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| 1 | Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks显示文摘Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks. | DEHGHAN S SATTARI Gh CHEHREH CHELGANI S ALIABADI M A | 2010 | Mining Science and Technology2010，20，1： | 18 |
| 2 | Modeling of fine coal flotation separation based on particle characteristics and hydrodynamic conditions显示文摘 | B. Shahbazi S. Chehreh Chelgani | 2016 | International Journal of Coal Science & Technology2016，3，4： | 12 |
| 3 | Explaining surface interactions for common associated gangues of rare earth minerals in response to the oxalic acid显示文摘In the flotation of rare earth minerals(REMs), oxalic acid is reportedly acting both as a depressant and p H modifier. Although results of testing have established the significance of oxalic acid in the flotation process, its specific role in either the recovery or selectivity of REMs over their common gangue minerals is not well understood. Pulp p H reduction trials with alternative acids have not shown the same effect on the REMs recovery or the depression of gangue phases. This work studies the effect of oxalic acid on the surface of common REMs gangue minerals(quartz and carbonates(dolomite and calcite)) in a series of conditioning tests. Gangue surface analyses by time of flight secondary ion mass spectroscopy(TOFSIMS) indicate that oxalic acid inhibits the transfer of secondary ions generated during the conditioning process from one mineral to another. In this regard, the oxalate anion acts to fix ions in solution through chelation, limiting their participation in surface adsorption. | Saeed Chehreh Chelgani Brian Hart | 2018 | International Journal of Mining Science and Technology2018，28，2： | 6 |
| 4 | Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network显示文摘An artificial neural network and regression procedures were used to predict the recovery and collision probability of quartz flotation concentrate in different operational conditions. Flotation parameters, such as dimensionless numbers (Froude, Reynolds, and Weber), particle size, air flow rate, bubble diameter, and bubble rise velocity, were used as inputs to both methods. The linear regression method shows that the relationships between flotation parameters and the recovery and collision probability of flotation can achieve correlation coefficients (R2) of 0.54 and 0.87, respectively. A feed-forward artificial neural network with 3-3-3-2 arrangement is able to simultaneously estimate the recovery and collision probability as the outputs. In testing stages, the quite satisfactory correlation coefficient of 0.98 was achieved for both outputs. It shows that the proposed neural network models can be used to determine the most advantageous operational conditions for the expected recovery and collision probability in the froth flotation process. | Saeed Chehreh Chelgani Behzad Shahbazi Bahram Rezai | 2010 | International Journal of Minerals,Metallurgy and Materials2010，17，5： | 5 |
| 5 | Modeling and process optimization for microbial desulfurization of coal by using a two-level full factorial design显示文摘The microbial sulfur removal was investigated on high sulfur content (1.9%) coal concentrate from Tabas coal preparation plant. A mixed culture of ferrooxidans microorganisms was isolated from the tailing dam of the plant. Full factorial method was used to design laboratory test and to evaluate the effects of pH, particle size, iron sulfate concentration, pulp density, and bioleaching time on sulfur reduction. Statistical analyses of experimental data were considered and showed increases of pH and particle size had negative effects on sulfur reduction, whereas increases of pulp density and bioleaching time raised microbial desulfurization rate. According to results of designing, and regarding statistical factors, the optimum values for maximum sulfur reduction were obtained; pH (1.5), particle size (-180 μm), iron sulfate concentration (2.7 mmol/L), pulp density (10%) and bioleaching time (14 d), which leaded to 51.5% reduction from the total sulfur of sample. | Golshani T. Jorjani E. Chelgani S. Chehreh Shafaei S.Z. Nafechi Y. Heidari | 2013 | International Journal of Mining Science and Technology2013，23，2： | 5 |
| 6 | Prediction of operational parameters effect on coal flotation using artificial neural network显示文摘Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation rate,coal particle size,dosage of col-lector,frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process. | E. Jorjani Sh. Mesroghli S. Chehreh Chelgani | 2008 | Journal of University of Science and Technology Beijing2008，15，5： | 5 |
