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Wind field reconstruction for the dispersion modeling of accidental chemical spills on complex geometry

Chinese Journal of Chemical Engineering 2019年第11期27卷 2712-2724页

作者： Bing Wang Feng Qian Weimin Zhong Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics(CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis(PCA) and extreme learning machine(ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace(MPME) algorithm. The fire dynamic simulator(FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case,the error increases to around 12% since no convergence check is available. With parameter tuning abilities,the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.

关键词： Wind field reconstruction CFD PCA Extreme learning machine Sensor placement

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Robust multi-layer extreme learning machine using bias-variance tradeoff

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Journal of Central South University 2020年第12期27卷 3744-3753页

作者： YU Tian-jun YAN Xue-feng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ***,ELM with a single hidden layer structure often fails to achieve good results when faced with large-scale multi-featured *** resolve this problem,we propose a multi-layer framework for the ELM learning algorithm to improve the model’s generalization ***,noises or abnormal points often exist in practical applications,and they result in the inability to obtain clean training *** generalization ability of the original ELM decreases under such *** address this issue,we add model bias and variance to the loss function so that the model gains the ability to minimize model bias and model variance,thus reducing the influence of noise signals.A new robust multi-layer algorithm called ML-RELM is proposed to enhance outlier robustness in complex *** results show that the method has high generalization ability and strong robustness to noise.

关键词： extreme learning machine deep neural network robustness unsupervised feature learning

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Spectral Calibration Model Building by Wavelet Function Regression for In-Situ Measurement of Crystallization Solution Concentration Using ATR-FTIR Spectroscopy

Spectral Calibration Model Building by Wavelet Function Regr...

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第32届中国过程控制会议(CPCC2021)

作者： Xiaojing Pei Tao Liu Siwei Yang Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Institute of Advanced Control Technology Dalian University of Technology

For in-situ measurement of the solution concentration of crystallization process via ATR-FTIR Spectroscopy,a novel spectral calibration model building method is proposed in this paper for improving the real-time measurement accuracy,based on wavelet function *** wavelet basis functions are used to establish the model structure,which could address the nonlinear properties and high-dimensional problem of infrared spectral ***,a moving window strategy is given to select the spectral variables for modeling from all measured data,by evaluating the fitting accuracy on sample solution *** addition,a comparative study on measuring the solution concentration of L-glutamic acid(LGA) crystallization process is conducted in terms of using different spectral datasets from unsaturated and metastable zones for calibration model *** is found that using all the measured data for model building could obtain good measurement accuracy compared to separate modeling for each *** results well demonstrate the measurement accuracy and advantage of the proposed spectral calibration method over the existing methods.

关键词： In-situ measurement spectral calibration model crystallization solution concentration wavelet function regression moving window ATR-FTIR spectroscopy

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Discrete-time 2DOF control design with a generalized predictor for stable and integrating processes with time delay

Discrete-time 2DOF control design with a generalized predict...

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第40届中国控制会议

作者： Yongzhi Zhou Shoulin Hao Tao Liu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Institute of Advanced Control Technology Dalian University of Technology

In this paper,a discrete-time domain two-degree-of-freedom（2 DOF） control design based on a generalized delay-free output predictor is proposed for stable and integrating processes with time ***,a generalized delay-free output predictor is constructed to obtain the delay-free output prediction,such that the controller design could be focused on the delay-free ***,two 2 DOF controllers are analytically designed to realize the set-point tracking and disturbance rejection,respectively,followed by robust stability analysis based on the small gain theory.A significant advantage of the proposed control scheme is that there exists only one adjustable parameter in the prediction filters,or the 2 DOF controllers,which could be monotonically tuned to make a trade-off between control performance and robust *** illustrative examples from the literature are given to demonstrate the effectiveness and superiority of the proposed method.

关键词： Stable and integrating processes time delay discrete-time domain two-degree-of-freedom（2DOF） predictor

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Study on microstructure and properties of cu-14fe-0.05c alloy prepared by drawing 3rd

Study on microstructure and properties of cu-14fe-0.05c allo...

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3rd International Conference on Green Composite Materials and Nanotechnology, GCMN 2019, International Conference on Polymer Synthesis and Application, ICPSA 2019 and International Conference on Composite Materials and Metallurgical Engineering, CMME 2019

作者： Guo, Wei Lu, Deping Jiang, Jiang Liu, Keming Institute of Applied Physics Jiangxi Academy of Sciences Nanchang330096 China Jiangxi Key Laboratory for Advanced Copper and Tungsten Materials Nanchang330096 China Jiangxi Key Laboratory for Precision Drive and Control Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9783035716894

Cu-14Fe-0.05C alloy was prepared by using the vacuum melting process and then multipass drawing deformation was performed. The results showed that with rise of drawing strain, iron-rich phases in the Cu-14Fe-0.05C alloy gradually change from irregularly distributed coarse dendritic phases in the as-cast state to slender fibrous ones distributed parallel along the drawing direction. The higher the strain is, the slender and denser the fibers are and the more uniform the distribution is. Moreover, more interfaces are found between copper matrix and iron-rich phases and the hardness and resistivity of the alloy become higher. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Microstructure

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Effect of annealing on the microstructure and properties of drawn cu-14fe-0.05c alloy 3rd

Effect of annealing on the microstructure and properties of ...

