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NO reduction by carbon-based gases based on coal gasification fractions:NO reduction intermediate identification and kinetic analysis

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Frontiers of Environmental Science & Engineering 2025年第3期19卷 175-189页

作者： Xiang Zhang Jian Tian Tanghui Hu Guangyong Yue Xianlong Liu Wen Zhou Xiaohong Liu Xinye Wang Ministry-of-Education Key Laboratory for the Green Preparation and Application of Functional Materials Hubei Engineering Research Center for Green Industrialization Technology of Industrial WasteSchool of Materials Science and EngineeringHubei UniversityWuhan 430062China Hubei Huda Tianshu New Energy Materials Industrial Research and Design Institute Co. Ltd.Wuhan 430062China Wuhan Tianshu Dust Removal Equipment Co. Ltd.Wuhan 430062China Engineering Laboratory for Energy System Process Conversion&Emission Control Technology of Jiangsu Province School of Energy and Mechanical EngineeringNanjing Normal UniversityNanjing 210042China

Combining pulverized coal gasifiers with cement kiln production is promising for application in low-cost and efficient NO *** paper presents a pulverized coal gasifier catalytic denitration technique and investigates the homogeneous reduction(by CO and CH_(4))and heterogeneous catalytic reduction(by coke,CaO,MgO,and Fe_(2)O_(3))of NO.A combination of Chemkin simulations and fixed-bed experiments is used to elucidate the reaction pathways and key intermediates of NO reduction by carbon-based *** addition,the activation energies for different catalyst combinations were analyzed via reaction *** results demonstrate that the presence of small amounts of O_(2) inhibits NO reduction by CO but promotes NO reduction by CH_(4).The NCO·radical is essential for the NO reduction process,and the generation of this radical depends on the CH_(4) cleavage intermediate and O·*** and Fe_(2)O_(3) exhibit more significant catalytic effects on NO reduction by carbon-based gases than the other catalysts *** presence of a small amount of O_(2) in the reacting gas mixtures facilitates the NO reduction *** activation energy is reduced to 1.02 kJ/mol,and the NO conversion reaches 99.80%when the catalyst is C+CaO+MgO+Fe_(2)O_(3) and the gas composition is CO+CH_(4)+O_(2).This work provides theoretical support and data recommendations for the use of pulverized coal gasifiers for the denitrification of cement kilns.

关键词： Carbon-based gas Catalytic denitration Coke Kinetic analysis Reaction mechanism

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling

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Chinese Journal of Chemical Engineering 2025年 81卷 241-254页

作者： Li, Zhi Xia, Yuchong Long, Jian Liu, Chensheng Zhang, Longfei State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai 200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China

Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty. Due to the outstanding ability for high-level feature extraction, stacked autoencoder (SAE) has been widely used to improve the model accuracy of soft sensors. However, with the increase of network layers, SAE may encounter serious information loss issues, which affect the modeling performance of soft sensors. Besides, there are typically very few labeled samples in the data set, which brings challenges to traditional neural networks to solve. In this paper, a multi-scale feature fused stacked autoencoder (MFF-SAE) is suggested for feature representation related to hierarchical output, where stacked autoencoder, mutual information (MI) and multi-scale feature fusion (MFF) strategies are integrated. Based on correlation analysis between output and input variables, critical hidden variables are extracted from the original variables in each autoencoder's input layer, which are correspondingly given varying weights. Besides, an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers. Then, the MFF-SAE method is designed and stacked to form deep networks. Two practical industrial processes are utilized to evaluate the performance of MFF-SAE. Results from simulations indicate that in comparison to other cutting-edge techniques, the proposed method may considerably enhance the accuracy of soft sensor modeling, where the suggested method reduces the root mean square error (RMSE) by 71.8%, 17.1% and 64.7%, 15.1%, respectively. © 2025 Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd.

关键词： Frequency estimation

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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H_∞ consensus control of a class of second-order multi-agent systems without relative velocity measurement

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Chinese Physics B 2010年第7期19卷 188-192页

作者：张文广曾德良郭振凯 Beijing Key Laboratory of Measurement and Control New Technology and System for Industrial Process Control & Computer Engineering SchoolNorth China Electric Power University School of Mathematics and Information Ludong University

This paper studies consensus control problems for a class of second-order multi-agent systems without relative velocity measurement. Some dynamic neighbour-based rules are adopted for the agents in the presence of external disturbances. A sufficient condition is derived to make all agents achieve consensus while satisfying desired H∞ performance. Finally, numerical simulations are provided to show the effectiveness of our theoretical results.