| 7 | Prediction of yttrium, lanthanum, cerium, and neodymium leaching recovery from apatite concentrate using artificial neural networks显示文摘The assay and recovery of rare earth elements (REEs) in the leaching process is being determined using expensive analytical methods: inductively coupled plasma atomic emission spectroscopy (ICP-AES) and inductively coupled plasma mass spec- troscopy (ICP-MS). A neural network model to predict the effects of operational variables on the lanthanum, cerium, yttrium, and neodymium recovery in the leaching of apatite concentrate is presented in this article. The effects of leaching time (10 to 40 min), pulp densities (30% to 50%), acid concentrations (20% to 60%), and agitation rates (100 to 200 r/min), were investigated and opti- mized on the recovery of REEs in the laboratory at a leaching temperature of 60°C. The obtained data in the laboratory optimization process were used for training and testing the neural network. The feed-forward artificial neural network with a 4-5-5-1 arrangement was capable of estimating the leaching recovery of REEs. The neural network predicted values were in good agreement with the experimental results. The correlations of R=1 in training stages, and R=0.971, 0.952, 0.985, and 0.98 in testing stages were a result of Ce, Nd, La, and Y recovery prediction respectively, and these values were usually acceptable. It was shown that the proposed neural network model accurately reproduced all the effects of the operation variables, and could be used in the simulation of a leaching plant for REEs. | E. Jorjani A.H. Bagherieh Sh. Mesroghli S. Chehreh Chelgani | 2008 | Journal of University of Science and Technology Beijing2008，15，4： | 4 |
| 8 | Recovery of coal particles from a tailing dam for environmental protection and economical beneficiations显示文摘 | M. Asghari M. Noaparast S. Z. Shafaie S. Ghassa S. Chehreh Chelgani | 2018 | International Journal of Coal Science & Technology2018，5，2： | 2 |
| 9 | Exploring relationships of gross calorific value and valuable elements with conventional coal properties for North Korean coals显示文摘Coal in North Korean(NKC)is one of the most important products;however,based on various strategic policies its detail properties remain opaque even for general researchers.Since there are some signs for opening of the North Korea economy,this investigation as a modest effort is going to explore principle relationships among some essential parameters of NKCs such as gross calorific value(GCV),valuable elements and conventional properties by different statistical methods.Correlations indicated that ultimate parameters(carbon,nitrogen,and hydrogen)are the best GCV predictors for NKCs in comparison with proximate parameters(ash,moisture and volatile matter).Multivariable regression demonstrated that predicted GCV based on ultimate properties has a quite accuracy when correlation of determination was 0.99.Descriptive statistics processes showed that on average,the contents of valuable elements such as Ga and V for NKCs are higher than the world coal ranges and they can be considered as byproducts of combustion of NKCs.Pearson correlations indicated that Y may have a mixed organic-inorganic affinity while Ga and V mainly occur in the inorganic part(mineral matter)of NKCs.High inter-correlations between Ga-V and Al showed that aluminosilicates can be considered as their main bring minerals. | Saeed Chehreh Chelgani | 2019 | International Journal of Mining Science and Technology2019，29，6： | 2 |
| 10 | Estimation of gross calorific value based on coal analysis using regression and artificial neural networks显示文摘 | Sh. Mesroghli E. Jorjani S. Chehreh Chelgani | 2009 | International Journal of Coal Geology2009，，1： | 2 |
| 11 | Explaining the relation- ship between common coal analyses and Afghan coal parameters using statistical modeling methods 显示文摘 | CHEHREH CHELGANI S MAKAREMI S | 2012 | Fuel Processing Technology2012，110，： | 1 |
| 12 | Prediction of microbial desulfurization of coal using artificial neural net works显示文摘 | Jorjani E Chehreh CS Mesroghli S | 2007 | Minerals Engineering2007，20，： | 1 |
| 13 | Alteration of delta-6-desaturase (FADS2), secretory phospholipase-A2 (sPLA2) enzymes by Hot-nature diet with co-supplemented hemp seed, evening primrose oils intervention in multiple sclerosis patients显示文摘 | Soheila Rezapour-Firouzi Seyed Rafie Arefhosseini Mehrangiz Ebrahimi-Mamaghani Behzad Baradaran Elyar Sadeghihokmabad Somaiyeh Mostafaei Mohammadali Torbati Mahtaj Chehreh | 2015 | Complementary Therapies in Medicine2015，，5： | 1 |
| 14 | Microwave irradiation pretreatment and peroxyacetic acid desulfurization of coal and application of GRNN simultaneous predictor显示文摘 | S. Chehreh Chelgani E. Jorjani | 2011 | Fuel2011，，11： | 1 |
| 15 | Prediction of microbial desulfurization of coal using artificial neural networks显示文摘 | Jorjani E Chehreh CS Mesroghli Sh | | 0，，14： | 1 |
| 16 | Explaining the relationship between common coal analyses and Afghan coal parameters using statistical modeling methods显示文摘 | CHEHREH CHELGANI S MAKAREMI S | 2012 | Fuel Processing Technology2012，110，： | 1 |
| 17 | Application of artificial neural networks to predict chemical desulfurization of Tabas coal显示文摘 | Jorjani E Chehreh CS Mesroghli Sh | | 0，，12： | 1 |
| 18 | Estimation of gross calorific value based on coal analysis using regression and artificial neural networks显示文摘 | MESROGHLI Sh JO1LIANI E CHEHREH CHELGANI S | 2009 | International Joumal ofCoalGeology2009，79，： | 1 |
| 19 | Rare EarthElements Leaching from Chadormalu ApatiteConcentrate: Laboratory Studies and RegressionPredictions 显示文摘 | Joi^ani E Bagherieh A H Chehreh C S | 2011 | Korean Journal of ChemicalEngineering2011，28，2： | 1 |
| 20 | Explaining the relationship between common coal analyses and Afghan coal parameters using statistical modeling methods显示文摘 | CHEHREH C S MAKAREMI S | 2013 | Fuel Processing Technology2013，110，6： | 1 |
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