P., Gandomi, A. H., & Esmaeili, M. A. (2011). Genetic-based modeling of uplift capacity of suction caissons. Expert Systems with Applications, 38(10), 12608-12618.
1
[2] Anemangely, M., Ramezanzadeh, A., & Tokhmechi, B. (2017). Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield. Journal of Natural Gas Science and Engineering, 38, 373-387.
2
[3] Bezdek, J. C. (1973). Fuzzy mathematics in pattern classification. Ithaca: Cornell University.
3
[4] Cao, J., Audibert, J., Al-Khafaji, Z., Phillips, R., & Popescu, R. (2002). Penetration resistance of suction caissons in clay. Paper presented at the The Twelfth International Offshore and Polar Engineering Conference.
4
[5] Catalão, J. P. d. S., Pousinho, H. M. I., & Mendes, V. M. F. (2010). H. Fattahi & H. Nazari / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 109-116 115 Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE transactions on power systems, 26(1), 137-144.
5
[6] Chen, M.-Y. (2013). A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Information Sciences, 220, 180-195.
6
[7] Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of intelligent and Fuzzy systems, 2(3), 267- 278.
7
[8] Cho, Y., Lee, T., Park, J., Kwag, D., Chung, E., & Bang, S. (2002). Field tests on suction pile installation in sand. Paper presented at the ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering.
8
[9] Clukey, E., Morrison, M., Gamier, J., & Corté, J. (1995). The response of suction caissons in normally consolidated TLP loading conditions. Paper presented at the Offshore Technology Conference.
9
[10] Clukey, E. C., & Morrison, M. J. (1993). A centrifuge and analytical study to evaluate suction caissons for TLP applications in the Gulf of Mexico. Paper presented at the Design and performance of deep foundations: Piles and piers in soil and soft rock.
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[11] Datta, M., & Kumar, P. (1996). Suction beneath cylindrical anchors in soft clay. Paper presented at the The Sixth International Offshore and Polar Engineering Conference.
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[12] Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4), 28-39.
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[13] Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical computer science, 344(2), 243-278.
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[14] Dyvik, R., Andersen, K. H., Hansen, S. B., & Christophersen, H. P. (1993). Field tests of anchors in clay. I: Description. Journal of Geotechnical engineering, 119(10), 1515-1531.
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[15] Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on.
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[16] El-Gharbawy, S., & Olson, R. (2000). Modeling of suction caisson foundations. Paper presented at the The Tenth International Offshore and Polar Engineering Conference.
16
[17] Ghasemi, E., Kalhori, H., & Bagherpour, R. (2016). A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting. Engineering with Computers, 32(4), 607-614.
17
[18] Ghomsheh, V. S., Shoorehdeli, M. A., & Teshnehlab, M. (2007). Training ANFIS structure with modified PSO algorithm. Paper presented at the 2007 Mediterranean Conference on Control & Automation.
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[19] Grima, M. A., Bruines, P., & Verhoef, P. (2000). Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnelling and Underground Space Technology, 15(3), 259-269.
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[20] Hasanipanah, M., Amnieh, H. B., Arab, H., & Zamzam, M. S. (2018). Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications, 30(4), 1015-1024.
20
[21] Iphar, M., Yavuz, M., & Ak, H. (2008). Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environmental geology, 56(1), 97-107.
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[22] Jang, J.-S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
22
[23] Jiang, H., Kwong, C., Ip, W., & Wong, T. (2012). Modeling customer satisfaction for new product development using a PSObased ANFIS approach. Applied Soft Computing, 12(2), 726-734.
23
[24] Kaveh, A., Hamze-Ziabari, S., & Bakhshpoori, T. (2018). Feasibility of PSO-ANFIS-PSO and GA-ANFIS-GA models in prediction of peak ground acceleration. International Journal of Optimization in Civil Engineering, 8(1), 1-14.
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[27] Meysam Mousavi, S., Tavakkoli-Moghaddam, R., Vahdani, B., Hashemi, H., & Sanjari, M. (2013). A new support vector modelbased imperialist competitive algorithm for time estimation in new product development projects. Robotics and ComputerIntegrated Manufacturing, 29(1), 157-168.
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[30] Pousinho, H. M. I., Mendes, V. M. F., & Catalão, J. P. d. S. (2012). Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach. International Journal of Electrical Power & Energy Systems, 39(1), 29-35.
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[38] Ustun, S. V., & Demirtas, M. (2009). Modeling and control of V/f 116 H. Fattahi & H. Nazari / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 109-116 controlled induction motor using genetic-ANFIS algorithm. Energy Conversion and Management, 50(3), 786-791.
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43
ORIGINAL_ARTICLE
Robustness price of open-pit mine production scheduling
An Open-Pit Production Scheduling (OPPS) problem focuses on specifying block production scheduling to achieve the highest possible Net Present Value (NPV). This paper presents a new mathematical model for OPPS under uncertainty. To this end, a robust box and ellipsoidal counterpart approach was used. The proposed method was implemented in a hypothetical model. A Genetic Algorithm (GA) and an exact mathematical modeling approach were used to solve the model. It was shown that the scheduling of deterministic and robust models in various conditions is different. Considering the type of robust counterparts, different production plans under various conditions were scheduled. Furthermore, the price of robustness was determined for various levels of conservation.
https://ijmge.ut.ac.ir/article_75993_82dff395b7f977ef350b625488933cca.pdf
2020-12-01
117
122
10.22059/ijmge.2019.267417.594762
Open-pit
Production Scheduling
robust counterpart and uncertainty
Aref
Alipour
aref.alipour@gmail.com
1
Department of Mining Engineering, Urmia University of Technology, Urmia, Iran.
LEAD_AUTHOR
Ali Asghar
Khodayari
khodaiar@ut.ac.ir
2
School of Mining Engineering, College of Engineering, University of Tehran
AUTHOR
Ahmad
Jafari
ajafari@ut.ac.ir
3
School of Mining Engineering, College of Engineering, University of Tehran
AUTHOR
Reza
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
4
School of Industrial Engineering & Engineering Optimization Research Group, College of Engineering, University of Tehran, Tehran, Iran.
AUTHOR
[1] Alipour, A., Khodaiari, A. A., Jafari, A., and TavakkoliMoghaddam, R., "Robust production scheduling in open-pit mining under uncertainty: a box counterpart approach," Journal of Mining and Environment, vol. 8, no. 2, pp. 255-267, 122 A. Alipour et al. / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 117-122 04/01 2017.
1
[2] Alipour, A., Khodaiari, A. A., Jafari, A., and TavakkoliMoghaddam, R., "Uncertain production scheduling optimization in open-pit mines and its ellipsoidal robust counterpart," International Journal of Management Science and Engineering Management, pp. 1-9, 2018.
2
[3] Averbakh, I., "Minmax regret solutions for minimax optimization problems with uncertainty," Operations Research Letters, vol. 27, no. 2, pp. 57-65, 2000.
3
[4] Soyster, A. L., "Technical note—convex programming with set-inclusive constraints and applications to inexact linear programming," Operations Research, vol. 21, no. 5, pp. 1154- 1157, 1973.
4
[5] Mulvey, J. M., Vanderbei, R. J., and Zenios, S. A., "Robust optimization of large-scale systems," Operations Research, vol. 43, no. 2, pp. 264-281, 1995.
5
[6] Yu, C.-S. and Li, H.-L., "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, vol. 64, no. 1, pp. 385-397, 2000.
6
[7] Ben-Tal, A. and Nemirovski, A., "Robust convex optimization," Mathematics of Operations Research, vol. 23, no. 4, pp. 769-805, 1998.
7
[8] Ben-Tal, A. and Nemirovski, A., "Robust solutions of linear programming problems contaminated with uncertain data," Mathematical programming, vol. 88, no. 3, pp. 411-424, 2000.
8
[9] Ghaoui, L. E., Oks, M., and Oustry, F., "Worst-case value-atrisk and robust portfolio optimization: A conic programming approach," Operations Research, vol. 51, no. 4, pp. 543-556, 2003.
9
[10] Bertsimas, D. and Sim, M., "Robust discrete optimization and network flows," Mathematical programming, vol. 98, no. 1-3, pp. 49-71, 2003.
10
[11] Bertsimas, D. and Sim, M., "The price of robustness," Operations Research, vol. 52, no. 1, pp. 35-53, 2004.
11
[12] Verderame, P. M. and Floudas, C. A., "Operational planning of large-scale industrial batch plants under demand due date and amount uncertainty: II. Conditional value-at-risk framework," Industrial & engineering Chemistry Research, vol. 49, no. 1, pp. 260-275, 2009.
12
[13] Chen, W., Sim, M., Sun, J., and Teo, C.-P., "From CVaR to uncertainty set: Implications in joint chance-constrained optimization," Operations Research, vol. 58, no. 2, pp. 470- 485, 2010.
13
[14] Li, Z., Ding, R., and Floudas, C. A., "A comparative theoretical and computational study on robust counterpart optimization: I. Robust linear optimization and robust mixed integer linear optimization," Industrial & Engineering Chemistry Research, vol. 50, no. 18, pp. 10567-10603, 2011.
14
[15] Li, Z. and Floudas, C. A., "A comparative theoretical and computational study on robust counterpart optimization: III. Improving the quality of robust solutions," Industrial & Engineering Chemistry Research, vol. 53, no. 33, pp. 13112- 13124, 2014.
