The application of the response surface methodology and the central composite design（CCD） technique for modeling and optimization of the influence of some operating variables on copper,molybdenum and rhenium recover...The application of the response surface methodology and the central composite design（CCD） technique for modeling and optimization of the influence of some operating variables on copper,molybdenum and rhenium recoveries in a bioleaching process was investigated.Three main bioleaching parameters,namely pH,solid concentration and inoculum percent,were changed during the bioleaching tests based on CCD.The ranges of the bioleaching process variables used in the design were as follows：pH1.46-2.14,solid concentration 0.95%-11.05%,and inoculum percent 1.59%-18.41%.A total of 20 bioleaching tests were carried out by the CCD method according to software-based designed matrix.Empirical model equations were developed according to the copper,molybdenum and rhenium recoveries obtained with these three parameters.Model equations of responses at the base of parameters were achieved by using statistical software.The model equations were then individually optimized by using quadratic programming to maximize copper,molybdenum and rhenium recoveries individually within the experimental range.The optimum conditions for copper recovery were pH 1.68,solid concentration 0.95% and the inoculum 18.41%（v/v）,while molybdenum and rhenium recoveries were 2.18% and 24.41%,respectively.The predicted values for copper,molybdenum and rhenium recoveries were found to be in good agreement with the experimental values.Also jarosite formation during bioleaching tests was also investigated.展开更多
Traditional reliability analysis requires probability distributions of all the uncertain parameters.However,in many practical applications,the variation bounds can be only determined for the parameters with limited in...Traditional reliability analysis requires probability distributions of all the uncertain parameters.However,in many practical applications,the variation bounds can be only determined for the parameters with limited information.A complex hybrid reliability problem then will be caused when the random and interval variables coexist in a same structure.In this paper,by introducing the response surface technique,we develop a new hybrid reliability method to efficiently compute the interval of the failure probability of the structure due to the probability-interval hybrid uncertainty.The present method consists of a sequence of iterations.At each step,a response surface model is constructed for the limit-state function by using a quadratic polynomial and a modified axial experimental design method.An approximate hybrid reliability problem is created based on the response surface model,which is subsequently solved by an efficient decoupling approach.An updating strategy is suggested to improve the quality of the response surface and whereby ensure the reliability analysis precision.A computational procedure is then summarized for the whole iterations.Four numerical examples and also a practical application are provided to demonstrate the effectiveness of the present method.展开更多
基金supported by the National Iranian Copper Industry Co. and Geological Survey of Iran
文摘The application of the response surface methodology and the central composite design（CCD） technique for modeling and optimization of the influence of some operating variables on copper,molybdenum and rhenium recoveries in a bioleaching process was investigated.Three main bioleaching parameters,namely pH,solid concentration and inoculum percent,were changed during the bioleaching tests based on CCD.The ranges of the bioleaching process variables used in the design were as follows：pH1.46-2.14,solid concentration 0.95%-11.05%,and inoculum percent 1.59%-18.41%.A total of 20 bioleaching tests were carried out by the CCD method according to software-based designed matrix.Empirical model equations were developed according to the copper,molybdenum and rhenium recoveries obtained with these three parameters.Model equations of responses at the base of parameters were achieved by using statistical software.The model equations were then individually optimized by using quadratic programming to maximize copper,molybdenum and rhenium recoveries individually within the experimental range.The optimum conditions for copper recovery were pH 1.68,solid concentration 0.95% and the inoculum 18.41%（v/v）,while molybdenum and rhenium recoveries were 2.18% and 24.41%,respectively.The predicted values for copper,molybdenum and rhenium recoveries were found to be in good agreement with the experimental values.Also jarosite formation during bioleaching tests was also investigated.
基金supported by the National Science Foundation for Excellent Young Scholars(Grant No.51222502)the Key Project of Chinese National Programs for Fundamental Research and Development(Grant No.2010CB832700)+1 种基金the National Natural Science Foundation of China(Grant No.11172096)the Key Program of the National Natural Science Foundation of China(Grant No.11232004)
文摘Traditional reliability analysis requires probability distributions of all the uncertain parameters.However,in many practical applications,the variation bounds can be only determined for the parameters with limited information.A complex hybrid reliability problem then will be caused when the random and interval variables coexist in a same structure.In this paper,by introducing the response surface technique,we develop a new hybrid reliability method to efficiently compute the interval of the failure probability of the structure due to the probability-interval hybrid uncertainty.The present method consists of a sequence of iterations.At each step,a response surface model is constructed for the limit-state function by using a quadratic polynomial and a modified axial experimental design method.An approximate hybrid reliability problem is created based on the response surface model,which is subsequently solved by an efficient decoupling approach.An updating strategy is suggested to improve the quality of the response surface and whereby ensure the reliability analysis precision.A computational procedure is then summarized for the whole iterations.Four numerical examples and also a practical application are provided to demonstrate the effectiveness of the present method.
