The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-...The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-ZnSO_(4) solution,employing seed-induced nucleation methods.Analysis of the iron removal rate,residue structure,morphology,and elemental composition involved ICP,XRD,FT-IR,and SEM.The existing state of zinc was investigated by combining step-by-step dissolution using hydrochloric acid.Concurrently,iron removal tests were extended to industrial solutions to assess the influence of seeds and solution pH on zinc loss and residue yield.The results revealed that seed addition increased the iron removal rate by 3%,elevated the residual iron content by 6.39%,and mitigated zinc loss by 29.55%in the simulated solution.Seed-induced nucleation prevented excessive nuclei formation,fostering crystal stable growth and high crystallinity.In addition,the zinc content of surface adsorption and crystal internal embedding in the residue was determined,and the zinc distribution on the surface was dense.In contrast,the total amount of zinc within the crystal was higher.The test results in the industrial solution demonstrated that the introduction of seeds expanded the pH range for goethite formation and growth,and the zinc loss per ton of iron removed was reduced by 50.91 kg(34.12%)and the iron residue reduced by 0.17 t(8.72%).展开更多
1 Introduction Recently the demand for fossil fuel has grown significantly with the rapid development of the Chinese economy.Renewable energy was developed to replace traditional fossil fuels,which would decrease the
A calcified roasting-acid leaching process was developed as a highly effective method for the extraction of valuable metals from low nickel matte in the presence of CaO additive. The influences of process parameters o...A calcified roasting-acid leaching process was developed as a highly effective method for the extraction of valuable metals from low nickel matte in the presence of CaO additive. The influences of process parameters on the metal extraction were studied, including the roasting temperature, roasting time, addition of CaO, H2SO4 concentration and liquid-solid ratio. Under the optimum condition, 94.2% of Ni, 98.1% of Cu, 92.2% of Co and 89.3% of Fe were recovered. Additionally, 99.6% of Fe was removed from the leachate as goethite by a subsequent goethite iron precipitation process. The behavior and mechanism of CaO additive in the roasting process was clarified. The role of CaO is to prevent the formation of nonferrous metal ferrite phases by a preferential reaction with Fe2O3 during the roasting process. The metal oxides(Cu O and NixCu1-xO) remained stable during high-temperature roasting and were subsequently efficiently leached using a sulfuric acid solution.展开更多
Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them co...Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.展开更多
基金Project(2018YFC1900403) supported by the National Key Research and Development Program of ChinaProject(CX20210197) supported by the Postgraduate Scientific Research Innovation Project of Hunan Province,China+1 种基金Project(202206370103) supported by the China Scholarship CouncilProject(2021zzts0115) supported by the Fundamental Research Funds for the Central Universities,China。
文摘The goethite residue generated from zinc hydrometallurgy is classified as hazardous solid waste,produced in large quantities,and results in significant zinc loss.The study was conducted on removing iron from FeSO_(4)-ZnSO_(4) solution,employing seed-induced nucleation methods.Analysis of the iron removal rate,residue structure,morphology,and elemental composition involved ICP,XRD,FT-IR,and SEM.The existing state of zinc was investigated by combining step-by-step dissolution using hydrochloric acid.Concurrently,iron removal tests were extended to industrial solutions to assess the influence of seeds and solution pH on zinc loss and residue yield.The results revealed that seed addition increased the iron removal rate by 3%,elevated the residual iron content by 6.39%,and mitigated zinc loss by 29.55%in the simulated solution.Seed-induced nucleation prevented excessive nuclei formation,fostering crystal stable growth and high crystallinity.In addition,the zinc content of surface adsorption and crystal internal embedding in the residue was determined,and the zinc distribution on the surface was dense.In contrast,the total amount of zinc within the crystal was higher.The test results in the industrial solution demonstrated that the introduction of seeds expanded the pH range for goethite formation and growth,and the zinc loss per ton of iron removed was reduced by 50.91 kg(34.12%)and the iron residue reduced by 0.17 t(8.72%).
基金National Natural Science Foundation of China (41572326) for the support of this study
文摘1 Introduction Recently the demand for fossil fuel has grown significantly with the rapid development of the Chinese economy.Renewable energy was developed to replace traditional fossil fuels,which would decrease the
基金Projects(U1860203,U1860108,51574164) supported by the National Natural Science Foundation of China
文摘A calcified roasting-acid leaching process was developed as a highly effective method for the extraction of valuable metals from low nickel matte in the presence of CaO additive. The influences of process parameters on the metal extraction were studied, including the roasting temperature, roasting time, addition of CaO, H2SO4 concentration and liquid-solid ratio. Under the optimum condition, 94.2% of Ni, 98.1% of Cu, 92.2% of Co and 89.3% of Fe were recovered. Additionally, 99.6% of Fe was removed from the leachate as goethite by a subsequent goethite iron precipitation process. The behavior and mechanism of CaO additive in the roasting process was clarified. The role of CaO is to prevent the formation of nonferrous metal ferrite phases by a preferential reaction with Fe2O3 during the roasting process. The metal oxides(Cu O and NixCu1-xO) remained stable during high-temperature roasting and were subsequently efficiently leached using a sulfuric acid solution.
基金supported in part by the Program of the National Natural Science Foundation of China under Grant No.61673399in part by the Program of National Natural Science Foundation of Hunan Province under Grant No.2017JJ2329in part by Fundamental Research Funds for Central Universities of Central South University under Grant No.2018zzts550。
文摘Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.