15
[16] Li, Z. and Li, Z., "Chance constrained planning and scheduling under uncertainty using robust optimization approximation," IFAC-PapersOnLine, vol. 48, no. 8, pp. 1156- 1161, 2015/01/01 2015.
16
[17] Li, Z. and Floudas, C. A., "Robust counterpart optimization: Uncertainty sets, formulations and probabilistic guarantees," in Proceedings of the 6th conference on Foundations of Computer-Aided Process Operations, Savannah (Georgia), 2012.
17
[18] Kumral, M., "Robust stochastic mine production scheduling," Engineering Optimization, vol. 42, no. 6, pp. 567-579, 2010/06/01 2010.
18
[19] Alipour, A., khodaiari, A. A., Jafari, A., and TavakkoliMoghaddam, R., "A genetic algorithm approach for open-pit mine production scheduling," Int. Journal of Mining & GeoEngineering, vol. 51, no. 1, pp. 47-52, 2017.
19
ORIGINAL_ARTICLE
Two-dimensional upscaling of reservoir data using adaptive bandwidth in the kernel function
In this paper, a new method called adaptive bandwidth in the kernel function has been used for two-dimensional upscaling of reservoir data. Bandwidth in the kernel can be considered as a variability parameter in porous media. Given that the variability of the reservoir characteristics depends on the complexity of the system, either in terms of geological structure or the specific feature distribution, variations must be considered differently for upscaling from a fine model to a coarse one. The upscaling algorithm, introduced in this paper, is based on the kernel function bandwidth, written in combination with the A* search algorithm and the first-depth search algorithm. In this algorithm, each cell in its x and y neighborhoods as well as the optimal bandwidth, obtained in two directions will be able to be merged with its adjacent cells. The upscaling process is performed on artificial data with 30×30 grid dimensions and SPE-10 model as real data. Four modes are used to start the point of upscaling and the process is performed according to the desired pattern, and in each case, the upscaling error and the number of final upscaled blocks are obtained. Based on the number of coarsen cells as well as the upscaling error, the first pattern is selected as the optimal pattern for synthetic data and the second pattern is selected as the optimal simulator model for real data. In this model, the number of cells was 236 and 3600, and the upscaling errors for synthetic and real data were 0.4183 and 12.2, respectively. The results of the upscaling in the real data were compared with the normalization method and showed that the upscaling error of the normalization method was 15 times the upscaling error of the kernel bandwidth algorithm.
https://ijmge.ut.ac.ir/article_75994_8d883f8ceac3c9d4b49304a9a8a9956d.pdf
2020-12-01
123
128
10.22059/ijmge.2019.270774.594768
Upscaling
Bandwidth
Kernel
Cell
Optimum model
Mohammadreza
Azad
azad66.mohammad@gmail.com
1
Phd Student of Shahrood university of Technology
LEAD_AUTHOR
Abulghasem
Kamkar Ruhani
kamkarr@yahoo.com
2
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Behzad
Tokhmechi
tokhmechi@alumni.ut.ac.ir
3
Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Mohammad
Arashi
m_arashi_stat@yahoo.com
4
Faculty of Mathematical Science, Shahrood University of Technology, Shahrood, Iran
AUTHOR
[1] Christie, M. A., (1996). Upscaling for Reservoir Simulation. Journal of Petroleum Technolog, 48(11), 1-4. https://doi.org/10.2118/37324-JPT.
1
[2] Christie, M. A., & Blunt, M. J. (2001). Tenth SPE Comparative Solution Project: A Comparison of Upscaling Techniques. Journal of Petroleum Technology. 4(4), 1-10. https://doi.org/10.2118/72469-PA.
2
[3] Chen, T., Clauser, C., Marquart, G., Willbrand, K. and Mottaghy, D. (2015). A new upscaling method for fractured porous media. journal of Advances in Water Resources, 80, 60-68. https://doi.org/10.1016/j.advwatres.2015.03.009.
3
[4] Farmer CL., (2000). Upscaling: a review. International Journal Numerical Methods Fluids. 40, 63 – 78. https://doi.org/10.1002/fld.267.
4
[5] Dadvar, M., & Sahimi, M., (2007). The effective diffusivities in porous media with and without nonlinear reactions. Chemical engineering science. 62, 1466-1476. https://doi.org/10.1016/j.ces.2006.12.002
5
[6] Hochstetler, D. L., & Kitanidis, P. K., (2013). The behavior of effective rate constants for bimolecular reactions in an asymptotic transport regime. Journal of contaminant hydrology. 144, 88-98. https://doi.org/10.1016/j.jconhyd.2012.10.002.
6
[7] Pereira, J. M. C., Navalho, J. E. P., Amador, A. C. G., & Pereira, J. C. F., (2014). Multi-scale modeling of diffusion and reaction–diffusion phenomena in catalytic porous layers: comparison with the 1D approach. Chemical Engineering Science. 117, 364-375. https://doi.org/10.1016/j.ces.2014.06.028.
7
[8] Ratnakar, R. R., Bhattacharya, M., & Balakotaiah, V., (2012). Reduced order models for describing dispersion and reaction in monoliths. Chemical Engineering Science. 83, 77-92. https://doi.org/10.1016/j.ces.2011.09.056.
8
[9] Chen, T., Clauser, C., Marquart, G., Willbrand, K. and Hiller, T., (2018). Upscaling permeability for three-dimensional fractured porous rocks with the multiple boundary method, Hydrogeology Journal, 1-14. https://doi.org/10.1007/s10040- 018-1744-z.
9
[10] Ding, D. Y., (2004). Near-Well Upscaling for Reservoir Simulations. Oil & Gas Science and Technology. 59(2), 157- 165.
10
[11] Gholinezhad, S., Jamshidi, S. and Hajizadeh, A., (2015). QuadTree decomposition method for areal upscaling of heterogeneous reservoirs: Application to arbitrary shaped reservoirs. Fuel, 139, 659-670. https://doi.org/10.1016/j.fuel.2014.09.039.
11
[12] Moslehi, M., Felipe P.J., de Barros, Ebrahimi, F. and Sahimi, M.,(2016). Upscaling of solute transport in disordered porous media by wavelet transformations, Advances in Water Resources, 96, 180-189. https://doi.org/10.1016/j.advwatres.2016.07.013.
12
[13] King, P.R. (1989). The Use of Renormalization for Calculating Effective Permeability, Transport in Porous Media, 4, 37-58.
13
[14] Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Published in Monographs on Statistics and Applied Probability, London: Chapman and Hall, 176 P.
14
[15] Scott, D. W., 1992. Multivariate Density Estimation: Theory, Practice, and Visualization. New York: John Wiley & Sons Inc., pp. 384.
15
[16] Raykar, V.C., Duraiswami, R., Zhao, L.H., (2010). Fast computation of kernel estimators. Journal of Computational and Graphical Statistics, 19 (1), 205–220.
16
[17] Hardle, W. K. K., Muller, M. and Sperlich., (2004). Nonparametric and semiparametric models, Springer Series in Statistics, 277 P.
17
[18] Delling, D., Sanders, P., Schultes, D. and Wagner, D., (2009). Algorithmic of Large and Complex Networks: Design, Analysis, and Simulation. Springer-Verlag Berlin Heidelberg. 401 P.
18
[19] Even, S., (2011), Graph Algorithms (2nd Ed.). Cambridge University Press, 48 P.
19
ORIGINAL_ARTICLE
An improved model of continuous leaching systems using segregation approach
In this study, a simplified dissolution model has been developed to evaluate the performance of continuous leaching reactors. The model considers continuous reduction of the surface area of particles using the distribution of their size and residence time. The model was validated by the bioleaching of a pyrite-arsenopyrite concentrate in the pilot plant scale, which resulted in good agreement between the experimental data and the predicted values. The developed model was also used to predict the outlet mass density function of particles, whose results showed that the mean particle size would not necessarily decrease as the mean residence time in the leaching process decreased. Using this model, the effect of operating parameters (e.g., particle size distribution, inlet flow, reagent concentration, kinetic parameters, and the type of residence time distribution) on the reactor performance can be predicted. Therefore, the model can be used for dynamic and static analyses of leaching circuits as well as designing and optimizing the processing plants.
https://ijmge.ut.ac.ir/article_75995_28cbffc4f2c2621ccc9917a05902bd41.pdf
2020-12-01
129
133
10.22059/ijmge.2020.288222.594823
Continuous leaching
Dissolution modelling
Particle size
Residence Time
Abdolrahim
Foroutan
abr.foroutan@gmail.com
1
Mining and Metallurgical Engineering Department, Yazd University, Yazd, Iran;
AUTHOR
Hojjat
Naderi
naderi@yazd.ac.ir
2
Mining and Metallurgical Engineering Department, Yazd University, Yazd, Iran;
LEAD_AUTHOR
Mohammad Reza
Khalesi
mrkhalesi@modares.ac.ir
3
Department of Mining Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Reza
Dehghan
rdehghans@yazd.ac.ir
4
Mining and Metallurgical Engineering Department, Yazd University, Yazd, Iran;
AUTHOR
[1] Papangelakis, V. & Demopoulos, G. (1993). Modeling, Simulation and Control of Hydrometallurgical Processes: Proceeding of the international symposium on Modeling, simulation and control of hydrometallurgical processes, Quebec City. Quebec, August 24-September2.
1
[2] Coelho, F.E.B., Balarini, J. C., Araújo, E.M.R., Miranda, T.L.S., Peres, A. E. C., Martins A.H., & Salum, A. (2018). Roasted zinc concentrate leaching: Population balance modeling and validation. Hydrometallurgy, 175(0), 208-217. doi: https://doi.org/10.1016/j.hydromet.2017.11.013.
