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Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic (vol 11, 5172, 2020)

NATURE COMMUNICATIONS 2020年第1期11卷 5172页

作者： Liu, Zhu Ciais, Philippe Deng, Zhu Lei, Ruixue Davis, Steven J. Feng, Sha Zheng, Bo Cui, Duo Dou, Xinyu Zhu, Biqing Guo, Rui Ke, Piyu Sun, Taochun Lu, Chenxi He, Pan Wang, Yuan Yue, Xu Wang, Yilong Lei, Yadong Zhou, Hao Cai, Zhaonan Wu, Yuhui Guo, Runtao Han, Tingxuan Xue, Jinjun Boucher, Olivier Boucher, Eulalie Chevallier, Frederic Tanaka, Katsumasa Wei, Yiming Zhong, Haiwang Kang, Chongqing Zhang, Ning Chen, Bin Xi, Fengming Liu, Miaomiao Breon, Francois-Marie Lu, Yonglong Zhang, Qiang Guan, Dabo Gong, Peng Kammen, Daniel M. He, Kebin Schellnhuber, Hans Joachim Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232 Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Institut Pierre-Simon Laplace Sorbonne Université / CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France. Center for Energy and Environmental Policy Research Beijing Institute of Technology Beijing China. Department of Electrical Engineering the State Key Lab of Control and Simulation of Power Systems and Generation Equipment Institute for National Governance and G

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5

关键词： Climate sciences Atmospheric science Environmental sciences Environmental social sciences

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Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

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Nature communications 2020年第1期11卷 6292页

作者： Zhu Liu Philippe Ciais Zhu Deng Ruixue Lei Steven J Davis Sha Feng Bo Zheng Duo Cui Xinyu Dou Biqing Zhu Rui Guo Piyu Ke Taochun Sun Chenxi Lu Pan He Yuan Wang Xu Yue Yilong Wang Yadong Lei Hao Zhou Zhaonan Cai Yuhui Wu Runtao Guo Tingxuan Han Jinjun Xue Olivier Boucher Eulalie Boucher Frédéric Chevallier Katsumasa Tanaka Yiming Wei Haiwang Zhong Chongqing Kang Ning Zhang Bin Chen Fengming Xi Miaomiao Liu François-Marie Bréon Yonglong Lu Qiang Zhang Dabo Guan Peng Gong Daniel M Kammen Kebin He Hans Joachim Schellnhuber Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Faculty of Management and Economics Kunming University of Science and Technology 13 Kunming China. Economic Research Centre of Nagoya University Furo-cho Chikusa-ku Nagoya Japan. Institut Pierre-Simon Laplace Sorbonne Université/CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France.

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5.

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Design of a coal quality detection management system for power plants based on UML

Design of a coal quality detection management system for pow...

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International Conference on Testing and Measurement: Techniques and Applications, TMTA 2015

作者： Liu, Z. Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation North China Electric Power University Baoding China

ISBN: (纸本)9781138028128

The coal quality detection is an integral part of the fuel management, including coal sampling, coal sample preparation, and coal detection three parts. Based on the idea of object-oriented analysis and design, the requirements analysis model for a Coal Quality Detection Management System (CQDMS) is established by using the Unified Modeling Language. Three-level electronic coding based on bar code technology is designed and the statistic model and the dynamic model of the system are visually modeled using UML. Finally, the CQDMS is developed by the *** technology. © 2015 Taylor & Francis Group, London.

关键词： Unified Modeling Language

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Optimization study on primary frequency control of circulating fluidized bed units

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Energy Education Science and technology Part A: Energy Science and Research 2014年第4期32卷 2623-2632页

作者： Yue, Zhang Chengcheng, Yuan Yatian, Li Renjie, Gong Peiqing, Sun North China Electric Power University Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation Baoding China Dalian Taishan Thermal Power Ltd Da lian China

Along with the construction of "smart grid" spreading rapidly, the primary frequency regulation has already been the important indicator used by power grids to assess the generating quality of power plants. Every plant inputs many human and material resources to improve the quality of primary frequency regulation. But some special units, such as some circulating fluidized bed units, have bad effect. This article has looked into the features of circulating fluidized bed units and dug into every chance to increase characters. By redesigning and optimizing logic of primary frequency regulation, the characters of primary frequency regulation have been improved. At last the feasibility of the scheme has been verified by examination on a practical unit. © Sila Science. All rights reserved.

关键词： Smart power grids

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Ultra-short-term wind speed and power forecast based on dynamic selective neural network ensemble

Ultra-short-term wind speed and power forecast based on dyna...

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作者： Liu, Yan Hua Dong, Ze Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Baoding China

ISBN: (纸本)9783038351382

With the scale of grid-connected wind farms increasing, accurate forecast of ultra-short-term wind speed and wind power is very important to the stable operation of power systems. This paper presents a dynamic selective neural network ensemble (DSNNE) forecast method, which makes use of K nearest neighbor algorithm to collect the generalization errors of certain different BP neural networks and RBF neural networks into a performance matrix and then the neural networks with low local generalization errors are dynamically selected and locally dynamic averaging is applied to the neural networks in order to conduct the final results of the ensemble. Then this method is applied to realize the wind speed and power ultra-short-term advance forecast, taking the wind speed and wind turbine power output from a wind farm in China as the original data. The research results show that DSNNE improves the generalization ability of the neural network system and the prediction accuracy of wind power and wind speed significantly. It proves the validity and effectiveness of the DSNNE with controlling the biggest mean relative error of 2 minutes ahead wind power and wind speed forecast as low as 25% and 16% respectively. © (2014) Trans Tech Publications, Switzerland.

