According to the features of melting process of regenerative aluminum melting furnaces, a three-dimensional mathematical model with user-developed melting model, burner reversing and burning capacity model was establi...According to the features of melting process of regenerative aluminum melting furnaces, a three-dimensional mathematical model with user-developed melting model, burner reversing and burning capacity model was established. The numerical simulation of melting process of a regenerative aluminum melting furnace was presented using hybrid programming method of FLUENT UDF and FLUENT scheme based on the heat balance test. Burner effects on melting process of aluminum melting furnaces were investigated by taking optimization regulations into account. The change rules of melting time on influence factors are achieved. Melting time decreases with swirl number, vertical angle of burner, air preheated temperature or natural gas flow; melting time firstly decreases with horizontal angle between burners or air-fuel ratio, then increases; melting time increases with the height of burner.展开更多
Estimation of support pressure is extremely important to the support system design and the construction safety of tunnels.At present,there are many methods for the estimation of support pressure based on different roc...Estimation of support pressure is extremely important to the support system design and the construction safety of tunnels.At present,there are many methods for the estimation of support pressure based on different rock mass classification systems,such as Q system,GSI system and RMR system.However,various rock mass classification systems are based on different tunnel geologic conditions in various regions.Therefore,each rock mass classification system has a certain regionality.In China,the BQ-Inex(BQ system)has been widely used in the field of rock engineering ever since its development.Unfortunately,there is still no estimation method of support pressure with BQ-index as parameters.Based on the field test data from 54 tunnels in China,a new empirical method considering BQ-Inex,tunnel span and rock weight is proposed to estimate the support pressure using multiple nonlinear regression analysis methods.And then the significance and necessity of support pressure estimation method for the safety of tunnel construction in China is explained through the comparison and analysis with the existing internationally widely used support pressure estimation methods of RMR system,Q system and GSI system.Finally,the empirical method of estimating the support pressure based on BQ-index was applied to designing the support system in the China’s high-speed railway tunnel—Zhengwan high-speed railway and the rationality of this method has been verified through the data of field test.展开更多
Global warming is recently an urgent issue worldwide. The increase of carbon emissions induced by human economic activi- ties has become a major driving force behind global climate change. Thus, as a matter of social ...Global warming is recently an urgent issue worldwide. The increase of carbon emissions induced by human economic activi- ties has become a major driving force behind global climate change. Thus, as a matter of social responsibility, reasonable carbon con- straints should be implemented to ensure environmental security and sustainable development for every country. Based on a summary of studies that examined the relationship between carbon emissions and regional development, this paper shows that human activity-led carbon emission is caused by the combination of several influencing factors, including population size, income level, and technical pro- gress. Thus, a quantitative model derived from IPAT-ImPACT-Kaya series and STIRPAT models was established. Empirical analysis using multivariate nonlinear regression demonstrated that the origins of growing global carbon emission included the increasing influ- encing elasticity of the population size and the declining negative effect of technical progress. Meanwhile, in context of classification of country groups at different income levels, according to the comparison of fluctuating patterns of the influencing elasticity, technical progress was found as the main factor influencing carbon emission levels in high-income countries, and population size might he the controlling factor in middle-income countries. However, for low-income countries, the nonlinear relationship between carbon emission and its influencing factors was not significant, whereas population growth was identified as an important potential driving force in future carbon emissions. This study can therefore provide a reference for the formulation of policies on carbon constraints, especially to de- velop more efficient carbon mitigating policies for countries at different income levels.展开更多
Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.T...Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.展开更多
Evaluation of grade and recovery plays an important role in process control and plant profitability in mineral processing operations, especially flotation. The accurate measurement or estimation of these two parameter...Evaluation of grade and recovery plays an important role in process control and plant profitability in mineral processing operations, especially flotation. The accurate measurement or estimation of these two parameters, based on the secondary variables, is a critical issue. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, two types of artificial neural networks(ANNs),namely radial basis function neural network(RBFNN) and layer recurrent neural network(RNN), and also a multivariate nonlinear regression(MNLR) model were employed to predict metallurgical performance of the flotation column. The training capacity and the accuracy of these three above mentioned types of models were compared. In order to acquire data for the simulation, a case study was conducted at Sarcheshmeh copper complex pilot plant. Based on the root mean squared error and correlation coefficient values, at training and testing stages, the RNN forecasted the metallurgical performance of the flotation column better than RBF and MNLR models. The RNN could predict Cu grade and recovery with correlation coefficients of 0.92 and 0.9, respectively in testing process.展开更多
基金Project(2009bsxt022)supported by the Dissertation Innovation Foundation of Central South University,ChinaProject(07JJ4016)supported by Natural Science Foundation of Hunan Province,ChinaProject(U0937604)supported by the National Natural Science Foundation of China
文摘According to the features of melting process of regenerative aluminum melting furnaces, a three-dimensional mathematical model with user-developed melting model, burner reversing and burning capacity model was established. The numerical simulation of melting process of a regenerative aluminum melting furnace was presented using hybrid programming method of FLUENT UDF and FLUENT scheme based on the heat balance test. Burner effects on melting process of aluminum melting furnaces were investigated by taking optimization regulations into account. The change rules of melting time on influence factors are achieved. Melting time decreases with swirl number, vertical angle of burner, air preheated temperature or natural gas flow; melting time firstly decreases with horizontal angle between burners or air-fuel ratio, then increases; melting time increases with the height of burner.