2
[3] Crundwell, F. & Bryson, A. (1992). The Modeling of particulate leaching reactors-the population balance approach. Hydrometallurgy, 29(1-3), 275-295. doi: https://doi.org/10.1016/0304-386X(92)90018-U
3
[4] Sohn, H.Y. & Wadsworth, M.E. (2013). Rate processes of extractive metallurgy. Springer Science & Business Media. doi: https://doi.org/10.1007/978-1-4684-9117-3
4
[5] Dixon, D.G. (1995). Improved methods for the design of multistage leaching systems. Hydrometallurgy, 39(1), 337- 351. doi: https://doi.org/10.1016/0304-386X(95)00040-N.
5
[6] Peters, E. (1991). The mathematical modeling of leaching systems. JOM, 43(2), 20-26.
6
[7] Dixon, D.G. (1996). The multiple convolution integral: a new method for modeling multistage continuous leaching reactors. Chemical engineering science, 51(21), 4759-4767. doi: https://doi.org/10.1016/0009-2509(96)00334-X.
7
[8] Kotsiopoulos, A., Hansford, G.S., & Rawatlal, R. (2008). An approach of segregation in modeling continuous flow tank bioleach systems. AIChE journal, 54(6), 1592-1599. doi: https://doi.org/10.1002/aic.11479
8
[9] Drossou, M. (1986). The kinetics of the bioleaching of a refractory gold-bearing pyrite concentrate. University of Cape Town. doi: https://open.uct.ac.za/bitstream/handle/11427/23206/Drossou _kinetics_1982.pdf?sequence=1.
9
[10] McKibben, M.A. & Barnes, H.L. (1986). Oxidation of pyrite in low temperature acidic solutions: Rate laws and surface textures. Geochimica et Cosmochimica Acta, 50(7),1509- 1520. doi: https://doi.org/10.1016/0016-7037(86)90325-X.
10
[11] Wiersma, C. & Rimstidt, J. (1984). Rates of reaction of pyrite and marcasite with ferric iron at pH 2. Geochimica et Cosmochimica Acta, 48(1), 85-92. doi: https://doi.org/10.1016/0016-7037(84)90351-X.
11
[12] Boon, M.(1996). Theoretical and experimental methods in the Modeling of bio-oxidation kinetics of sulphide minerals. TU Delft, Delft University of Technology. doi: http://resolver.tudelft.nl/uuid:d97e28be-eb6d-452b-8648- 807c192a2600.
12
[13] Vignes, A. (2013). Extractive metallurgy 3: Processing operations and routes. John Wiley & Sons. doi: 10.1002/9781118617106.
13
[14] Petersen, J.(2010). Modeling of bioleach processes: connection between science and engineering. Hydrometallurgy, 104(3- 4), 404-409. doi: https://doi.org/10.1016/j.hydromet.2010.02.023
14
[15] Hansford, G. & Miller, D.(1993). Biooxidation of a gold‐ bearing pyrite‐arsenopyrite concentrate. FEMS microbiology reviews.,11(1‐3), 175-181. [16] de Andrade Lima, L. & Hodouin, D. (2005). Residence time distribution of an industrial mechanically agitated cyanidation tank. Minerals engineering, 18(6), 613-621. doi: https://doi.org/10.1016/j.mineng.2004.10.006
15
[17] Murphy, T. (2002). Residence Time Distribution of Solid Particles in a CSTR. McGill University Libraries. doi: http://digitool.Library.McGill.CA:80/R/-?func=dbin-jumpfull&object_id=79251&silo_library=GEN01
16
[18] Crundwell, F., Preez, N, Du. & Lloyd, J. (2013). Dynamics of particle-size distributions in continuous leaching reactors and autoclaves. Hydrometallurgy, 133, 44-50. doi: https://doi.org/10.1016/j.hydromet.2012.11.016
17
[19] Saloojee, F. & Crundwell, F, K. (2016). Optimization of circuits for pressure leaching of sulphide ores and concentrates. Journal of the Southern African Institute of Mining and Metallurgy, 116(6), 517-524. doi: 10.17159/2411- 9717/2016/v116n6a5.
18
ORIGINAL_ARTICLE
A non-monetary valuation system for open-pit mine design
In open-pit mining, different designs are created, such as optimal ultimate pit limit and production planning. In order to determine the ultimate pit limit, two approaches are generally used based on geological and economic block models. In this paper, according to the long-term trend of metals price and mining costs, some suggestions were made to design the ultimate pit limit using the geological block model. In addition, a grade-based objective function was presented for determining the ultimate pit limit. Then, in order to solve the problem, a heuristic algorithm was developed to simultaneously determine the ultimate pit limit and the sequence of block mining. For a 2D geological block model, the final pit was generated using the proposed algorithm. Furthermore, to validate the generated pit limit, the results of a 3D geological block model were compared with those of the Lerchs-Grossman algorithm. The comparison showed that the two pits corresponded to each other with an accuracy value of 97.7 percent.
https://ijmge.ut.ac.ir/article_76605_cf1b103cfe82de2b187b481d52d903e0.pdf
2020-12-01
135
145
10.22059/ijmge.2019.262989.594752
Open pit design
Ultimate pit
Non-monetary value
Optimization
Heuristic algorithm
Meisam
Saleki
meisam.saleki@gmail.com
1
Department of Mining Eng, Geophysics and Petroleum, Shahrood University of Technology
LEAD_AUTHOR
Reza
Khalo Kakaei
r_kakaie@shahroodut.ac.ir
2
shahrood
AUTHOR
Mohammad
Ataei
ataei@shahroodut.ac.ir
3
third author
AUTHOR
[1] Pana, M. T. (1965, March). The simulation approach to openpit design. In APCOM SYMPOSIUM (Vol. 5, pp. 127-138).
1
[2] Wright, A. (1999). MOVING CONE II-A simple algorithm for optimum pit limits design. Proceedings of the 28th APCOM, 367-374.
2
[3] David, M., Dowd, P. A., & Korobov, S. (1974, April). Forecasting departure from planning in open-pit design and grade control. In 12th Symposium on the application of computers and operations research in the mineral industries (APCOM) (Vol. 2, pp. F131-F142).
3
[4] Roman, R. J. (1974). The role of time value of money in determining an open-pit mining sequence and pit limits. In Proc. 12th Symp. Application Computers and Operation Research in the Mineral Industry.
4
[5] Lerchs, H., & Grossman, I. F. (1965). Optimum design of openpit mines. CIM bulletin, 58(633), 47-54.
5
[6] Yegulalp, T. M., & Arias, J. A. (1992, April). A fast algorithm to solve the ultimate pit limit problem. In 23rd International Symposium on the Application of Computers and Operations Research in The Mineral Industries (pp. 391-398). Littleton, Co: AIME.
6
[7] Johnson, T. B., & Barnes R. J. (1988). Application of the Maximal Flow algorithm to ultimate pit design. Engineering design: better results through operations research methods, 518-531.
7
[8] François-Bongarçon, D., & Guibal, D. (1982, April). Algorithms for parameterizing reserves under different geometrical constraints. In Proc. 17th symposium on the application of computers and operations research in the mineral industries (APCOM: AIME) (pp. 297-309).
8
[9] Wang, Q., & Sevim, H. (1992). Enhanced production planning in open-pit mining through intelligent dynamic search. Institute of Mining Metallurgy (ed), 23, 461-471.
9
[10] Wang, Q., & Sevim H. (1993). Open-pit production planning through pit-generation and pit-sequencing. Transactions of the American Society for Mining, Metallurgy and Exploration, 294(7), 1968-1972.
10
[11] Wang, Q., & Sevim H. (1995). Alternative to parameterization in finding a series of maximum-metal pits for production planning. Mining engineering, 178-182.
11
[12] Gershon, M. (1987). Heuristic approaches for mine planning and production scheduling. International Journal of Mining and Geological Engineering, 5(1), 1-13.
12
[13] Dimitrakopoulos, R., Farrelly, C. T., & Godoy, M. (2002). Moving forward from traditional optimization: grade uncertainty and risk effects in open-pit design. Mining Technology, 111(1), 82-88.
13
[14] Dimitrakopoulos, R., & Ramazan, S. (2004). Uncertainty based production scheduling in open-pit mining. SME transactions, 316.
14
[15] Harrison, S., Leuangthong, O., Crawford, B., & Oshust, P. M. Saleki et al. / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 135-145 145 (2009). Uncertainty-based grade modelling of kimberlite: a case study of the Jay kimberlite pipe, EKATI Diamond Mine, Canada. Lithos, 112, 73-82.
15
[16] Gholamnejad, J., & Moosavi, E. (2012). A new mathematical programming model for long-term production scheduling considering geological uncertainty. Journal of the Southern African Institute of Mining and Metallurgy, 112(2), 77-81.
16
[17] Moosavi, E., & Gholamnejad, J. (2015). Long-term production scheduling modeling for the open-pit mines considering tonnage uncertainty via indicator kriging. Journal of Mining Science, 51(6), 1226-1234.
17
[18] Lamghari, A., & Dimitrakopoulos, R. (2016). Network-flow based algorithms for scheduling production in multiprocessor open-pit mines accounting for metal uncertainty. European Journal of Operational Research, 250(1), 273-290.
18
[19] Gilani, S. O., & Sattarvand, J. (2016). Integrating geological uncertainty in long-term open-pit mine production planning by ant colony optimization. Computers & Geosciences, 87, 31-40.
