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Mining science and Technology 2010年第1期20卷 41-46页

作者： DEHGHAN s sATTARI Gh chehreh chelgani s ALIABADI M A Mining Engineering Department Mahallat Branch Islamic Azad University surface science Western University of Western Ontario

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.

关键词： uniaxial compressive strength modulus of elasticity artificial neural networks regression travertine

Exploring relationships of gross calorific value and valuable elements with conventional coal properties for North Korean coals

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International Journal of Mining science and Technology 2019年第6期29卷 867-871页

作者： saeed chehreh chelgani Minerals and Metallurgical Engineering Dept.of Civil Environmental and Natural Resources Engineering Lule? University of Technology

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 *** 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 *** 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 *** 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 *** correlations indicated that Y may have a mixed organic-inorganic affinity while Ga and V mainly occur in the inorganic part(mineral matter)of *** inter-correlations between Ga-V and Al showed that aluminosilicates can be considered as their main bring minerals.

关键词： Yttrium Gallium Vanadium Calorific value Energy By product

Explaining surface interactions for common associated gangues of rare earth minerals in response to the oxalic acid

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International Journal of Mining science and Technology 2018年第2期28卷 343-346页

作者： saeed chehreh chelgani Brian Hart Department of Electrical Engineering and Computer science University of Michigan surface science Western University of Western Ontario

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.

关键词： Oxalic acid Rare earth silicate Carbonate TOF-sIMs

Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network

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International Journal of Minerals,Metallurgy and Materials 2010年第5期17卷 526-534页

作者： saeed chehreh chelgani Behzad shahbazi Bahram Rezai surface science Western Research Park University of Western Ontario Mining Engineering Department Science and Research Branch Islamic Azad University Amirkabir University of Technology

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.

关键词： flotation recovery collision probability neural networks

Estimating REY content of eastern Kentucky coal samples based on their associated ash elements

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Journal of Rare Earths 2018年第11期36卷 1234-1238页

作者： s.chehreh chelgani James C.Hower Department of Electrical Engineering and Computer science University of Michigan Center for Applied Energy Research University of Kentucky

Coal and coal combustion byproducts can be considered as an alternative source of rare earth elements and yttrium （REY）. The study of relationships between REY and other main coal properties could have several advantages such as estimating REY content of coal particles and designing beneficial extraction method. In this investigation, inter-correlations between REY content with coal parameters （proximate and ash elements） for a wide range of eastern Kentucky coal samples （708 records） were explored. Results demonstrate that zircon and monazite are the main source of heavy and light rare earth elements （HREE and LREE）, respectively. Zr has the highest correlation with Y and Th has the strength relationship with Ce and La. In general, LREE have higher interaction with coal ash content in comparison with HREE. Results indicated that REY can be estimated quite satisfactorily by using their associated elements in coal ash.

关键词： Coal Heavy rare earth Light rare earth Proximate Ash elements

Prediction of operational parameters effect on coal flotation using artificial neural network

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Journal of University of science and Technology Beijing 2008年第5期15卷 528-533页

作者： E. Jorjani sh. Mesroghli s. chehreh chelgani Department of Mining Engineering Science and Research BranchIslamic Azad University

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 collector,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.

关键词： coal flotation operational parameters artificial neural networks combustible recovery

Modeling of fine coal flotation separation based on particle characteristics and hydrodynamic conditions

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International Journal of Coal science & Technology 2016年第4期3卷 429-439页

作者： B. shahbazi s. chehreh chelgani Department of Mining Engineering Tarbiat Modares University Tehran Iran Department of Electrical Engineering and Computer science University of Michigan Ann Arbor MI 48109 USA

Flotation is a complex multifaceted process that is widely used for the separation of finely ground minerals. The theory of froth flotation is complex and is not completely understood. This fact has been brought many monitoring challenges in a coal processing plant. To solve those challenges, it is important to understand the effect of different parameters on the fine particle separation, and control flotation performance for a particular system. This study is going to indicate the effect of various parameters （particle Characteristics and hydrodynamic conditions） on coal flotation responses （flotation rate constant and recovery） by different modeling techniques. A comprehensive coal flotation database was prepared for the statistical and soft computing methods. statistical factors were used for variable selections. Results were in a good agreement with recent theoretical flotation investigations. Computational models accurately can estimate flotation rate constant and coal recovery （correlation coefficient 0.85, and 0.99, respectively）. According to the results, it can be concluded that the soft computing models can overcome the complexity of process and be used as an expert system to control, and optimize parameters of coal flotation process.

