Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
A series of high surface area graphitic carbon materials （HSGCs） were prepared by ball-milling method. Effect of the graphitic degree of HSGCs on the catalytic performance of Ba-Ru-K/HSGC-x （x is the ball-milling t...A series of high surface area graphitic carbon materials （HSGCs） were prepared by ball-milling method. Effect of the graphitic degree of HSGCs on the catalytic performance of Ba-Ru-K/HSGC-x （x is the ball-milling time in hour） catalysts was studied using ammonia synthesis as a probe reaction. The graphitic degree and pore structure of HSGC-x supports could be successfully tuned via the variation of ball-milling time. Ru nanoparticles of different Ba-Ru-K/HSGC-x catalysts are homogeneously distributed on the supports with the particle sizes ranging from 1.6 to 2.0 nm. The graphitic degree of the support is closely related to its facile electron transfer capability and so plays an important role in improving the intrinsic catalytic performance of Ba-Ru-K/HSGC-x catalyst.展开更多
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
基金supported by the Natural Science Foundation of China(NSFC Grant No.20803064)the Natural Science Foundation of Zhejiang Provence(Y4090348 and LY12B03007)Qianjiang Talent Project in Zhejiang Province(2010R10039 and 2013R10056)
文摘A series of high surface area graphitic carbon materials （HSGCs） were prepared by ball-milling method. Effect of the graphitic degree of HSGCs on the catalytic performance of Ba-Ru-K/HSGC-x （x is the ball-milling time in hour） catalysts was studied using ammonia synthesis as a probe reaction. The graphitic degree and pore structure of HSGC-x supports could be successfully tuned via the variation of ball-milling time. Ru nanoparticles of different Ba-Ru-K/HSGC-x catalysts are homogeneously distributed on the supports with the particle sizes ranging from 1.6 to 2.0 nm. The graphitic degree of the support is closely related to its facile electron transfer capability and so plays an important role in improving the intrinsic catalytic performance of Ba-Ru-K/HSGC-x catalyst.