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[20] Baek, J., Choi, Y., & Park, H. S. (2016). Uncertainty representation method for open-pit optimization results due to variation in mineral prices. Minerals, 6(1), 17.
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[21] Upadhyay, S. P., & Askari-Nasab, H. (2018). Simulation and optimization approach for uncertainty-based short-term planning in open-pit mines. International Journal of Mining Science and Technology, 28(2), 153-166.
21
[22] Tahernejad, M. M., Ataei, M., & Khalokakaie, R. (2018). A practical approach to open-pit mine planning under price uncertainty using information gap decision theory. Journal of Mining and Environment, 9(2), 527-537.
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[23] Paricheh, M., & Osanloo, M. (2018). A simulation-based risk management approach to locating facilities in open-pit mines under price and grade uncertainties. Simulation Modelling Practice and Theory.
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[24] Tahernejad, M. M., Khalo Kakaei, R., & Ataei, M. (2018). Analyzing the effect of ore grade uncertainty in open-pit mine planning; A case study of Rezvan iron mine, Iran. Int. Journal of Mining & Geo-Engineering.
24
[25] Jamshidi, M., & Osanloo, M. (2018). UPL determination of multi-element deposits with grade uncertainty using a new block economic value calculation approach. Journal of Mining and Environment, 9(1), 61-72.
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[26] Dimitrakopoulos, R. G., & Sabour, S. A. A. (2007). Evaluating mine plans under uncertainty: Can the real options make a difference?. Resources Policy, 32(3), 116-125.
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[27] Akbari, A. D., Osanloo, M., & Shirazi, M. A. (2009). Reserve estimation of an open-pit mine under price uncertainty by real option approach. Mining Science and Technology (China), 19(6), 709-717.
27
[28] Haque, M. A., Topal, E., & Lilford, E. (2016). Estimation of mining project values through real option valuation using a combination of hedging strategy and a mean reversion commodity price. Natural Resources Research, 25(4), 459- 471.
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[29] Siña, M., & Guzmán, J. I. (2018). Real option valuation of open-pit mines with two processing methods. Journal of Commodity Markets.
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[32] Wellmer, F. W., Dalheimer, M., & Wagner, M. (2007). Economic evaluations in exploration. Springer Science & Business Media.
32
ORIGINAL_ARTICLE
The effects of temperature on mechanical properties of rocks
In a natural condition, temperature variations and phase transition of pore water are the two most effective factors on the mechanical properties of rocks. Instabilities occurred as a result of climate changes, highlight the importance of rock characteristics. This paper conducted a laboratory investigation to study the temperature-dependent mechanical behavior of rocks and to examine the quantity and quality of this relationship. In order to perform laboratory tests, a temperature-controlling apparatus was developed. Studies were conducted on 152 specimens of concrete and three types of rocks, including granite, red travertine, and walnut travertine. Then, the effect of temperature variations, from -30 to +30ºC with 10ºC intervals on the mechanical properties of the rocks, was studied. The results showed that temperature reduction, caused by pore water phase transition, improved the mechanical properties of the rocks. The maximum variation of the mean uniaxial compressive strength from +30ºC to -30ºC belonged to granite (40.1%), while the concrete specimen showed the minimum variation on the test results (33.7%). Red travertine (38.7%) and walnut travertine (34.2%) exhibited lower variations compared to granite. Also, the maximum variation in the mechanical behavior of rocks occurred between -10 and 0 °C. Additionally, variations in the mechanical properties of cracked rock samples were more than the rocks with spherical pore and the same porosity percent.
https://ijmge.ut.ac.ir/article_76606_1d0176b2813faf67a8ce78eee8b0fa99.pdf
2020-12-01
147
152
10.22059/ijmge.2019.271982.594771
temperature
Mechanical Behavior
Travertine
Granite
Concrete
Jabbar
Ashrafi
j.ashrafi@mi.iut.ac.ir
1
Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Lohrasb
Faramarzi
lfaramarzi@cc.iut.ac.ir
2
Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Mohammad
Darbor
darbor@sut.ac.ir
3
Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran
LEAD_AUTHOR
Mostafa
Sharifzadeh
m.sharifzadeh@curtin.au
4
Department of Mining Engineering, Western Australian School of Mines (WASM), Curtin University, Australia
AUTHOR
Behnam
Ferdosi
behnam.ferdosi@curtin.au
5
Department of Mining Engineering, Western Australian School of Mines (WASM), Curtin University, Australia
AUTHOR
[1] Lu, Z., Xian, S., Yao, H., Fang, R., & She, J. (2019). Influence of freeze-thaw cycles in the presence of a supplementary water supply on mechanical properties of compacted soil. Cold Regions Science and Technology, 157, 42-52.
1
[2] Liu, S., & Xu, J. (2015). An experimental study on the physicomechanical properties of two post-high-temperature rocks. Engineering Geology, 185, 63-70.
2
[3] Yu, J., Chen, X., Li, H., Zhou, J. W., & Cai, Y. Y. (2015). Effect of freeze-thaw cycles on mechanical properties and permeability of red sandstone under triaxial compression. Journal of Mountain Science, 12(1), 218-231.
3
[4] Lu, C., Sun, Q., Zhang, W., Geng, J., Qi, Y., & Lu, L. (2017). The effect of high temperature on tensile strength of sandstone. Applied Thermal Engineering, 111, 573-579.
4
[5] Su, H., Jing, H., Yin, Q., & Yu, L. (2018). Effect of thermal environment on the mechanical behaviors of building marble. Advances in Civil Engineering, 1-8.
5
[6] Chen, Y.L., Ni, J., Shao, W., & Azzam, R. (2012). Experimental study on the influence of temperature on the mechanical properties of granite under uniaxial compression and fatigue loading. International Journal of Rock Mechanics & Mining Sciences, 56, 62-66.
6
[7] Kolay, E. (2016). Modeling the effect of freezing and thawing for sedimentary rocks. Environmental Earth Sciences, 75, 210.
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[8] Luo, X., Jiang, N., Fan, X., Mei, N., & Luo, H. (2015). Effects of freeze-thaw on the determination and application of parameters of slope rock mass in cold regions. Cold Regions Science and Technology, 110, 32-37.
8
[9] Celik, M.Y., Akbulut, H., & Ergul, A. (2014). Water absorption process effect on strength of Ayazini tuff, such as the uniaxial compressive strength (UCS), flexural strength and freeze and thaw effect. Environmental Earth Sciences, 71(9), 4247-4259.
9
[10] Sammis, C.G., & Biegel, R.L. (2005). Seismic radiation from explosion in frozen crystalline rock. 27th Seismic Research Review: Ground-Based Nuclear Explosion Monitoring Technologies, Rancho Mirage, California.
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[11] Nicholson, D.T., & Nicholson, F.H. (2000). Physical deterioration of sedimentary rocks subjected to experimental freeze–thaw weathering. Earth Surface Processes and Landforms, 25(12), 1295-1307.
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[12] Xu, G., & Liu, Q.S. (2005). Analysis of mechanism of rock failure due to freeze–thaw cycling and mechanical testing study on frozen–thawed rocks. Chinese Journal of Rock Mechanics and Engineering, 24(17), 3076-3082.
12
[13] Takarli, M., Prince, W., & Siddique, R. (2008). Damage in granite under heating/cooling cycles and water freeze–thaw condition. International Journal of Rock Mechanics & Mining Sciences, 45(7), 1164-1175.
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[14] Tan, X., Chen, W., Yang, J., & Cao, J. (2011). Laboratory investigations on the mechanical properties degradation of granite under freeze–thaw cycles. Cold Regions Science and Technology, 68(3), 130-138.
14
[15] Kodama, J., Goto, T., Fujii, Y., & Hagan, P. (2013). The effects of water content, temperature and loading rate on strength and failure process of frozen rocks. International Journal of Rock Mechanics & Mining Sciences, 62, 1-13.
15
[16] Hosseini, M. (2017). Effect of temperature as well as heating and cooling cycles on rock properties. Journal of Mining & Environment, 8(4), 631-644.
16
[17] Chen, S., Yang, C., & Wang, G. (2017). Evolution of thermal damage and permeability of Beishan granite. Applied Thermal Engineering, 110, 1533-1542.
17
[18] Lu, Y., Liu, S., Alonso, E., Wang, L., Xu, L., & Li, Z. (2019). Volume changes and mechanical degradation of a compacted expansive soil under freeze-thaw cycles. Cold Regions Science and Technology, 157, 206-214.
18
[19] Ruedrich, J., & Siegesmund, S. (2007). Salt and ice crystallisation in porous sandstones. Environmental Geology, 52(2), 225-249.
19
[20] Chen, T.C., Yeung, M.R., & Mori, N. (2004). Effect of water saturation on deterioration of welded tuff due to freeze–thaw action. Cold Regions Science and Technology, 38(2-3), 127- 136.
20
[21] ISRM. (2007). The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974-2006. In R. Ulusay & J. A. Hudson (Eds.), Suggested methods prepared by the commission on testing methods (pp. 137–140). Ankara: International Society for Rock Mechanics, Compilation Arranged by the ISRM Turkish National Group.
21
[22] Hauck, C. (2001). Geophysical methods for detecting permafrost in high mountains (Doctoral Dissertation). ETH Zurich, Zurich, Switzerland.
22
[23] Ghobadi, M.H., Beydokhti, A.R.T., Nikudel, M.R., Asiabanha, A., & Karakus, M. (2016). The effect of freeze–thaw process on the physical and mechanical properties of tuff. Environmental Earth Sciences, 75, 846.
