Copper smelting is a significant source of SO2 emission. It is important to quantify SO2 emissions from combustion sources for regulatory and control purposes in relation to air quality. The characteristics of SO2 emi...Copper smelting is a significant source of SO2 emission. It is important to quantify SO2 emissions from combustion sources for regulatory and control purposes in relation to air quality. The characteristics of SO2 emissions from copper smelting industry in Yurman Province, China, were examined. Analysis based on the present situation, material balance and measuring method were used to confirm SO2 emission factors of copper smelting industry. Results show that SO2 emission factors for Isa system, side blown-continuous converting system （SB-CC）, blast furnace-continuous converting systems （B-CC） and blast furnace-converter blowing （B-C） are 11.69-18.64, 62.44--101.4, 19.43-37.88 and 45.48-81.03 kg/t（blister copper）, respectively. The comprehensive emission factor based on all smelting plants is found to be in the range of 23-39.99 kg-SO2/t（blister copper） for Yunnan Province, China. The results are compared with those for discharge coefficients of industrial pollutants in the First National General Survey of Pollution Sources and the emission factor of the total amount of major pollutants. It is observed that there are some differences among emission factors.展开更多
Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degrad...Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.展开更多
基金Projects(KKK0201022137,KKK0201122183) supported by the Special Research Foundation of Yunnan Provincial Environmental Protection Bureau,China
文摘Copper smelting is a significant source of SO2 emission. It is important to quantify SO2 emissions from combustion sources for regulatory and control purposes in relation to air quality. The characteristics of SO2 emissions from copper smelting industry in Yurman Province, China, were examined. Analysis based on the present situation, material balance and measuring method were used to confirm SO2 emission factors of copper smelting industry. Results show that SO2 emission factors for Isa system, side blown-continuous converting system （SB-CC）, blast furnace-continuous converting systems （B-CC） and blast furnace-converter blowing （B-C） are 11.69-18.64, 62.44--101.4, 19.43-37.88 and 45.48-81.03 kg/t（blister copper）, respectively. The comprehensive emission factor based on all smelting plants is found to be in the range of 23-39.99 kg-SO2/t（blister copper） for Yunnan Province, China. The results are compared with those for discharge coefficients of industrial pollutants in the First National General Survey of Pollution Sources and the emission factor of the total amount of major pollutants. It is observed that there are some differences among emission factors.
基金Projects(51475462,61374138,61370031)supported by the National Natural Science Foundation of China
文摘Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.
