A semi-empirical adsorption kinetic model was proposed with the time compensation method to describe the chemisorption of SO2 in flue gas by carbon adsorbents for flue gas purification.The change in adsorption capacit...A semi-empirical adsorption kinetic model was proposed with the time compensation method to describe the chemisorption of SO2 in flue gas by carbon adsorbents for flue gas purification.The change in adsorption capacity and adsorption rate with time at different water vapor concentrations and different SO2 concentrations was studied.The model was in good agreement with experimental data.The surface reaction was probably the rate controlling step in the early stage for SO2 adsorption by ZL50 activated carbon.The parameters m and n in the nth order adsorption kinetic model were related to the magnitude of the time compensation and adsorption driving force,respectively.The change of parameter n with water vapor concentrations and sulfur dioxide concentrations was studied and some physical implications were given.The sum of square errors was less than 1.0 and the average absolute percentage deviations ranged from 0.5 to 3.2.The kinetic model was compared with other models in the literature.展开更多
I Since 1990, as a person in charge of key research projects of the China Tibetology Research Center, I have had opportunities almost every year to conduct field surveys on anthropology in Tibet together with my colle...I Since 1990, as a person in charge of key research projects of the China Tibetology Research Center, I have had opportunities almost every year to conduct field surveys on anthropology in Tibet together with my colleagues. Our investigations focus on the family. The purpose of our surveys is to see social changes in Tibet during the 50 years after the Democratic Reform through the study of the family as a social cell.展开更多
Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and control...Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.展开更多
文摘A semi-empirical adsorption kinetic model was proposed with the time compensation method to describe the chemisorption of SO2 in flue gas by carbon adsorbents for flue gas purification.The change in adsorption capacity and adsorption rate with time at different water vapor concentrations and different SO2 concentrations was studied.The model was in good agreement with experimental data.The surface reaction was probably the rate controlling step in the early stage for SO2 adsorption by ZL50 activated carbon.The parameters m and n in the nth order adsorption kinetic model were related to the magnitude of the time compensation and adsorption driving force,respectively.The change of parameter n with water vapor concentrations and sulfur dioxide concentrations was studied and some physical implications were given.The sum of square errors was less than 1.0 and the average absolute percentage deviations ranged from 0.5 to 3.2.The kinetic model was compared with other models in the literature.
文摘I Since 1990, as a person in charge of key research projects of the China Tibetology Research Center, I have had opportunities almost every year to conduct field surveys on anthropology in Tibet together with my colleagues. Our investigations focus on the family. The purpose of our surveys is to see social changes in Tibet during the 50 years after the Democratic Reform through the study of the family as a social cell.
文摘Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.