23
[24] Hale, P. A., & Shakoor, A. (2003). A laboratory investigation of the effect of cyclic heating and cooling, wetting and drying, and freezing and thawing on the comprehensive strength of selected sandstones. Environmental and Engineering Geoscience, 9(2), 117–130.
24
[25] Concu, G., Nicolo, B.D., & Valdes, M. (2014). Prediction of building limestone physical and mechanical properties by means of ultrasonic P-wave velocity. The Scientific World Journal, 1-8. [
25
26] Ding, Q.L., Song, S.B. (2016). Experimental investigation of the relationship between the P-wave velocity and the mechanical properties of damaged sandstone. Advances in Materials Science and Engineering, 1-10.
26
[27] Bieniawski, Z.T. (1967). Mechanism of brittle fracture of rock, Part I: theory of the fracture process. International Journal of Rock Mechanics and Mining Sciences, 4(4), 395-406
27
ORIGINAL_ARTICLE
Geostatistical-based geophysical model of electrical resistivity and chargeability data applied to image copper mineralization in the Ghalandar deposit, Iran
This research aims to construct 3D geophysical models of electrical resistivity and induced polarization by interpolating 2D inverted physical models through the geostatistical approach. The applicability of the method was examined for the Ghalandar porphyry-skarn copper deposit in the Agh-Daragh region, northwest of Iran. The 3D geophysical properties and block models of Cu grades were prepared by implementing the kriging interpolation method, whereby the recovered electrical models were closely linked to the Cu-sulfide mineralization. In order to evaluate the efficiency of the applied technique, the variogram models were validated using a cross-validation analysis of the kriging operation, proving the accuracy of data interpolation for each model. For the sake of meaningful correlation between geophysical models and Cu grades, the mineralization zones were extracted and subsequently propagated in the 3D space according to the generated physical properties. Meanwhile, the evaluation matrix was utilized to assess the performance of acquired results, where it confirmed that simultaneous consideration of physical models could much better determine the location of the copper mineralization. Also, the Swath plot was used as a second validation way to compare the anomalous zones.
https://ijmge.ut.ac.ir/article_76650_5428cef9e274a1efde91bbb10d6ffd6e.pdf
2020-12-01
153
160
10.22059/ijmge.2019.275091.594780
chargeability
Copper Mineralization
cross validation
Electrical resistivity
Kriging
Siavash
Salarian
siavash.salarian@gmail.com
1
Simulation and Data Processing Lab, School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Omid
Asghari
o.asghari@ut.ac.ir
2
Simulation and Data Processing Lab, School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Maysam
Abedi
maysamabedi@ut.ac.ir
3
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Saeed Kazem
Alilou
s.k.alilou@ut.ac.ir
4
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Sun, J., & Li, Y. (2014). Exploration of a sulfide deposit using joint inversion of magnetic and induced polarization data. In SEG Technical Program Expanded Abstracts 2014 (pp. 1780- 1784). Society of Exploration Geophysicists.
1
[2] Telford, W. M., Geldart, L. P., Sheriff, R. E. (1990). Applied geophysics (Vol. 1). Cambridge university press.
2
[3] Oldenburg, D. W., Li, Y., & Ellis, R. G. (1997). Inversion of geophysical data over a copper gold porphyry deposit: a case history for Mt. Milligan. Geophysics, 62(5), 1419-1431.
3
[4] Mostafaei, K., & Ramazi, H. R. (2018). 3D model construction of induced polarization and resistivity data with quantifying uncertainties using geostatistical methods and drilling (Case study: Madan Bozorg, Iran). Journal of Mining and Environment, 9(4), 857-872.
4
[5] Irvine, R. J., & Smith, M. J. (1990). Geophysical exploration for epithermal gold deposits. Journal of Geochemical exploration, 36(1-3), 375-412.
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[6] Allis, R. G. (1990). Geophysical anomalies over epithermal systems. Journal of Geochemical Exploration, 36(1-3), 339- 374.
6
[7] Bakkali, S. (2006). A resistivity survey of phosphate deposits containing hardpan pockets in Oulad Abdoun, Morocco. Geofísica internacional, 45(1), 73-82.
7
[8] Locke, C. A., Johnson, S. A., Cassidy, J., & Mauk, J. L. (1999). Geophysical exploration of the Puhipuhi epithermal area, Northland, New Zealand. Journal of Geochemical Exploration, 65(2), 91-109.
8
[9] Moreira, C. A., Rezende Borges, M., Vieira, L., Matheus, G., Malagutti Filho, W., & Fernándes Montanheiro, M. A. (2014). Geological and geophysical data integration for delimitation of mineralized areas in a supergene manganese deposits. Geofísica internacional, 53(2), 199-210.
9
[10] Vieira, L. B., Moreira, C. A., Côrtes, A. R., & Luvizotto, G. L. (2016). Geophysical modeling of the manganese deposit for Induced Polarization method in Itapira (Brazil). Geofísica internacional, 55(2), 107-117.
10
[11] Pelton, W. H., Ward, S. H., Hallof, P. G., Sill, W. R., & Nelson, P. H. (1978). Mineral discrimination and removal of inductive coupling with multifrequency IP. Geophysics, 43(3), 588-609.
11
[12] Vanhala, H., & Peltoniemi, M. (1992). Spectral IP studies of Finnish ore prospects. Geophysics, 57(12), 1545-1555.
12
[13] Thoman, M. W., Zonge, K. L., & Liu, D. (1998). Geophysical case history of North Silver Bell, Pima County, Arizona—a supergene-enriched porphyry copper deposit. Northwest Mining Association, 42.
13
[14] John, D. A., Ayuso, R. A., Barton, M. D., Blakely, R. J., Bodnar, R. J., Dilles, J. H., ... & Seal, R. R. (2010). Porphyry copper deposit model, chap. B of Mineral deposit models for resource assessment. US Geological Survey Scientific Investigations Report, 2010, 1-169.
14
[15] Sinclair, W. D. (2007). Porphyry deposits. Mineral deposits of Canada: A synthesis of major deposit-types, district metallogeny, the evolution of geological provinces, and exploration methods: Geological Association of Canada, Mineral Deposits Division, Special Publication, 5, 223-243.
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[16] Brant, A. A. (1966). Geophysics in the exploration for Arizona porphyry coppers. Geology of the porphyry copper deposits: southwestern North America. University of Arizona Press, Tucson, 87-110.
16
[17] Niederleithinger, E. (2015). 3G-Geophysical Methods Delivering Input to Geostatistical Methods for Geotechnical Site Characterization.
17
[18] Tang, H. (2005). Geostatistical integration of geophysical well bore and outcrop data for flow modeling of a deltaic reservoir analogue.
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[19] Pendrel, J. V. (2013). Integrating geologic and geophysical data in geostatistical inversion: GeoConvention 2013: Integration.
19
[20] Bourges, M., Mari, J. L., & Jeannée, N. (2012). A practical review of geostatistical processing applied to geophysical data: methods and applications. Geophysical Prospecting, 60(3), 400-412. 160 S. Salarian et al. / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 153-165
20
[21] Ramazi, H., & Jalali, M. (2015). The contribution of geophysical inversion theory and geostatistical simulation to determine geoelectrical anomalies. Studia Geophysica et Geodaetica, 59(1), 97-112.
21
[22] Asghari, O., Sheikhmohammadi, S., Abedi, M., & Norouzi, G. H. (2016). Multivariate geostatistics based on a model of geoelectrical properties for copper grade estimation: a case study in Seridune, Iran. Bollettino di Geofisica Teorica ed Applicata, 57(1).
22
[23] Berberian, F., & Berberian, M. (1981). Tectono‐plutonic episodes in Iran. Zagros Hindu Kush Himalaya Geodynamic Evolution, 3, 5-32.
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[24] StScklin, J. (1968). Structural history and tectonics of Iran. AAPG Bull, 52(7), 1229-58.
24
[25] Nouri, F., Azizi, H., Stern, R. J., Asahara, Y., Khodaparast, S., Madanipour, S., & Yamamoto, K. (2018). Zircon U-Pb dating, geochemistry, and evolution of the Late Eocene Saveh magmatic complex, central Iran: Partial melts of subcontinental lithospheric mantle and magmatic differentiation. Lithos, 314, 274-292.
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[26] Berberian, M., & King, G. C. P. (1981). Towards a paleogeography and tectonic evolution of Iran. Canadian journal of earth sciences, 18(2), 210-265.
26
[27] Rezaei, S., Lotfi, M., Afzal, P., Jafari, M. R., Meigoony, M. S., & Khalajmasoumi, M. (2015). Investigation of copper and gold prospects using index overlay integration method and multifractal modeling in Saveh 1: 100,000 sheet, Central Iran. Gospodarka Surowcami Mineralnymi, 31(4), 51-74.
27
[28] Shahabpour, J. (2005). Tectonic evolution of the orogenic belt in the region located between Kerman and Neyriz. Journal of Asian Earth Sciences, 24(4), 405-417.
28
[29] Kazemi, K., Kananian, A., Xiao, Y., & Sarjoughian, F. (2018). Petrogenesis of Middle-Eocene granitoids and their Mafic microgranular enclaves in central Urmia-Dokhtar Magmatic Arc (Iran): Evidence for interaction between felsic and mafic magmas. Geoscience Frontiers.