关键词： Wind speed

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The optimized design of coordinated control system based on load feedforward and unidirectional decoupling

The optimized design of coordinated control system based on ...

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4th International Conference on Frontiers of Manufacturing Science and Measuring technology, ICFMM 2014

作者： Zhai, Yong Jie Zhang, Jun Ying Zhang, Yan Xia Zhao, Jin Song Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Hebei Province China

ISBN: (纸本)9783038351924

In actual operation, the rising load will lead to the reduction of pressure because of the long delay of the boiler. It can lead to the main steam pressure lockout in severe cases. The proposed model is derived by the principle analysis of control object and the introduction of load feedforward. The unidirectional decoupling method is verified by the simulation in Matlab. Not only the decoupling has achieved good results, but also the performance of the control system has been greatly improved. All the above study are based on a 1000MW power unit. © (2014) Trans Tech Publications, Switzerland.

关键词： Boilers

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Identification of thermal process using hammerstein model based on particle swarm optimization algorithm

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2012 International Conference on Electrical and Electronics Engineering, ICEE 2012

作者： Wang, Dong Feng Ren, Yan Yan Liu, Chang Liang Han, Pu Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Baoding China

ISBN: (纸本)9781461449805

In order to identify the controlled objects which are nonlinear time-delay processes with slow time-varying in the thermal system accurately, the Hammerstein model and particle swarm optimization (PSO) algorithm were used in this paper. For the Hammerstein model discussed in this paper, the polynomial and difference equations were used to express the nonlinear part and linear part of Hammerstein model, respectively. This study used the PSO algorithm to find the optimal solution of Hammerstein model's undetermined parameters in the parameters space. For illustration, an example of main-steam temperature system identification was utilized to show the feasibility of the Hammerstein model based on PSO algorithm in identifying the thermal system processes. The PSO-based Hammerstein model can effectively represent the controlled objects which are nonlinear time-delay processes in the thermal system and thus a class of identification problems with nonlinearity in thermal system can be solved. © 2014 Springer Science+Business Media New York.

关键词： Difference equations

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Design of a coal quality detection management system for power plants based on UML

Design of a coal quality detection management system for pow...

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2015 International Conference on Testing and Measurement: Techniques and Applications(TMTA 2015)

作者： Z.Liu Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation (North China Electric Power University)

ISBN: (纸本)9781138028128

The coal quality detection is an integral part of the fuel management, including coal sampling, coal sample preparation, and coal detection three parts. Based on the idea of object-oriented analysis and design, the requirements analysis model for a Coal Quality Detection Management System(CQDMS) is established by using the Unified Modeling Language. Three-level electronic coding based on bar code technology is designed and the statistic model and the dynamic model of the system are visually modeled using UML. Finally, the CQDMS is developed by the *** technology.

关键词： System NET Design of a coal quality detection management system for power plants based on UML

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Novel Multi-output Support Vector Regression Model via Double Regularization

Novel Multi-output Support Vector Regression Model via Doubl...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yanyan Yang Degang Chen Ze Dong School of Control and Computer Engineering North China Electric Power University Beijing China Department of Mathematics and Physics North China Electric Power University Beijing China Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation North China Electric Power University Baoding P. R. China

ISBN: (纸本)9781479986989

Multi-output regression estimation aims at mining a vector-valued function from multi-dimensional input vector to multi-dimensional output vector. However, the output variables may be correlative. It is desirable to develop a multi-dimensional regression model taking advantage of the possible correlations. Therefore, this paper proposes a novel multi-output support vector regression model via double regularization. For each output variable, we first introduce an influential level vector with the dimensionality equal to the one of the input vector, in order to characterize the correlation between this variable and other output variables. As a second regularization term, 2-norms of all influential level vectors are then added into the objective function. Each influential level vector is also considered in constraints of our model. Finally, experimental comparisons demonstrate that our proposed model in this paper has a better generalization performance as well as a better robustness.

关键词： Yttrium Support vector machines Correlation Mathematical model Linear programming Kernel Computational modeling

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Application of DMC algorithm on pH control of WFGD system

Application of DMC algorithm on pH control of WFGD system

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作者： Guo, Shan Shan Dong, Ze Hebei Engineering Research Center of Simulation and Optimized Control for Power Generation North China Electric Power University Baoding China

ISBN: (纸本)9783038351382

The paper studies on wet flue gas desulfurization process and the factors of affecting the desulfurization efficiency, and analyses the control system of the absorber slurry pH value. The conventional PID control is difficult to achieve satisfactory control effect, so it puts forward the predictive control scheme based on Dynamic Matrix control (DMC) algorithm to identify and control the change of pH value. simulation results show that under the control of DMC algorithm, the slurry pH value has a good tracking for setting input and its changes, reflecting a high degree of adaptability. The control with low overshoot and good accuracy is better than conventional PID control and meets the requirements of real-time control. © (2014) Trans Tech Publications, Switzerland.

关键词： Three term control systems

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