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IEEE Instrumentation and Measurement Technology Conference

作者： Jinkun Li Xiao-Yu Tang Songchen Li Xin Wang Chunjie Yang Wenhai Wang State Key Laboratory of Industrial Control Science Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Reasonable control of the distribution and development of gas flow is one of the most important operations for blast furnace (BF) ironmaking process. The BF throat temperature directly reflects internal coal gas flow distribution. Therefore, BF throat temperature monitoring is the key prerequisite for BF abnormal monitoring. In this paper, a novel fault diagnosis method for BF cross temperature measurement device is proposed, based on a residual analysis method combining principal component analysis (PCA) and furnace throat temperature estimation via multi-burden layer reconstruction. Firstly, the material surface profile of each BF burden layer is calculated according to the burden material distributing movement process, the material layer distribution model is established in combination with particle descending process, and the multi-burden layer reconstruction is implemented. Then, using the multilayer ore coke ratio (OCR) distribution concluded from the reconstructed multi-burden layers and the main BF state parameters, the long short-term memory (LSTM) network is adopted to estimate the furnace throat temperature. The residual difference between the estimated result and the actual temperature value is calculated. Finally, residual difference and BF main parameters are used as inputs of PCA to obtain fault diagnosis results, and the majority voting (MV) method is introduced to improve the stability and accuracy of the results. A case study is conducted using data from a BF located in the south of China to verify the effectiveness of the method. The fault diagnosis accuracy is above 90% when the offset is greater than 9°C or the drift rate is greater than 0.4 %

关键词： Temperature measurement Fault diagnosis Temperature sensors Temperature distribution Accuracy Blast furnaces Monitoring

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Hybrid algorithm of invasive weeds and particle swarm optimization 9

Hybrid algorithm of invasive weeds and particle swarm optimi...

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9th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2023

作者： Du, Xianjun Li, Zhuohang Lanzhou Univ. of Tech College of Electrical Engineering and Information Engineering Lanzhou730050 China Lanzhou Univ. of Tech Key Laboratory of Gansu Advanced Control for Industrial Process Lanzhou730050 China

ISBN: (纸本)9798350304428

Aiming at the disadvantages of low optimization accuracy and easy to fall into local optimum of particle swarm optimization algorithm, a hybrid algorithm combining the invasive weed algorithm and particle swarm optimization algorithm is proposed. The main structure of the hybrid algorithm is PSO, and IWO makes the algorithm avoid falling into local optimum. The simulation test is carried out with 11 benchmark test functions, and through comparison with the other two improved PSO algorithms, it is concluded that the hybrid algorithm in this paper is not easy to fall into local optimization and has high optimization accuracy. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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A Blast Furnace Throat Temperature Estimation Method Based on Multi-time Scale Online Fusion

A Blast Furnace Throat Temperature Estimation Method Based o...

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Chinese control and Decision Conference, CCDC

作者： Jinkun Li Xiao-Yu Tang Weiting Song Wangzhen Jin Chunjie Yang Wenhai Wang State Key Laboratory of Industrial Control Science Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Blast furnace (BF) ironmaking is a critical stage in the production of steel. Reasonable operation of BF requires maintaining a stable and effective gas flow, and the throat temperature directly reflects the distribution of gas flow. Due to the harsh environment inside BF, the internal temperature sensors are often damaged. Therefore, estimating the temperature at the BF throat location is crucial. This paper proposes a BF throat temperature estimation method based on multi-timescale online fusion (MTSOF), combining the mechanics of BF operation and a data-driven model. According to the burden movement model, the ore-coke ratio (OCR) is calculated. Subsequently, considering the multi-time scale characteristics within BF, the furnace dataset is divided into a high-frequency dataset and a low-frequency dataset. The support vector regression (SVR) is used to train the low-frequency temperature estimation model, and an integrated gated recurrent unit (I-GRU) network is proposed to train the high-frequency temperature estimation model. Finally, in the testing phase, the proposed online fusion network based on update gate and forget gate integrates the outputs of the low-frequency temperature estimation model and high-frequency temperature estimation model to obtain the final temperature estimation results. This method has been validated on a BF in South China. Compared with the measurement results, the mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE) of the BF throat temperature estimation results obtained by the proposed method are 2.997%, 1.659°C and 1.978 °C respectively.