29
[30] Richards, J. P., Wilkinson, D., & Ullrich, T. (2006). Geology of the Sari Gunay epithermal gold deposit, northwest Iran. Economic Geology, 101(8), 1455-1496.
30
[31] Asl, H. A., Mehrabi, B., & Fazel, E. T. (2017). Mineralogy, occurrence of mineralization and temperature-pressure conditions of the Agh-Daragh polymetallic deposit in the Ahar-Arasbaran metallogenic area. Journal of Economic Geology, 9(9), 1-23.
31
[32] Mehrabi, E., Masoudi, F., Jamali, H., Asgharzadeh, H. (2013). Petrography, alteration, and mineralization of Agh-Daragh region. 17th Conference of Iranian Geological Society. Tehran, Iran. (Published in Persian).
32
[33] Kazem Alilou, S. (2015). Application of Fuzzy decision making approach in 2D mineral potential mapping and its comparison with 3D magnetic geophysical presentation in Ghalandar Zone, West Azerbaijan province of Iran. MSc. Thesis in University of Tehran, 1113 p. (Published in Persian).
33
[34] Kazem Alilou, S., Abedi, M., Norouzi, GH., Dowlati, F. (2013). Application of magnetometry, special resistivity and induced polarization for exploration of iron and copper skarn deposits, a case study: Ghalandar, Ahar. 1st national conference of exploration engineering, University of Shahroud (Published in Persian).
34
[35] Loke, M. H. (2004). Tutorial: 2-D and 3-D electrical imaging surveys.
35
[36] Mostafaie, K., Ramazi, H., & Jalali, M. (2014). Application of Integrated Geophysical and Geostatistical Methods in Amiriyeh Site Classification. Geodynamics Research International Bulletin (GRIB), 2(2), 1-15.
36
[37] Deutsch, C. V. (2010). Display of cross validation/jackknife results. Centre for Computational Geostatistics Annual Report, 12(406), 1-4.
37
[38] Ertunç, G., Tercan, A. E., Hindistan, M. A., Ünver, B., Ünal, S., Atalay, F., & Kıllıoğlu, S. Y. (2013). Geostatistical estimation of coal quality variables by using covariance matching constrained kriging. International Journal of Coal Geology, 112, 14-25.
38
ORIGINAL_ARTICLE
Effect of solid impurity on creep behavior of salt rocks of the Hormoz formation
Salt rocks have one of the most complex behaviors among different rock types due to their creep behavior. Creep in rocks can cause a lot of undesired displacenments imposing tremendeous rehabilitation and maintenance costs to the projects. Creep depends on many factors such as rock type, stress level, and boundary conditions in order for rock to move freely. Amongst intrinsic factors in the rock type, the impurity of salt samples (either in gas, liquid, and/or solid form) is one of the least studied factors. This study aims to present the influence of impurity on the creep behavior of the salt rocks of the Hormuz series, as the case study. This series is one of the oldest evaporitic deposits in the world resulted in more than 350 salt domes in Iran and other parts of the Middle East. Unfortunately, there has been no comprehensive rock mechanics study on the Hormuz salt rocks so far. In this study, a few recovered cores were obtained and prepared from the exploration boreholes drilled in this formation, and the creep parameters were determined using laboratory tests. Also, the effect of impurity percentage on the creep properties of the Hormuz salt rocks was investigated. Since in salt rock masses the purity percentage is different, impurity affects the creep behavior. The tested samples were categorized into seven different groups, based on the quantity of the impurity, which consists mainly of anhydrite and quartzite. Laboratory tests showed that the uniaxial compressive and tensile strength values increase by increasing the solid impurity in the samples. In contrast, the maximum and instantaneous strains reduce by increasing the percentage of impurities in different stages of the creep test. Increasing the amount of impurity in pure samples led to increasing Burger's parameters. Also, it was observed that obtaining creep parameters from laboratory test results with mathematical approximation method had fewer errors compared to the manual method explained by Goodman. This is worth for the development of underground mining operations in salt structures. Accurate recognition of creep properties might have a considerable impact on the design as well.In the present study, the effect of impurity percentage is investigated on the creep properties of Hormuz salt rock. Because in salt rock masses, purity percentage is different, impurity and its amounts affects creep behavior. The tested samples were categoried into seven different groups based on the quantity of the impurity (which consists mainly of anhydrite and quartzite). Laboratory tests showed that the uniaxial compressive strength and tensile strength increase by increasing solid impurity in the samples. In contrast, the maximum strain and instantaneous strain reduce by increasing the percentage of impurities in different stages of creep test. Increasing amount of impurity in pure sample led to increasing Burger's parameters. Also it was observed that obtaining creep parameters from laboratory test results with mathematical approximation method has less error than the manual method explained by Goodman. This might be worth noticing because for development of underground mining operations in relation with salt structures, accurate recognition of creep properties might have considerable impact on the design.
https://ijmge.ut.ac.ir/article_76652_0b5428f7d4b0693051327ca1493e158a.pdf
2020-12-01
161
166
10.22059/ijmge.2019.276823.594787
Creep
Hormuz series
Impurity
Laboratory tests
Salt rock
Farhad
Abedi
farhad_abedi@ut.ac.ir
1
The University of Tehran
AUTHOR
Mahdi
Moosavi
mmoosavi@ut.ac.ir
2
Associated Prof at The University of Tehran
LEAD_AUTHOR
Abbas
Bahroudi
bahroudi@ut.ac.ir
3
Assistant professor of Geology in university of Tehran
AUTHOR
Alireza
Moazenian
malireza@aut.ac.ir
4
The University of Amirkabir
AUTHOR
[1] Talbot, C. and M. Alavi, The past of a future syntaxis across the Zagros. Geological Society, London, Special Publications, 1996. 100(1): p. 89-109.
1
[2] Bahroudi, A. and H.A. Koyi, Tectono-sedimentary framework of the Gachsaran Formation in the Zagros foreland basin. Marine and Petroleum Geology, 2004. 21(10): p. 1295-1310.
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[3] Hudson, J., et al., Comprehensive Rock Engineering: Principles. Practice Projects, 1993. 4: p. 715-30.
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[4] Jeremic, M.L., Rock mechanics in salt mining. 1994: CRC Press.
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[5] Urai, J.L., et al., Weakening of rock salt by water during longterm creep. 1986. [6] Berest, P., et al., Very slow creep tests on rock samples. Proc. Mechanical Behavior of Salt, 2012. 7: p. 81-88.
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[7] Zhang, H., et al., Study on tri-axial creep experiment and constitutive relation of different rock salt. Safety Science, 2012. 50(4): p. 801-805.
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[8] Hunsche, U. Measurement of creep in rock salt at small strain rates. in The Mechanical Behavior of Salt Proceedings of the Second Conference. 1984. Clausthal: Trans. Tech. Publications.
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[9] Özsen, H., İ. Özkan, and C. Sensögüt, Measurement and mathematical modelling of the creep behaviour of Tuzköy rock salt. International journal of rock mechanics and mining sciences, 2014. 66: p. 128-135.
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[10] Moosavi, M., M. Jafari, and F.S. Rassouli. Investigation on creep behavior of salt rock under high temperature with impression technique. in ISRM International Symposium on Rock MechanicsSinorock 2009. 2009. International Society for Rock Mechanics.
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[11] Li, Y. and C. Xia, Time-dependent tests on intact rocks in uniaxial compression. International Journal of Rock Mechanics and Mining Sciences, 2000. 37(3): p. 467-475.
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[12] Yang, S.-Q. and L. Cheng, Non-stationary and nonlinear viscoelastic shear creep model for shale. International Journal of Rock Mechanics and Mining Sciences, 2011. 48(6): p. 1011-1020.
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[13] Lama, R. and V. Vutukuri, Handbook on mechanical properties of rocks-testing techniques and results. Vol. 2. Vol. 3. 1978.
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[14] Zhang, X. and C. Spiers, Compaction of granular calcite by pressure solution at room temperature and effects of pore fluid chemistry. International Journal of Rock Mechanics and Mining Sciences, 2005. 42(7): p. 950-960.
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[15] Graiss, G. and M. Mahmoud, Effect of cyclic stress reduction on the creep behaviour of Al‐Ag and Al‐Ag‐Zr alloys containing γ′ and γ precipitates. Crystal Research and Technology, 2000. 35(1): p. 95-100.
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[16] Wang, T., et al., Safety evaluation of salt cavern gas storage close to an old cavern. International Journal of Rock Mechanics and Mining Sciences, 2016. 83: p. 95-106.
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[17] Talbot, C., et al., Salt Extrusion at Kuh-e-Jahani. 1997, Iran.
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[18] Liang, W.-g., et al., Experimental investigation of mechanical properties of bedded salt rock. International Journal of Rock Mechanics and Mining Sciences, 2007. 44(3): p. 400-411.
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[19] Arad, S., et al., Stability study for a large cavern in salt rock from Slanic Prahova. Acta Physica Polonica B, 2010. 41(7): p. 1789- 1802.
18
[20] Gorjian, M., et al. Temperature effect on static and dynamic properties of salt rock case study: Gachsaran evaporitic formation, Iran. in ISRM Regional Symposium-7th Asian Rock Mechanics Symposium. 2012. International Society for Rock Mechanics.
19
[21] Hansen, F.D., K.D. Mellegard, and P.E. Senseny. Elasticity and strength of ten natural rock salts. in Proc First Conf. on the Mechanical Behavior of Salt. 1984.
20
[22] Goodman, R.E., Introduction to rock mechanics. Vol. 2. 1989: Wiley New York.