关键词： Coal processing Flotation Modeling Particle characteristics - Hydrodynamic conditions

Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by XGBoost and sHAP-A “conscious-lab” development

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International Journal of Mining science and Technology 2021年第6期31卷 1135-1144页

作者： s.chehreh chelgani H.Nasiri M.Alidokht Minerals and Metallurgical Engineering Dept.of CivilEnvironmental and Natural Resources EngineeringLuleåUniversity of TechnologySE-97187 LuleåSweden Department of Computer Engineering Amirkabir University of TechnologyTehranIran Tabas Parvardeh Coal Company(TPCCO) BirjandIran

surprisingly,no investigation has been explored relationships between operating variables and metallurgical responses of coal column flotation(CF) circuits based on industrial databases for under operation *** a novel approach,this study implemented a conscious-lab "CL" for filling this *** this approach,for developing the CL dedicated to an industrial CF circuit,sHapley Additive explanations(sHAP) and extreme gradient boosting(XGBoost) were powerful unique machine learning systems for the first time *** explainable artificial intelligence models could effectively convert the dataset to a basis that improves human capabilities for better understanding,reasoning,and planning the *** could provide precise multivariable correlation assessments between the CF dataset by using the Tabas Parvadeh coal plant(Kerman,Iran),and showed the importance of solid percentage and washing water on the metallurgical responses of the coal CF *** could predict metallurgical responses(R-square> 0.88) based on operating variables that showed quite higher accuracy than typical modeling methods(Random Forest and support vector regression).

关键词： sHAP XGBoost Explainable AI Coal flotation separation efficiency

Prediction of yttrium, lanthanum, cerium, and neodymium leaching recovery from apatite concentrate using artificial neural networks

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Journal of University of science and Technology Beijing 2008年第4期15卷 367-374页

作者： E. Jorjani A.H. Bagherieh sh. Mesroghli s. chehreh chelgani Department of Mining Engineering Research and Science Campus Islamic Azad University Poonak Hesarak Tehran Iran

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 spectroscopy （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 optimized on the recovery of REEs in the laboratory at a leaching temperature of 60℃. 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=l 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.

关键词： apatite neural networks rare earth elements leaching recovery

Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN:a"Conscious-Lab"development

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International Journal of Coal science & Technology 2021年第6期8卷 1436-1446页

作者： Mehdi Alidokht samaneh Yazdani Esmaeil Hadavandi saeed chehreh chelgani Tabas Parvardeh Coal Company(TPCCO) BirjandIran Department of Electrical and Computer Engineering Islamic Azad UniversityNorth Tehran BranchTehranIran Department of Industrial Engineering Birjand University of TechnologyBirjandIran Minerals and Metallurgical Engineering Department of CivilEnvironmental and Natural Resources EngineeringLulea University of Technology97187 LuleaSweden

Tri-flo cyclone,as a dense-medium separation device,is one of the most typical environmentally friendly industrial techniques in the coal washery ***,no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses(yield and recovery).To fill this gap,this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method(boosted-neural network"BNN")which is able to linearly and nonlinearly assess multivariable correlations among all variables,rank them based on their effectiveness and model their produced *** assessments and modeling were considered a new concept called"Conscious Laboratory(CL)".CL can markedly decrease the number of laboratory experiments,reduce cost,save time,remove scaling up risks,expand maintaining processes,and significantly improve our knowledge about the modeled *** this study,a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo ***-known machine learning methods,random forest,and support vector regression were developed to validate BNN *** comparisons indicated the accuracy and strength of BNN over the examined traditional modeling *** a sentence,generating a novel BNN within the CL concept can apply in various energy and coal processing areas,fill gaps in our knowledge about possible interactions,and open a new window for plants’fully automotive process.

关键词： Conscious laboratory BNN Tri-flo Random forest support vector regression