Grey correlation analysis of factors influencing maldistribution in feeding device of copper flash smelting

Zhou, Panyue; Yao, Ying-jin; Yuan-fang, AI; An-ming, Liu; Xu, Zelin; Xie, Jun

doi:10.1007/s11771-012-1229-5

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…It is an index to characterize the relational degree between the comparison sequence and the reference sequence; ρ∈[0,1], is the resolution coefficient, and generally ρ = 0.5. 52 , 53 and are the minimum difference between two levels and the maximum difference between two levels, respectively. (5) Since each coal sample in eq 5 has a relational coefficient, the information is scattered and cannot be directly compared, it is necessary to calculate the grey relational degree where r i is the relational degree between the influencing factors and the contact angle.…”

Section: Appendix 11 Grey Relational Analysis Modelmentioning

confidence: 99%

Experimental Study on the Wettability of Coal with Different Metamorphism Treated by Surfactants for Coal Dust Control

Liao

Wang

et al. 2021

ACS Omega

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Wet dedusting is the main coal dust suppression technique in coal mines, and coal wettability is the main factor affecting dust suppression efficiency. To investigate the main factors affecting the coal wettability and improve it, the coal–water contact angle was used as an index to characterize the coal wettability, and the wettability of six coal samples with different metamorphic degree was studied by analyzing the relationship between the physicochemical properties and the contact angle. To improve the coal wettability, the nonionic surfactant alkyl polyglycoside (APG), anionic surfactant sodium dodecyl benzene sulfonate (SDBS), and polymer surfactant polyacrylamide (PAM) were applied to the coal samples. The results show that SDBS is the most effective surfactant to improve the coal wettability, followed by APG, while the application of PAM would lead to more hydrophobic coal. It is also found that the coal wettability shows a high–low–high trend with the increase in the metamorphic degree. The wettability of long flame coal is the strongest and that of gas coal is the weakest. Moisture is the main hydrophilic factor of coal, while 1,4-dimethylbenzene is the main hydrophobic factor. The main factors affecting the treatment effect of APG, SDBS, and PAM on wettability are the aromatic methylbenzene, hydroxyl, and hydroxyl content of coal, respectively. Therefore, according to the content of hydroxyl in different coals, an SDBS solution can be prepared to improve the coal wettability. For coal with a low hydroxyl content, a higher concentration SDBS solution could be needed.

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Section: Appendix 11 Grey Relational Analysis Modelmentioning

confidence: 99%

Experimental Study on the Wettability of Coal with Different Metamorphism Treated by Surfactants for Coal Dust Control

Liao

Wang

et al. 2021

ACS Omega

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“…(5) Cu2S+2Cu2O=6Cu+SO2 ( 6) Except for reaction (6), FeO generated in other reactions would react with silica in the molten bath to make slag, as shown in reaction (7). The relationship between Gibbs free energy (G 0 ) and the temperature for the reactions (1)-( 6) are calculated and shown in Fig.…”

Section: Theoretical Assessment Of Reducing Copper Content In Converting Slagmentioning

confidence: 99%

“…More than 80% of copper (Cu) is produced by pyro-metallurgical processing world-wide [1][2][3][4][5], with the main converting unit processes including flash converting, Peirce Smith (PS) converting, bottom blowing, top blowing and other improved converting processes. The copper content in the slag is typically less than 5 wt.% in the operation of PS converters and other improved furnaces [5][6][7], while it is higher than 16 wt.% Cu in some converting processes [8][9][10]. Numerous studies on reducing copper content in the converting slags have been carried out by experts and scholars [11][12][13] with their researches mainly focused on increasing feed of matte grade, process control   Foundation item: Project (51734006) supported by the National Natural Science Foundation of China, Project (2017HB009) supported by the Middle-Aged Academic Leaders of Yunnan province and Project (2017HA006) supported by Academician free exploration fund of Yunnan province system improvements of the converting process, converting slag reduction, converting slag beneficiation and copper recovery.…”

Section: Introductionmentioning

confidence: 99%

Insight on the reduction of copper content in slags produced from the Ausmelt Converting Process

Yuan

Cai

Song

et al. 2021

J min metall B Metall

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The reduction of copper content in converting slag using process control is significant to copper smelter. In this study, the slags produced from the Ausmelt Converting Process for copper matte have been analyzed using X-ray diffraction and chemical analysis. Thermodynamic calculation and effects of various conditions including the lance submerging depth in molten bath, the molten bath temperature, the addition of copper matte, and airflow rate were carried out to lower the content in the slag. Thermodynamic analysis indicates that the decrease of copper content was achieved by reducing Fe3O4, CuFe2O4and Cu2O in the slag, decreasing the magnetism of slag and lowering the viscosity of slag, which is feasible at the operating temperature of the molten bath. Experiments show that the optimal combination of operating conditions were found to be the addition of copper matte between 5000 -7000 kg/h, a lance airflow rate of 13000-14000 Nm3/h and a lance submergence depth into the molten bath of 700-900 mm, in which the copper content in the slag can be effectively reduced from 22.74 wt. % to 7.70 wt. %.This study provides a theoretical support and technical guidance for promoting the utilization of slags from the Ausmelt Converting Process.

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“…However, the accuracy of predicted results and actual values is not fairly reliable, on account of some big defects, such as local optimization, slow convergence speed, and large dependence on training data, when a single BP neural network algorithm is used to predict the amount of fertilizer applied to trees. In this paper, the analyzing method of grey relativity [16][17][18][19] is introduced to extract the original variable data of multiple indexes in advance, and the factors with high correlation degree are taken as the network input layer, and then the global searching ability of PSO [20][21][22][23][24][25] is used to optimize the BP neural network, which greatly avoids the defect of the model falling into the local optimum and further improves the accuracy. Although neural network prediction has been applied in medical engineering, electric power, system, and other fields at home and abroad, and some gratifying results have been achieved, there are only few studies on the application of neural network prediction model to forest fertilizer application.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model of Forest Preliminary Precision Fertilization Based on Improved GRA-PSO-BP Neural Network

Chen

Dian

2020

Mathematical Problems in Engineering

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The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.

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Grey correlation analysis of factors influencing maldistribution in feeding device of copper flash smelting

Cited by 9 publications

References 11 publications

Experimental Study on the Wettability of Coal with Different Metamorphism Treated by Surfactants for Coal Dust Control

Experimental Study on the Wettability of Coal with Different Metamorphism Treated by Surfactants for Coal Dust Control

Insight on the reduction of copper content in slags produced from the Ausmelt Converting Process

A Prediction Model of Forest Preliminary Precision Fertilization Based on Improved GRA-PSO-BP Neural Network

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