21
ORIGINAL_ARTICLE
A nonlinear model to estimate vibration frequencies in surface mines
Twenty measured blast data from the Golegohar iron mine (southern Iran) were used to generalize nonlinear models for the estimation of dominant frequencies of blast waves using rock mass, explosive characteristics, and blast design. The imperialist Competitive Algorithm (ICA) was used to determine the nonlinear regression model coefficients. Possessing a good correlation coefficient, the proposed model can be directly used for predicting blast-induced dominant frequencies of waves. The determination coefficient (R2) found by the ACI-based nonlinear model was 0.98 for frequency, while that of the traditional Multivariate Linear Regression Model (MVLRM) was 0.89. Also, according to the calculation of other well-known statistical errors between the estimated and real measured values of frequency, ICA-based models have higher Variance Account for (VAF) value, as well as lower values of Route Mean Square Error (RMSE), Variance Absolute Relative Error (VARE), Median Absolute Error (MEDAE), and Mean absolute percentage error (MAPE)compared to the linear model. It was found that the proposed nonlinear model is more accurate and capable of estimating the values of the dominant frequency of blast waves.
https://ijmge.ut.ac.ir/article_76682_955c066122bcc4aecc010a9c8aa4482a.pdf
2020-12-01
167
171
10.22059/ijmge.2019.276445.594785
Ground vibration
frequency
Nonlinear model
Imperialist competitive algorithm
Mojtaba
Mokhtarian Asl
m.mokhtarian@uut.ac.ir
1
Mining engineering department, Urmia university of Technology.
LEAD_AUTHOR
Aref
Alipour
a.alipour@mie.uut.ac.ir
2
Faculty of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia
AUTHOR
[1] Hossaini, S. and Sen G. (2004). Effect of explosive type on particle velocity criteria in ground vibration. Journal of Explosives Engineering, 21(4), 34-36.
1
[2] Khandelwal, M. and Singh T.N. (2006). Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach. Journal of Sound and Vibration, 289(4), 711-725.
2
[3] Bakhshandeh Amnieh, H., Siamaki A., and Soltani S. (2012). Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach. Safety Science, 50(9), 1913-1916.
3
[4] Khandelwal, M. and Singh T.N. (2009). Prediction of blastinduced ground vibration using artificial neural network. International Journal of Rock Mechanics and Mining Sciences, 46(7), 1214-1222.
4
[5] Q. Yuan, L. Wu, Qingjun Zuo, and Li B. (2014). Peak particle velocity and principal frequency prediction based on RS-FNN comprehension method for blasting vibration. Electronic Journal of Geotechnical Engineering, 19, 10043-10056.
5
[6] Singh, T. and Singh V. (2005). An intelligent approach to prediction and control ground vibration in mines. Geotechnical & Geological Engineering, 23(3), 249-262.
6
[7] Mines, U.S.B.o. and Siskind D. (1980). Structure response and damage produced by ground vibration from surface mine blasting. US Department of the Interior, Bureau of Mines New York.
7
[8] Singh, T., Singh A., and Singh C. (1994). Prediction of ground vibration induced by blasting. Indian Min Eng J, 31-34(33), 16.
8
[9] Singh, T. (2004). Artificial neural network approach for prediction and control of ground vibrations in mines. Mining Technology, 113(4), 251-256.
9
[10] Berta, G. (1994). Blasting-induced vibration in tunnelling. Tunnelling and Underground Space Technology, 9(2), 175-187.
10
[11] Adhikari, G. and Singh R. (1989). Structural response to ground vibration from blasting in opencast coal mines. Journal of Mines, Metals & Fuels, 37(4), 135-138.
11
[12] Singh, T.N. and Verma A.K. (2010). Sensitivity of total charge and maximum charge per delay on ground vibration. Geomatics, Natural Hazards and Risk, 1(3), 259-272.
12
[13] Saeidi, O., Torabi S.R., Ataei M., and Rostami J. (2014). A stochastic penetration rate model for rotary drilling in surface mines. International Journal of Rock Mechanics and Mining Sciences, 68, 55-65.
13
[14] Kamkar-Rouhani, A. and Hojat A.). Determination of groundwater and geological factors using geoelectrical methods to design a suitable drainage system in Gol-e-Gohar iron ore M. Mokhtarian-Asl & A. Alipour / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 167-171 171 mine, Iran.
14
[15] Singh, D. and Sastry V. (1986). Rock fragmentation by blasting influence of joint filling material. Journal of Explosive Engineering, 18-27.
15
[16] Khabbazi, A., Atashpaz-Gargari E., and Lucas C. (2009). Imperialist competitive algorithm for minimum bit error rate beamforming. International Journal of Bio-Inspired Computation, 1(1-2), 125-133.
16
[17] Atashpaz-Gargari, E. and Lucas C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. in Evolutionary computation, 2007. CEC 2007. IEEE Congress on. IEEE, 4661-4667.
17
[18] Shokrollahpour, E., Zandieh M., and Dorri B. (2011). A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem. International Journal of Production Research, 49(11), 3087-3103.
18
[19] Sadaei, H.J., Enayatifar R., Lee M.H., and Mahmud M. (2016). A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting. Applied Soft Computing, 40, 132-149.
19
[20] Sharifi, M.A. and Mojallali H. (2015). A modified imperialist competitive algorithm for digital IIR filter design. Optik - International Journal for Light and Electron Optics, 126(21), 2979-2984.
20
[21] Ardalan, Z., Karimi S., Poursabzi O., and Naderi B. (2015). A novel imperialist competitive algorithm for generalized traveling salesman problems. Applied Soft Computing, 26, 546-555.
21
[22] Maroufmashat, A., Sayedin F., and Khavas S.S. (2014). An imperialist competitive algorithm approach for multi-objective optimization of direct coupling photovoltaic-electrolyzer systems. International Journal of Hydrogen Energy, 39(33), 18743-18757.
22
[23] Nazari-Shirkouhi, S., Eivazy H., Ghodsi R., Rezaie K., and Atashpaz-Gargari E. (2010). Solving the integrated product mixoutsourcing problem using the Imperialist Competitive Algorithm. Expert Systems with Applications, 37(12), 7615-7626.
23
[24] Mokhtarian Asl, M. and Sattarvand J. (2016). An imperialist competitive algorithm for solving the production scheduling problem in open pit mine. Int. Journal of Mining & GeoEngineering, 50(1), 131-143.
24
[25] Behnamian, J. and Zandieh M. (2011). A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties. Expert Systems with Applications, 38(12), 14490-14498.
25
[26] Lian, K., Zhang C., Gao L., and Shao X. (2012). A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem. International Journal of Production Research, 50(18), 5117-5131.
26
[27] Mortazavi, A., Khamseh A.A., and Naderi B. (2015). A novel chaotic imperialist competitive algorithm for production and air transportation scheduling problems. Neural Computing and Applications, 26(7), 1709-1723.
27
ORIGINAL_ARTICLE
Optimization of horizontal drain dimensions in homogeneous earth dams using neural network
Designing and optimizing the dimensions of drainage systems is very important for keeping the downstream shells dry and preventing the increase of pore water pressure in the body of earth dams. By optimizing the drainage dimensions, the minimum factor of safety, and consequently the construction costs, can be reduced. The purpose of this research was to optimize the size of horizontal drainage that is affected by some important parameters of the dam. In this study, a homogeneous earth dam was modeled using the Geostudio software. The minimum factor of safety was obtained by changing drainage dimensions, materials, and the slope of the dam body. A two-layer neural network was used to predict the least factor of safety resulted from different scenarios created in the software. By training the neural network based on the data obtained from homogeneous dams, the minimum factor of safety for drainage optimization was extracted. For optimal, an Mfile was fitted to the trained neural network function, by which the optimal values of the dam parameters were calculated.The results showed that the optimum values of drainage dimensions obtained for homogeneous dams for three heights of 10, 20, and 30 m could be generalized to other heights between 10 and 30 m with a simple interpolation.
https://ijmge.ut.ac.ir/article_76683_c6883c474c72769d7bbed060c2b9491e.pdf
2020-12-01
173
177
10.22059/ijmge.2019.278411.594792
Horizontal drainage
Homogeneous dam
Optimization
Minimum factor of Safety
Mehdi
Komasi
komasi@abru.ac.ir
1
Assistant Professor,Hydraulic Stucture ph.D.,Department of Civil Engineering, Faculty of Engineering,University of Ayatollah ozma Borujerdi,Borujerd,Iran,Komasi@abru.ac.ir.
AUTHOR
Ali
Mohammadzadeh
besh1371@chmail.ir
2
M.Sc. Graduate, Department of Civil Engineering, Faculty of Engineering,University of Ayatollah ozma Borujerdi,Borujerd,Iran
AUTHOR
Behrang
Beiranvand
behrang220@gmail.com
3
M.Sc. Graduate, Department of Civil Engineering, Faculty of Engineering,University of Ayatollah ozma Borujerdi,Borujerd,Iran
LEAD_AUTHOR
[1] Tesarik, D.R and Kealy, C.D. (2005). Estimation horizontal drain design by the Finite-Difference method, International journal of mine water, 3:1-19
1
[2] Xu, Y.Q., Unami, K., and Kawachi, T. (2002). Optimal hydraulic design of the earth dam cross-section using saturatedunsaturated seepage flow model, Elsevier, Advances in Water Resource, 26: 1-7
2
[3] Chahar Bhagu, R. (2004). Determination of the length of the horizontal drain in homogeneous earth dams, Journal of irrigation and drainage engineering, 130:530-536
3
[4] Mishra, G.C., and Singh, A.K. (2005). Seepage through a levee. International Journal of Geomechanics, 74: 1532-3641
4
[5] Alonso, E., and Pinyol, N. (2009). Slope stability under rapid drawdown conditions. The report, published by Universitat Politècnica de Catalunya, Barcelona.
