By means of low-field nuclear magnetic resonance(LF-NMR),the transverse relaxation time(T_(2))signals of physically bound water in cement paste were monitored to indicate water content change and characterize the earl...By means of low-field nuclear magnetic resonance(LF-NMR),the transverse relaxation time(T_(2))signals of physically bound water in cement paste were monitored to indicate water content change and characterize the early-age hydration process.With the curves of the T_(2)signals and hydration time obtained,the hydration process could be divided into four typical periods using the null points of the second derivative curve,and the influences of water-cement ratio(w/c)and hydration heat regulating materials(HHRM)on hydration process were analyzed.The experimental results showed that the hydration rate of pure cement paste in accelerated period presented a positive correlation with w/c.Compared to pure cement paste,the addition of HHRM extended all four periods,and led to a much faster hydration rate in initial period as well as a slower rate in accelerated period.Finally,according to the LFNMR test results,the early-age hydration model of cementitious materials was proposed considering w/c and HHRM content.展开更多
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
基金Funded by National Natural Science Foundation of China(Nos.U1965105,51878245)National Key R&D Program of China(No.2021YFF0500802)。
文摘By means of low-field nuclear magnetic resonance(LF-NMR),the transverse relaxation time(T_(2))signals of physically bound water in cement paste were monitored to indicate water content change and characterize the early-age hydration process.With the curves of the T_(2)signals and hydration time obtained,the hydration process could be divided into four typical periods using the null points of the second derivative curve,and the influences of water-cement ratio(w/c)and hydration heat regulating materials(HHRM)on hydration process were analyzed.The experimental results showed that the hydration rate of pure cement paste in accelerated period presented a positive correlation with w/c.Compared to pure cement paste,the addition of HHRM extended all four periods,and led to a much faster hydration rate in initial period as well as a slower rate in accelerated period.Finally,according to the LFNMR test results,the early-age hydration model of cementitious materials was proposed considering w/c and HHRM content.
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.
