Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples acco...Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.展开更多
Estimating weak rock mass modulus has historically proven difficult although this mechanical property is an important input to many types of geotechnical analyses. An empirical database of weak rock mass modulus with ...Estimating weak rock mass modulus has historically proven difficult although this mechanical property is an important input to many types of geotechnical analyses. An empirical database of weak rock mass modulus with associated detailed geotechnical parameters was assembled from plate loading tests per- formed at underground mines in Nevada, the Bakhtiary Dam project, and Portugues Dam project. The database was used to assess the accuracy of published single-variate models and to develop a multivari- ate model for predicting in-situ weak rock mass modulus when limited geoteehnical data are available. Only two of the published models were adequate for predicting modulus of weak rock masses over lim- ited ranges of alteration intensities, and none of the models provided good estimates of modulus over a range of geotechnical properties. In light of this shortcoming, a multivariate model was developed from the weak rock mass modulus dataset, and the new model is exponential in form and has the following independent variables： （1） average block size or joint spacing, （2） field estimated rock strength, （3） dis- continuity roughness, and （4） discontinuity infilling hardness. The multivariate model provided better estimates of modulus for both hard-blocky rock masses and intensely-altered rock masses.展开更多
基金Supported by the National High Technology Research and Development Program of China (2006AA040309)National BasicResearch Program of China (2007CB714000)
文摘Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.
基金funded by the National Institute of Occupational Safety and Health through research contract 200-2011-39965(Principal Investigator Dr.Kallu)University of Nevada,Reno,NV
文摘Estimating weak rock mass modulus has historically proven difficult although this mechanical property is an important input to many types of geotechnical analyses. An empirical database of weak rock mass modulus with associated detailed geotechnical parameters was assembled from plate loading tests per- formed at underground mines in Nevada, the Bakhtiary Dam project, and Portugues Dam project. The database was used to assess the accuracy of published single-variate models and to develop a multivari- ate model for predicting in-situ weak rock mass modulus when limited geoteehnical data are available. Only two of the published models were adequate for predicting modulus of weak rock masses over lim- ited ranges of alteration intensities, and none of the models provided good estimates of modulus over a range of geotechnical properties. In light of this shortcoming, a multivariate model was developed from the weak rock mass modulus dataset, and the new model is exponential in form and has the following independent variables： （1） average block size or joint spacing, （2） field estimated rock strength, （3） dis- continuity roughness, and （4） discontinuity infilling hardness. The multivariate model provided better estimates of modulus for both hard-blocky rock masses and intensely-altered rock masses.