5
[6] Najafpour, N., Shayannezhad, M., and Samadi, H. (2014). Investigation of the pattern of the leakage lines and the design of paw drainage in homogeneous earth dams on impermeable soil using the PLAXIS physical and software model, Journal of Soil and Water (Agricultural Sciences and Technology). 28: 461-451.
6
[7] Malekpour, A. Farsadizadeh, D. Hosseinzadeh Delir, A. and Sadr Karimi, J. (2012). The effect of the horizontal drain on the stability of homogeneous earth dam under rapid discharge conditions, Journal of Water and Soil Science. 22:139-152
7
[8] Lowe, J., and Karafiath, L. (1980). Effect of anisotropic consolidation of the undrained shear strength of compacted clays, Proc Research Conference on Shear Strength of Cohesive Soils, 1-2 Feb, Boulder, Colorado. 237-258
8
[9] Baker, R., Rydman, S., and Talesnick, M. (1993). Slope stability analysis for undrained loading conditions. Int J NUM and Anal Methods Geomech, 17: 14-43
9
[10] US Army Corps of Engineers, (2003). Engineering and design manual slope stability, Engineer Manual EM 1110-2-1902, Department of the Army Corps of Engineers, Washington DC.
10
[11] Svano, G., and Nordal, S. (1987). Undrained effective stability analysis. Proc of the 9th European Conf on Soil Mech and Found Eng, 31 Aug-3 Sep. Dublin.
11
[12] Wright, S.G., and Duncan, J.M. (1987). An examination of slope stability computation procedures for sudden drawdown, Report GL-87-25. US Army Corps Engineers, Waterway Experiment Station.
12
[13] Lane, P.A., and Griffiths, D.V. (2000). Assessment of stability of slopes under drawdown conditions. J Geotech and Geoenv Eng, 126: 443-450
13
[14] Berilgen, M.M. (2007). Investigation of stability of slopes under drawdown conditions. J Computers and Geotech, 34: 81-91
14
[15] Duncan, J.M., and Wright, S.G. (2005). Soil Strength and Slope Stability. John Wiley & Sons, Inc., Hoboken, New Jersey
15
[16] Zomoradiyan, M.A, Abdollahzadeh, M, (2012). The Effect of Horizontal Drainages on the Upper Slope Sustainability of Earth dams during the drawdown of the Reservoir, Journal of Civil and Environmental Engineering, 42:29-35
16
[17] Bahrehbor, A. R, Bouzari. A and Bahrehbor. F. (2017). Laboratory study of the effect of the type of drainage system in the amount of leakage from the body and the homogeneous earth dam, 6th Environmental, Energy and Biological Conservation Conference, Tehran, Institute of Higher Education Arvand Stamp, Center for Sustainable Development
17
[18] Kalantari, B., and Nazeri, F. (2016). Effect of material quality on the stability of embankment, EJGE Journal, 21: 5061-5071
18
[19] Yazdanian, M., Afshoon, H.R., Ghasemi, S., Afshoon, V., and Fahim, F. (2017). Effect of height on the static stability of heterogeneous embankment dams, Journal of Engineering Science and Technology (ESTEC), 5:274-282
19
[20] Fattah, M.Y., Omran, H.A., and Hassan, M.A. (2017). Flow and stability of Al-wand dam during the rapid drawdown of water in the reservoir, Acta Montanistica Slovaca, 22: 43-57
20
[21] Darabi, M., and Maleki, M. (2017). Effect of drainage geometry on the dynamic response of homogeneous earth dams, Journal of Civil and Environmental Engineering, 48:99-108.
21
ORIGINAL_ARTICLE
Numerical Investigation of the Impact of Geomechanical Parameters of Formations on Well Integrity of One of the Iranian Oil Fields
Well integrity is defined as the application of technical and operational solutions to reduce the uncontrollable risk of fluids leakage in the well lifetime. In any drilling and production operation, lack of knowledge about geomechanical behavior of the surrounding formations is considered as a major risk. Therefore, in-situ stress conditions and mechanical properties of formations are important factors in well integrity studies. In this paper, a 3D finite element model was built to simulate the integrity of wells. An FEM analysis was used to investigate the plastic deformation in cement and theVon Mises failure criterion inside the casings under different stress conditions, and to study the mechanical properties of the formation. A clear increase in plastic strain in the cement and Von Mises stress inside the casings was observed with increasing the ratio of horizontal to vertical stress in orthotropic and isotropic conditions as well as with increasing the difference between horizontal stresses in anisotropic conditions. When conducting the translation error sensitivity analysis, the impact of major mechanical parameters of the formation was evaluated as well. The results showed thatby increasing Young's modulus, cement became hard and brittle. Meanwhile, an increase in the Poisson ratio led to plastic behavior.The maximum plastic strain was found at the cement-casing boundary due to the presence of a lower cement-formation friction value. The highest Von Mises stress value in the casings was also produced parallel toward the minimum horizontal stress.Additionally, with an increase in the cohesion and friction angleof formation, the cement became harder, and consequently, the safety factor for the casings increased.
https://ijmge.ut.ac.ir/article_76770_825820f227f07e6854959e4a195e77ce.pdf
2020-12-01
179
183
10.22059/ijmge.2019.278501.594793
Well integrity
Geomechanical Parameters
Numerical Method
plastic strain
Eissa
Khodami
essakhodami@gmail.com
1
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Ahmad
Ramezanzadeh
aramezanzadeh@gmail.com
2
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Mehdi
Noroozi
mnoroozi.mine@gmail.com
3
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Mohammad
Mehrad
manemangaly@gmail.com
4
Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
[1] N. Standard. (2004). Well integrity in drilling and well operations, D-010, rev, vol. 3.
1
[2] D. U. Etetim. (2013). Well Integrity behind casing during well operation. Alternative sealing materials to cement, Institutt for petroleumsteknologi og anvendt geofysikk.
2
[3] Sangesland, S., Rausand, M., Torbergsen, H., Haga, H., Aadnøy, B., Sæby, J., Johnsen, S., Lundeteigen, M. (2012). An Introduction to well integrity, Rev 0.
3
[4] N. C. Himmelberg. (2014). Numerical simulations for wellbore stability and integrity for drilling and completions. Missouri University of Science and Technology.
4
[5] M. Shahri, J. J. Schubert, and M. Amani. (2005) Detecting and modeling cement failure in high-pressure/high-temperature (HP/HT) wells, using finite element method (FEM), in International Petroleum Technology Conference.
5
[6] C. Topini, F. Bertolo, G. Capasso, and S. Mantica. (2011). Buckling analysis for the long-term integrity evaluation of a hydrocarbon well, in Proc. Simulia Customer Conference, Barcelona, Spain.
6
[7] Li, Y., Yuan, J., Qi, F., Liu, S., Wang, Z., & Qu, C. (2012, January). Analysis of cemented casing mechanical failure under arbitrary in-situ stress field coupling effects of downhole pressure and temperature. In IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition. Society of Petroleum Engineers.
7
[8] Feng, Y., Podnos, E., & Gray, K. E. (2016, June). Well integrity analysis: 3D numerical modeling of cement interface debonding. In 50th US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association.
8
[9] Z. Shen and F. E. Beck. (2012). Three-dimensional modeling of casing and cement sheath behavior in layered, nonhomogeneous formations, in IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition.
9
[10] M. G. Haider, J. Sanjayan, and P. G. Ranjith. (2012). Modeling of a well-bore composite cylinder system for cement sheath stress analysis in geological sequestration of CO2, in 46th US Rock Mechanics/Geomechanics Symposium.
10
[11] S. Abaqus. (2017). Abaqus Version 2017 Analysis User’s Guide. [12] National Iranian South Oil Company, geological report, 2009. [13] G. Capasso and G. Musso. (2010). Evaluation of Stress and Strain Induced by the Rock Compaction on a Hydrocarbon Well Completion Using Contact Interfaces with Abaqus,” in Proc. ABAQUS Users’ Conference, Providence RI, USA.
11
[14] H. Arias. (2013). Use of finite-element analysis to improve well cementing in HTHP conditions. Texas A&M University.
12
[15] J. Fan and B. Yue. (1997). Analysis of surface loading on casing and cement sheath under nonuniform geologic stress, JOURNAL-UNIVERSITY Pet. CHINA Nat. Sci. Ed., vol. 21, pp. 46–48.
13
[16] F. Jun, Z. Huaiwen, Y. Boqian, and S. Yufeng. (1995). Analysis of Loading Propekty of Casing and Cement Sheath Under Nonuniform Geologic Stress [J], J. Univ. Pet. China, vol. 6.
14
[17] L. Jun, C. Mian, L. Gonghui, and Z. Hui. (2005). Elastic-plastic analysis of casing-concrete sheath-rock combination, Acta Pet. Sin., vol. 26, no. 6, pp. 99–103.
15
[18] J. Li, M. Chen, H. Zhang, and S.-L. Zi. (2005). Effects of cement sheath elastic modulus on casing external collapse load, Shiyou Daxue Xueban Ziran Kexue Ban(Journal Univ. Pet. China Nat. Sci. Ed., vol. 29, no. 6, pp. 41–44.
16