关键词： Temperature measurement Temperature sensors Support vector machines Temperature distribution Optical character recognition Estimation Fluid flow

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Root-KGD: A Novel Framework for Root Cause Diagnosis Based on Knowledge Graph and industrial Data

arXiv

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arXiv 2024年

作者： Chen, Jiyu Qian, Jinchuan Zhang, Xinmin Song, Zhihuan State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated using two industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow Facility (MFF). Compared to existing methods, Root-KGD not only gives more accurate root cause variable diagnosis results but also provides interpretable fault-related information by locating faults to corresponding physical entities in knowledge graph (such as devices and streams). In addition, combined with its lightweight nature, Root-KGD is more effective in online industrial applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Simultaneous adsorption of As(Ⅲ) and Pb(Ⅱ) by the iron-sulfur codoped biochar composite:Competitive and synergistic effects

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Journal of Environmental Sciences 2023年第3期125卷 14-25页

作者： Yijun Chen Qintie Lin Xiaoqing Wen Jin He Haoyu Luo Quanfa Zhong Libin Wu Jiaqi Li Guangdong Industrial Contaminated Site Remediation Technology and Equipment Engineering Research CenterSchool of Environmental Science and EngineeringGuangdong University of TechnologyGuangzhou 510006China Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters Ministry of EducationSchool of Environment and EnergySouth China University of TechnologyGuangzhou 510006China Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education Institute of Environmental and Ecological EngineeringGuangdong University of TechnologyGuangzhou 510006China

Simultaneous elimination of As(Ⅲ) and Pb(Ⅱ) from wastewater is still a great *** this work,an iron-sulfur codoped biochar (Fe/S-BC) was successfully fabricated in a simplified way and was applied to the remediate the co-pollution of As(Ⅲ) and Pb(Ⅱ).The positive enthalpy indicated that the adsorption in As-Pb co-pollution was an endothermic *** mechanism of As(Ⅲ) removal could be illustrated by surface complexation,oxidation and *** addition to precipitation and complexation,the elimination mechanism of Pb(Ⅱ) also contained ion exchange and electrostatic *** and synergistic effects existed simultaneously in the co-contamination *** suppression of As(Ⅲ) was ascribed to competitive complexation of the two metals on Fe/S-BC,while the synergy of Pb(Ⅱ) was attributed to the formation of the PbFe2(AsO_(4))2(OH)*** experiments revealed that Fe/S-BC had outstanding ability to remove As(Ⅲ) and Pb(Ⅱ),regardless of pH dependency and interference by various coexisting *** maximum adsorption capacities of the Fe/S-BC for As(Ⅲ) and Pb(Ⅱ) were 91.2 mg/g and 631.7 mg/g,***/S-BC could be treated as a novel candidate for the elimination of As(Ⅲ)-Pb(Ⅱ) combined pollution.

关键词： Iron-sulfur codoped Biochar Arsenic and lead Co-contamination remediation

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Efficient Autonomous Exploration of Unknown Environment Using Regions Segmentation and VRP 1

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Xue, Chaoyu Zhao, Tianhao Xie, Anhuan Zhu, Qiuguo Wu, Jun Xiong, Rong College of Control Science and Engineering Zhejiang University Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Autonomous exploration of unknown environments is a critical application scenario in robotics. However, existing studies have strived to generate an efficient tour plan that enables the robots to fully explore the environment. This is due to the greedy strategies and the lack of utilizing the information of the unexplored area. To address the above issues, we propose a new exploration strategy. Our approach involves partitioning the unexplored area into distinct regions and utilizing their estimated workload and spatial structure to construct a region graph. Subsequently, a VRP (Vehicle Routing Problem) planner allocates regions to robots and generates a global tour for each robot in a coordinated manner, which is then refined and provided to the robot’s navigation module. Experimental results demonstrate that that our method outperforms existing approaches in terms of exploration speed, total distance, and overlap ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robots

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Improved Variable Deviation Collaborative control of Vector Nozzles for Aircraft Engine 43

Improved Variable Deviation Collaborative Control of Vector ...

