The Nd(III)extraction in flat renewal supported liquid membrane(FRSLM),with polyvinylidene fluoride membrane and renewal solution including HNO3 solution as the stripping solution and di(2-ethylhexyl)phosphoric acid(D...The Nd(III)extraction in flat renewal supported liquid membrane(FRSLM),with polyvinylidene fluoride membrane and renewal solution including HNO3 solution as the stripping solution and di(2-ethylhexyl)phosphoric acid(D2EHPA)dissolved in kerosene as the membrane solution,was investigated.The effects of pH in the feed phase,volume ratio of membrane solution to stripping solution,concentra-tion of HNO3 solution and concentration of carrier in the renewal phase on extraction of Nd(III)were also studied,respectively.As a result,the optimum extraction conditions of Nd(III)were obtained when concentration of HNO3 solution was 4.00 mol/L,concentration of D2EHPA was 0.100 mol/L,and volume ratio of membrane solution to stripping solution was 1.00 in the renewal phase,and pH was 4.60 in the feed phase.When initial concentration of Nd(III)was 2.00×10-4 mol/L,the extraction percentage of Nd(III)was up to 92.9%in 75 min.展开更多
To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, q...To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to over- come the defects live in the existent recognition methods based on fuzzy, neural network and support vector regres- sion （SVR） theory, a novel flatness pattern recognition method based on least squares support vector regression （LS-SVR） was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhan- cing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out （LOO） error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LDSVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability.展开更多
基金Project supported by the National Natural Science Foundation of China for Young Scientists(51109197)Foundation for Planning Project of West Action of Chinese Academy of Sciences(KZCX2-XB2-13)Research Fund for Excellent Doctoral Thesis of Xi'an University of Technology(602-210805)
文摘The Nd(III)extraction in flat renewal supported liquid membrane(FRSLM),with polyvinylidene fluoride membrane and renewal solution including HNO3 solution as the stripping solution and di(2-ethylhexyl)phosphoric acid(D2EHPA)dissolved in kerosene as the membrane solution,was investigated.The effects of pH in the feed phase,volume ratio of membrane solution to stripping solution,concentra-tion of HNO3 solution and concentration of carrier in the renewal phase on extraction of Nd(III)were also studied,respectively.As a result,the optimum extraction conditions of Nd(III)were obtained when concentration of HNO3 solution was 4.00 mol/L,concentration of D2EHPA was 0.100 mol/L,and volume ratio of membrane solution to stripping solution was 1.00 in the renewal phase,and pH was 4.60 in the feed phase.When initial concentration of Nd(III)was 2.00×10-4 mol/L,the extraction percentage of Nd(III)was up to 92.9%in 75 min.
基金Sponsored by National Natural Science Foundation of China (50675186)
文摘To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to over- come the defects live in the existent recognition methods based on fuzzy, neural network and support vector regres- sion （SVR） theory, a novel flatness pattern recognition method based on least squares support vector regression （LS-SVR） was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhan- cing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out （LOO） error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LDSVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability.
