The aim of this work is to investigate and optimize the effects of the leaching parameters on the selective leaching of zinc from electric arc furnace steelmaking dust （EAFD）. The response surface method was applied...The aim of this work is to investigate and optimize the effects of the leaching parameters on the selective leaching of zinc from electric arc furnace steelmaking dust （EAFD）. The response surface method was applied on the basis of a three-level Box–Behnken experimental design method for optimization of selective leaching parameters of zinc from EAFD. The leaching recoveries of zinc （YZn） and iron （YFe） were taken as the response variables, where the concentration of sulphuric acid （X1, mol/L）, leaching temperature （X2, °C）, leaching time （X3, min）, and liquid/solid ratio （X4, mL/g） were considered as the independent variables （factors）. The mathematical model was proposed. Statistical ANOVA analysis and confirmation tests were applied. A maximum of 79.09% of zinc was recovered while the minimum iron recovery was 4.08% under the optimum conditions of leaching time 56.42 min, H2SO4 concentration 2.35 mol/L, leaching temperature 25 °C and liquid/solid ratios. By using ANOVA, the most influential factors on leaching of zinc and iron were determined as H2SO4 concentration and leaching temperature, respectively. The proposed model equations using response surface methodology show good agreement with the experimental data, with correlation coefficients （R2） of 0.98 for zinc recovery and 0.97 for iron recovery.展开更多
An adaptive chaotic gradient descending optimization algorithm for single objective optimization was presented. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method....An adaptive chaotic gradient descending optimization algorithm for single objective optimization was presented. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method. A new optimal minimum was obtained to replace the local minimum by mutative-scale chaotic search algorithm whose scales are magnified gradually from a small scale in order to escape local minima. The global optimal value was attained by repeatedly iterating. At last, a BP (back-propagation) neural network model for forecasting slag output in matte converting was established. The algorithm was used to train the weights of the BP neural network model. The simulation results with a training data set of 400 samples show that the training process can be finished within 300 steps to obtain the global optimal value, and escape local minima effectively. An optimization system for operation parameters, which includes the forecasting model, is achieved, in which the output of converter increases by 6.0%, and the amount of the treated cool materials rises by 7.8% in the matte converting process.展开更多
Laboratory-scale experiments were performed to investigate the deoxidation of H13 tool steel with CaF_(2)-MgO-Al_(2)O_(3)-CaO-SiO_(2) slags at 1873 K.The calculation of thermodynamics and kinetics was also verified th...Laboratory-scale experiments were performed to investigate the deoxidation of H13 tool steel with CaF_(2)-MgO-Al_(2)O_(3)-CaO-SiO_(2) slags at 1873 K.The calculation of thermodynamics and kinetics was also verified through the experimental results.The results show that[Si]-[O]reaction is the control reaction,and with the increase of basicity of slag,the limitation of deoxidation was decreased.The limitation of deoxidation is the lowest for the slag with basicity of 2.0.Under the conditions of the basicity of 2.0 and the content of CaF_(2) more than 50%,the limitation of deoxidation is less than 10×10^(−6),and it does not depend on the contents of Al_(2)O_(3) and CaF_(2) in slags.The mass transport of oxygen in the metal phase is the rate-controlling step,and the slag composition has no effect on the equilibrium time of deoxidation.Based on this finding,the optimized slag composition is designed and it contains the following components:51.5%CaF_(2),20.3%MgO,16.2%Al_(2)O_(3),8.2%CaO and 3.8%SiO_(2).In the case of the optimized deoxidizing slag,the total oxygen content in H13 steel can be reduced from 25×10^(−6) to 6×10^(−6).展开更多
The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and t...The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.展开更多
文摘The aim of this work is to investigate and optimize the effects of the leaching parameters on the selective leaching of zinc from electric arc furnace steelmaking dust （EAFD）. The response surface method was applied on the basis of a three-level Box–Behnken experimental design method for optimization of selective leaching parameters of zinc from EAFD. The leaching recoveries of zinc （YZn） and iron （YFe） were taken as the response variables, where the concentration of sulphuric acid （X1, mol/L）, leaching temperature （X2, °C）, leaching time （X3, min）, and liquid/solid ratio （X4, mL/g） were considered as the independent variables （factors）. The mathematical model was proposed. Statistical ANOVA analysis and confirmation tests were applied. A maximum of 79.09% of zinc was recovered while the minimum iron recovery was 4.08% under the optimum conditions of leaching time 56.42 min, H2SO4 concentration 2.35 mol/L, leaching temperature 25 °C and liquid/solid ratios. By using ANOVA, the most influential factors on leaching of zinc and iron were determined as H2SO4 concentration and leaching temperature, respectively. The proposed model equations using response surface methodology show good agreement with the experimental data, with correlation coefficients （R2） of 0.98 for zinc recovery and 0.97 for iron recovery.
文摘An adaptive chaotic gradient descending optimization algorithm for single objective optimization was presented. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method. A new optimal minimum was obtained to replace the local minimum by mutative-scale chaotic search algorithm whose scales are magnified gradually from a small scale in order to escape local minima. The global optimal value was attained by repeatedly iterating. At last, a BP (back-propagation) neural network model for forecasting slag output in matte converting was established. The algorithm was used to train the weights of the BP neural network model. The simulation results with a training data set of 400 samples show that the training process can be finished within 300 steps to obtain the global optimal value, and escape local minima effectively. An optimization system for operation parameters, which includes the forecasting model, is achieved, in which the output of converter increases by 6.0%, and the amount of the treated cool materials rises by 7.8% in the matte converting process.
基金Project(18SYXHZ0069)supported by the Science and Technology Program of Sichuan Province,ChinaProjects(51974139,51664021)supported by the National Natural Science Foundation of China。
文摘Laboratory-scale experiments were performed to investigate the deoxidation of H13 tool steel with CaF_(2)-MgO-Al_(2)O_(3)-CaO-SiO_(2) slags at 1873 K.The calculation of thermodynamics and kinetics was also verified through the experimental results.The results show that[Si]-[O]reaction is the control reaction,and with the increase of basicity of slag,the limitation of deoxidation was decreased.The limitation of deoxidation is the lowest for the slag with basicity of 2.0.Under the conditions of the basicity of 2.0 and the content of CaF_(2) more than 50%,the limitation of deoxidation is less than 10×10^(−6),and it does not depend on the contents of Al_(2)O_(3) and CaF_(2) in slags.The mass transport of oxygen in the metal phase is the rate-controlling step,and the slag composition has no effect on the equilibrium time of deoxidation.Based on this finding,the optimized slag composition is designed and it contains the following components:51.5%CaF_(2),20.3%MgO,16.2%Al_(2)O_(3),8.2%CaO and 3.8%SiO_(2).In the case of the optimized deoxidizing slag,the total oxygen content in H13 steel can be reduced from 25×10^(−6) to 6×10^(−6).
基金Projects(2007AA041401,2007AA04Z194) supported by the National High Technology Research and Development Program of China
文摘The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.