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43rd Chinese control Conference, CCC 2024

作者： Dai, Jiacheng Ma, Yanhua Du, Xian Gao, Yongfeng Dalian University of Technology School of Control Science and Engineering Dalian116024 China Dalian University of Technology School of Integrated Circuits Dalian116024 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Vector nozzle is an adjustable tail nozzle in aircraft engines. Due to the complex structure and coupling effect of each actuator, the traditional collaborative control methods can hardly achieve the required time synchronization. In this paper, an improved variable deviation collaborative control method is proposed to address such issues, which promotes the synchronization performance of nozzle actuators through an improved deviation coupler. At the same time, an adaptive disturbance rejection controller is used to enhance the robustness of the system. The experimental results show that the error of synchronization time of each actuator in the vector nozzle is less than 5%, and the steady-state error of displacement is less than 1%, which meets the requirement of practical engineering. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Disturbance rejection

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FWNRS:A Fast Attribute Reduction Algorithm based on Weighted Neighborhood Rough Set 23

FWNRS:A Fast Attribute Reduction Algorithm based on Weighted...

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8th International Conference on Mathematics and Artificial Intelligence, ICMAI 2023

作者： Peng, Xiaoli Wang, Ping Shao, Yabin Pu, Chenggen Qian, Jie College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450399982

The weighted neighborhood rough set (WNRS) is a novel neighborhood rough set model which doesn't treat all attributes as equally important. It performs well in attribute reduction on real-valued data. However, there are still so many calculations in the attribute reduction based on WNRS that its efficiency is not high enough. To solve this drawback, we propose a fast attribute reduction algorithm based on WNRS (FWNRS) in this work. Firstly, we propose three fast strategies to reduce the neighborhood search range. The first one is reducing the neighborhood search rang of each testing sample from the whole universe or the boundary region to its corresponding neighborhood. The second one is removing the positive region samples from the corresponding neighborhood. The third one is to set the initial value of the cycle to be greater than the final value and the step size to be negative when the neighborhood radius δ is optimizing. Secondly, we design the fast attribute reduction algorithm based on the fast strategies. Finally, six UCI benchmark datasets are used to verify the efficiency of FWNRS. The experimental results illustrate that FWNRS is more efficient than the state-of-the-art comparison algorithms, which are all fast attribute reduction algorithms. © 2023 Copyright held by the owner/author(s)

关键词： Efficiency

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A Review of Rotation Mechanical Fault Diagnosis Research Based on Deep Domain Adaptation 9

A Review of Rotation Mechanical Fault Diagnosis Research Bas...

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9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Zhang, Shun Xie, Gang Tian, Juan Shi, Hui Zhang, Xiaohong Nie, Xiaoyin Xie, Xinlin Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan China Taiyuan University of Science and Technology Dept. of Electronic and Information Engineering Taiyuan China Division of Industrial and System Engineering Taiyuan China

ISBN: (纸本)9798350333794

With the rapid development of computer technology and the Internet of Things (IoT), a large amount of data containing both healthy and faulty states can be collected from existing devices in factories. However, the usable faulty state data is scarce. Transfer learning makes it possible to train efficient and reliable neural networks with a small number of samples. Domain adaptation, as a branch of transfer learning, can effectively realize knowledge transfer between small sample data. Starting from three subcategories of deep learning-based domain adaptation methods, this article summarizes the latest research achievements and provides insights into future research directions. © 2023 IEEE.

关键词： Failure analysis

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Principal Trotter observation error with truncated commutators

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Physical Review A 2024年第6期110卷 062614-062614页

作者： Langyu Li Institute of Cyber-system and Control College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang 310027 China and State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou Zhejiang 310027 China

Hamiltonian simulation is one of the most promising applications of quantum computers, and the product formula is one of the most important methods for this purpose. Previous related work has mainly focused on the worst-case, average-case, or actual-case scenarios. In this work we consider the simulation error under a fixed observable. Specifically, errors that commute with this observable become less significant. To illustrate this point, we define the observation error as the expectation under the observable and provide a commutativity-based upper bound using the Baker-Campbell-Hausdorff formula. Furthermore, we observe that the evolution order plays a critical role in determining the observation error. By leveraging a simulated annealing algorithm, we design an evolution order optimization algorithm, to identify an order that enhances commutativity with the observable, where the observation error indicated by this upper bound can be significantly compressed. In the experiment with the Heisenberg model, the observation bound compresses the Trotter number by nearly half compared to recent commutator bounds. The experiment on the hydrogen molecule Hamiltonian demonstrates that optimizing the order can lead to nearly half the reduction in the Trotter number.

关键词： Hamiltonians

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