关键词： consensus control H∞ second-order multi-agent systems

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Data driven state detection algorithm for ash deposition detection

Data driven state detection algorithm for ash deposition det...

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2013 18th International Conference on Methods and Models in Automation and Robotics, MMAR 2013

作者： Liu, Jiwei Liu, Jizhen Deliang, Zeng Chang, Taihua Key Laboratory of Measurement and Control New Technology and System for Industrial Process North China Electric Power University Beijing China

ISBN: (纸本)9781467355063

A data driven state detection algorithm was proposed to improve the security and reliability of equipment. The algorithm is used for one class of objects whose state parameters change slowly and cumulatively in the long term. The concepts of multi-scale system, multi-scale entropy and multi-scale exergy were used to describe these processes. With the help of the algorithm, an ash deposition index for the radiant heating surface of a 600 MW unit was constructed to monitor the states of the instruments. Noise was analyzed. The results of simulation experiments demonstrate the effectiveness of the algorithm, which can provide a technical basis for condition maintenance. © 2013 West Pomeranian University of technology.

关键词： Exergy

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Modeling on SCR process of a coal-fired boiler using LSSVM method 27

Modeling on SCR process of a coal-fired boiler using LSSVM m...

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27th Chinese control and Decision Conference, CCDC 2015

作者： Yang, Tingting Chao, Cui You, Lv Jian, Li Key Laboratory of Measurement and Control New Technology and System for Industrial Process North China Electric Power University Beijing China

ISBN: (纸本)9781479970179

Selective catalytic reduction (SCR) is widely applied in coal-fired power plants. SCR process is a complex physical and chemical process with multi-input, strong coupling, nonlinear, and time delay characteristics. An effective model is the basis of conducting control and optimization. In this paper, influencing factors of catalytic reaction in SCR converter are analyzed and five measurable parameters are selected as the model input variables. NOx emission and NH3slip are separately taken as output variables. The LSSVM algorithm is employed to build the SCR models using real plant operation data from a 1000 MW coal-fired boiler. Simulation results have shown satisfied model accuracy and effectiveness are achieved. © 2015 IEEE.

关键词： coal-fired power plant data-driven model LSSVM selective catalytic reduction

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Modeling on SCR process of a Coal-fired Boiler Using LSSVM method

Modeling on SCR Process of a Coal-fired Boiler Using LSSVM m...

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第27届中国控制与决策会议

作者： YANG Tingting CUI Chao LV You LI Jian Key Laboratory of Measurement & Control New Technology and System for Industrial Process(North China Electric Power University)

ISBN: (纸本)9781479970186

Selective catalytic reduction（SCR） is widely applied in coal-fired power *** process is a complex physical and chemical process with multi-input,strong coupling,nonlinear,and time delay *** effective model is the basis of conducting control and *** this paper,influencing factors of catalytic reaction in SCR converter are analyzed and five measurable parameters are selected as the model input *** emission and NHslip are separately taken as output *** LSSVM algorithm is employed to build the SCR models using real plant operation data from a 1000 MW coal-fired *** results have shown satisfied model accuracy and effectiveness are achieved.

关键词： selective catalytic reduction coal-fired power plant LSSVM data-driven model

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Study of sliding-mode control based grid-side PWM rectifier for wind power generation system under unbalanced input voltage condition

Study of sliding-mode control based grid-side PWM rectifier ...

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2009 4th IEEE Conference on industrial Electronics and Applications, ICIEA 2009

作者： Zhang, Jianhua Li, Xiaojun Hou, Guolian An, Xuemin Beijing Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University Beijing China

ISBN: (纸本)9781424428007

A sliding mode control scheme is presented in αβ frame for grid-side PWM rectifiers in wind power generation systems. The positive and negative sequence voltages are needed to design the controller, nevertheless, the positive and negative sequence currents are not needed. This scheme is compared with the dual current control scheme in the rotating frame d-q. The simulation results show that sliding mode control scheme can realize unit power factor operation when the amplitude is unbalanced. © 2009 IEEE.

关键词： Sliding mode control

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Electricity price forecasting using neural networks trained with entropic criterion

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7th International Symposium on Neural Networks, ISNN 2010

作者： Zhang, Jianhua Wang, Jingyue Wang, Rui Hou, Guolian Beijing Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University Beijing 102206 China

ISBN: (纸本)9783642129896

Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. The problem becomes more complex because the acquired data series are non-linear and non-Gaussian. In this paper, Multi Layer Perceptrons (MLP) trained with minimizing error entropy (MEE) algorithm is utilized to forecast electricity price. Compared with the conventional MLP with mean square error (MSE) criterion, the proposed approach can achieve better performance in simulated examples. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Entropy

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