Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,...Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.展开更多
The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid ...The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.展开更多
The local time dependence of the geomagnetic disturbances during magnetic storms indicates the necessity of forecasting the localized magnetic storm indices.For the first time,we construct prediction models for the Su...The local time dependence of the geomagnetic disturbances during magnetic storms indicates the necessity of forecasting the localized magnetic storm indices.For the first time,we construct prediction models for the SuperMAG partial ring current indices(SMR-LT),with the advance time increasing from 1 h to 12 h by Long Short-Term Memory(LSTM)neural network.Generally,the prediction performance decreases with the advance time and is better for the SMR-06 index than for the SMR-00,SMR-12,and SMR-18 index.For the predictions with 12 h ahead,the correlation coefficient is 0.738,0.608,0.665,and 0.613,respectively.To avoid the over-represented effect of massive data during geomagnetic quiet periods,only the data during magnetic storms are used to train and test our models,and the improvement in prediction metrics increases with the advance time.For example,for predicting the storm-time SMR-06 index with 12 h ahead,the correlation coefficient and the prediction efficiency increases from 0.674 to 0.691,and from 0.349 to 0.455,respectively.The evaluation of the model performance for forecasting the storm intensity shows that the relative error for intense storms is usually less than the relative error for moderate storms.展开更多
南水入冀新水情下,百泉泉域地下水环境发生改变,岩溶地下水地球化学过程有待查明。综合利用数值模拟、机器学习(自组织聚类)和同位素(δD和δ^(18)O)等方法系统揭示了矿业活动与南水入冀下百泉泉域岩溶地下水地球化学过程,并基于熵变权...南水入冀新水情下,百泉泉域地下水环境发生改变,岩溶地下水地球化学过程有待查明。综合利用数值模拟、机器学习(自组织聚类)和同位素(δD和δ^(18)O)等方法系统揭示了矿业活动与南水入冀下百泉泉域岩溶地下水地球化学过程,并基于熵变权水质指数(Entropy-weighted water quality index,EWQI)进行了水质分级评价。南水入冀后,百泉泉域岩溶地下水位整体抬升。时间上,呈现出既有年际动态变化又有年内季节变化特征。空间上,补给区表现为剧变型,而径流、排泄区呈缓变型。地下水降落漏斗主要分布在泉域东南部的煤、铁矿密集区。泉域岩溶地下水呈弱碱性,水化学类型以Ca-HCO_(3)型和Ca-SO_(4)型为主导。主要阴阳离子质量浓度遵循ρ(HCO_(3)^(-))>ρ(SO_(4)^(2-))>ρ(Cl^(-))和ρ(Ca^(2+))>ρ(Mg^(2+))>ρ(Na^(+))>ρ(K^(+))的顺序。各离子沿着径流路径呈现出逐渐增大的空间分布特征。岩溶地下水化学成分主要受岩石(方解石、白云石和石膏)风化溶解和反向阳离子交换作用主导。人为活动对泉域岩溶地下水系统中的SO_(4)^(2-)和NO_(3)^(-)质量浓度有一定程度影响。岩溶地下水来源于大气降水,并且在入渗前发生了二次蒸发作用,氘盈余值在径流过程中有所降低。水质评价结果表明,岩溶地下水质量整体优于第四系地下水,分别有50%的岩溶水和37.5%的第四系水样满足饮用目的。TDS、ρ(SO_(4)^(2-))和ρ(NO_(3)^(-))是影响泉域地下水水质的关键指标。引起泉域岩溶地下水系统水质恶化的潜在人类活动主要包括矿山排水、农业灌溉和城市污水排放。通过水质分级评价,提出了泉域地下水环境保护措施。研究结果将有助于为百泉泉域岩溶地下水资源的供水安全和地下水环境保护治理提供参考。展开更多
There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill l...There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters.展开更多
基金Project(202006430012)supported by the China Scholarship Council。
文摘Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.
文摘The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.
基金Supported by National Natural Science Foundation of China grants(42022032,41874203,42188101)project of Civil Aerospace"13 th Five Year Plan"Preliminary Research in Space Science(D020301,D030202),Strategic Priority Research Program of CAS(XDA17010301)+1 种基金Key Research Program of Frontier Sciences CAS(QYZDJ-SSW-JSC028)International Partner-National Program of CAS(183311KYSB20200017)。
文摘The local time dependence of the geomagnetic disturbances during magnetic storms indicates the necessity of forecasting the localized magnetic storm indices.For the first time,we construct prediction models for the SuperMAG partial ring current indices(SMR-LT),with the advance time increasing from 1 h to 12 h by Long Short-Term Memory(LSTM)neural network.Generally,the prediction performance decreases with the advance time and is better for the SMR-06 index than for the SMR-00,SMR-12,and SMR-18 index.For the predictions with 12 h ahead,the correlation coefficient is 0.738,0.608,0.665,and 0.613,respectively.To avoid the over-represented effect of massive data during geomagnetic quiet periods,only the data during magnetic storms are used to train and test our models,and the improvement in prediction metrics increases with the advance time.For example,for predicting the storm-time SMR-06 index with 12 h ahead,the correlation coefficient and the prediction efficiency increases from 0.674 to 0.691,and from 0.349 to 0.455,respectively.The evaluation of the model performance for forecasting the storm intensity shows that the relative error for intense storms is usually less than the relative error for moderate storms.
文摘南水入冀新水情下,百泉泉域地下水环境发生改变,岩溶地下水地球化学过程有待查明。综合利用数值模拟、机器学习(自组织聚类)和同位素(δD和δ^(18)O)等方法系统揭示了矿业活动与南水入冀下百泉泉域岩溶地下水地球化学过程,并基于熵变权水质指数(Entropy-weighted water quality index,EWQI)进行了水质分级评价。南水入冀后,百泉泉域岩溶地下水位整体抬升。时间上,呈现出既有年际动态变化又有年内季节变化特征。空间上,补给区表现为剧变型,而径流、排泄区呈缓变型。地下水降落漏斗主要分布在泉域东南部的煤、铁矿密集区。泉域岩溶地下水呈弱碱性,水化学类型以Ca-HCO_(3)型和Ca-SO_(4)型为主导。主要阴阳离子质量浓度遵循ρ(HCO_(3)^(-))>ρ(SO_(4)^(2-))>ρ(Cl^(-))和ρ(Ca^(2+))>ρ(Mg^(2+))>ρ(Na^(+))>ρ(K^(+))的顺序。各离子沿着径流路径呈现出逐渐增大的空间分布特征。岩溶地下水化学成分主要受岩石(方解石、白云石和石膏)风化溶解和反向阳离子交换作用主导。人为活动对泉域岩溶地下水系统中的SO_(4)^(2-)和NO_(3)^(-)质量浓度有一定程度影响。岩溶地下水来源于大气降水,并且在入渗前发生了二次蒸发作用,氘盈余值在径流过程中有所降低。水质评价结果表明,岩溶地下水质量整体优于第四系地下水,分别有50%的岩溶水和37.5%的第四系水样满足饮用目的。TDS、ρ(SO_(4)^(2-))和ρ(NO_(3)^(-))是影响泉域地下水水质的关键指标。引起泉域岩溶地下水系统水质恶化的潜在人类活动主要包括矿山排水、农业灌溉和城市污水排放。通过水质分级评价,提出了泉域地下水环境保护措施。研究结果将有助于为百泉泉域岩溶地下水资源的供水安全和地下水环境保护治理提供参考。
文摘There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters.
