Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenome...Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.展开更多
Minjingu Phosphate Rock （MPR） from Northern Tanzania and the Ikutha Phosphate Rock （IPR） found in Central-Southeast Kenya are well documented as potential sources of phosphorous （P） available in East Africa. On-...Minjingu Phosphate Rock （MPR） from Northern Tanzania and the Ikutha Phosphate Rock （IPR） found in Central-Southeast Kenya are well documented as potential sources of phosphorous （P） available in East Africa. On-farm trials in phosphate-deficient soils in Western Kenya demonstrated MPR to be as effective as triple superphosphate （TSP） - 20% P, at equal P rates. The aim of this work is to determine the distribution of phosphorus in these phosphate rocks （PRs）. The different phosphorus fractions were extracted using the modified Williams extraction procedure and analysis carried on a UV/VIS spectrometer （SHIMADZU UV-220-02 and NOVASPEC II）. The analysis showed that the most abundant form of phosphorus in the phosphate rocks was the Inorganic Phosphorus （IP） contributing 74.20% of total phosphorus （TP） for Minjingu, and 83,28% of total phosphorus for Ikutha phosphate rock.展开更多
基金Supported by the National Natural Science Foundation of China(61273070)the Foundation of Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.
文摘Minjingu Phosphate Rock （MPR） from Northern Tanzania and the Ikutha Phosphate Rock （IPR） found in Central-Southeast Kenya are well documented as potential sources of phosphorous （P） available in East Africa. On-farm trials in phosphate-deficient soils in Western Kenya demonstrated MPR to be as effective as triple superphosphate （TSP） - 20% P, at equal P rates. The aim of this work is to determine the distribution of phosphorus in these phosphate rocks （PRs）. The different phosphorus fractions were extracted using the modified Williams extraction procedure and analysis carried on a UV/VIS spectrometer （SHIMADZU UV-220-02 and NOVASPEC II）. The analysis showed that the most abundant form of phosphorus in the phosphate rocks was the Inorganic Phosphorus （IP） contributing 74.20% of total phosphorus （TP） for Minjingu, and 83,28% of total phosphorus for Ikutha phosphate rock.