基金Projects(51878567,51878568,51578458)supported by the National Natural Science Foundation of ChinaProjects(2017G007-F,2017G007-H)supported by China Railway Science and Technology Research and Development Plan。
文摘Estimation of support pressure is extremely important to the support system design and the construction safety of tunnels.At present,there are many methods for the estimation of support pressure based on different rock mass classification systems,such as Q system,GSI system and RMR system.However,various rock mass classification systems are based on different tunnel geologic conditions in various regions.Therefore,each rock mass classification system has a certain regionality.In China,the BQ-Inex(BQ system)has been widely used in the field of rock engineering ever since its development.Unfortunately,there is still no estimation method of support pressure with BQ-index as parameters.Based on the field test data from 54 tunnels in China,a new empirical method considering BQ-Inex,tunnel span and rock weight is proposed to estimate the support pressure using multiple nonlinear regression analysis methods.And then the significance and necessity of support pressure estimation method for the safety of tunnel construction in China is explained through the comparison and analysis with the existing internationally widely used support pressure estimation methods of RMR system,Q system and GSI system.Finally,the empirical method of estimating the support pressure based on BQ-index was applied to designing the support system in the China’s high-speed railway tunnel—Zhengwan high-speed railway and the rationality of this method has been verified through the data of field test.
基金Under the auspices of Major State Basic Research Development Program of China(No.2012CB955802)National Natural Science Foundation of China(No.41171099)Strategy of Public Participation of Low Carbon Development in China(No.201315)
文摘Global warming is recently an urgent issue worldwide. The increase of carbon emissions induced by human economic activi- ties has become a major driving force behind global climate change. Thus, as a matter of social responsibility, reasonable carbon con- straints should be implemented to ensure environmental security and sustainable development for every country. Based on a summary of studies that examined the relationship between carbon emissions and regional development, this paper shows that human activity-led carbon emission is caused by the combination of several influencing factors, including population size, income level, and technical pro- gress. Thus, a quantitative model derived from IPAT-ImPACT-Kaya series and STIRPAT models was established. Empirical analysis using multivariate nonlinear regression demonstrated that the origins of growing global carbon emission included the increasing influ- encing elasticity of the population size and the declining negative effect of technical progress. Meanwhile, in context of classification of country groups at different income levels, according to the comparison of fluctuating patterns of the influencing elasticity, technical progress was found as the main factor influencing carbon emission levels in high-income countries, and population size might he the controlling factor in middle-income countries. However, for low-income countries, the nonlinear relationship between carbon emission and its influencing factors was not significant, whereas population growth was identified as an important potential driving force in future carbon emissions. This study can therefore provide a reference for the formulation of policies on carbon constraints, especially to de- velop more efficient carbon mitigating policies for countries at different income levels.
基金Projects(61001188,1161140319)supported by the National Natural Science Foundation of ChinaProject(2012ZX03001034)supported by the National Science and Technology Major ProjectProject(YETP1202)supported by Beijing Higher Education Young Elite Teacher Project,China
文摘Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.
基金the support of the Department of Research and Development of Sarcheshmeh Copper Plants for this research
文摘Evaluation of grade and recovery plays an important role in process control and plant profitability in mineral processing operations, especially flotation. The accurate measurement or estimation of these two parameters, based on the secondary variables, is a critical issue. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, two types of artificial neural networks(ANNs),namely radial basis function neural network(RBFNN) and layer recurrent neural network(RNN), and also a multivariate nonlinear regression(MNLR) model were employed to predict metallurgical performance of the flotation column. The training capacity and the accuracy of these three above mentioned types of models were compared. In order to acquire data for the simulation, a case study was conducted at Sarcheshmeh copper complex pilot plant. Based on the root mean squared error and correlation coefficient values, at training and testing stages, the RNN forecasted the metallurgical performance of the flotation column better than RBF and MNLR models. The RNN could predict Cu grade and recovery with correlation coefficients of 0.92 and 0.9, respectively in testing process.