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ISBN: (纸本)9783035716894

Cu-14Fe-0.05C alloy was prepared by using the vacuum melting process and then multipass drawing deformation was performed. After that, the alloy in the as-drawn state was annealed. Based on this, the influence of annealing temperature on microstructure, mechanical performance and electrical conductivity of the alloy was studied. The results showed that the speed of recovery and recrystallization of the as-drawn Cu-14Fe-0.05C alloy accelerates and iron-rich fibers gradually become slender, bend and fracture, with the increase of annealing temperature. The tensile strength of the alloy constantly decreases, while elongation continuously rises and resistivity gradually reduces. With the extension of annealing time, tensile strength and resistivity of the Cu-14Fe-0.05C alloy gradually decreases, while elongation gradually increases. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Microstructure

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Fault Diagnosis Strategy for Few Shot Industrial Process Based on Data Augmentation and Depth Information Extraction

SSRN

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SSRN 2022年

作者： Tian, Ying Xiang, Xin Peng, Xin Zhong, Yin Zhang, Wei Department of control science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Data based intelligent fault diagnosis method is an important tool for ensuring the stability of industrial process. However, in the actual industrial process, due to the difficulty of feature extraction and the lack of labeled fault data, it is hard to form a fault diagnosis model with good performance. To address this problem, the Self-Attention embedded Generative Adversarial Networks combined with the Residual Network(SAGAN-ResNet) is proposed in this study. Firstly, to handle the lack of the fault data, the data enhancement method which consists of Self-Attention embedded generator and discriminator is adopted. Then, to extract the feature for better diagnosis performance, based on the augmented training dataset, Residual Network (ResNet) is introduced. Finally, the proposed method is compared with others, the results show that proposed method has advantages in the case of complex process fault diagnosis with few shot industrial data. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

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Optimizing Bounding Box Regression in Complex Backgrounds

Optimizing Bounding Box Regression in Complex Backgrounds

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Chinese Automation Congress (CAC)

作者： Jun Chen Haiyan Zhang Anjun Yu Yiwei Wang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Jiangxi Ganyue Expressway Co. Ltd. Nanchang China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the context of electrical power operation sites, the automated and accurate detection of whether workers are properly equipped with safety gear, such as safety clothing, helmets, and safety ropes for high-altitude operations, is crucial for ensuring the safety of personnel. Traditional object detection models often struggle to accurately identify these critical pieces of safety equipment in complex environments, due to variable object sizes, overlapping objects, and substantial background noise. To specifically address these challenges, we propose a novel loss function-Enhanced Alignment Intersection over Union (EAIoU) loss. This loss function is designed to enhance the model's ability to discriminate between closely spaced and overlapping safety gear in real-time, significantly improving robustness against scale variations and complex backgrounds. In experiments based on the YOLOv8s architecture, our model achieved a mean Average Precision (mAP) of 92.1 % on a dataset specific to electrical power operations, markedly outperforming traditional IoU loss functions. Additionally, EAIoU demonstrated improved performance on the COCO dataset, indicating its generalization capability across various complex scenarios. This research not only enhances the accuracy and real-time performance of safety equipment detection at electric power operation sites but also opens new avenues for advanced object detection technology in complex, multi-object environments.

关键词： Accuracy Gears Shape Object detection Robustness Real-time systems Safety Power systems Personnel Object recognition

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Distributed Energy Seheduling of Islanded Microgrid Based on Potential Game

Distributed Energy Seheduling of Islanded Microgrid Based on...

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2020 Chinese Automation Congress, CAC 2020

作者： Chen, Lei Niu, Yugang Jia, Tinggang East China University of Science and Technology The Key Laboratory of Advanced Control and Optimization for Chemical Process Shanghai China Shanghai Electric Automation Group Shanghai200023 China

ISBN: (纸本)9781728176871

Microgrid is an advanced application of distributed renewable energy utilization. With the development of energy Internet technology, microgrid is characterized by strong decentralization and high intelligence. The operation optimization of microgrid requires higher individual autonomy and rational ability, and the traditional centralized optimization is difficult to meet this development trend. In this paper, in order to better reflect the high autonomy of microgrid energy management, a distributed optimization based on potential game is proposed by comprehensively considering multiple independent individuals in the microgrid. Under this strategy, each individual makes decisions with the goal of maximizing their own profits, and completes the iterative solution of the strategy through communication with each other. Finally, a simulation example of islanded microgrid is used to verify the results, which shows that the individual autonomy and intelligence of the microgrid were improved, and the feasibility and effectiveness of the strategy are verified. © 2020 IEEE.

关键词： Energy management

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Asymptotic state estimation for linear systems with sensor and actuator faults

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Science China(Information Sciences) 2019年第11期62卷 106-120页

作者： Chun-Hua XIE Hui YANG Dianhui WANG Zhe LI School of Electrical and Automation Engineering East China Jiaotong University Key Laboratory of Advanced Control & Optimization of Jiangxi Province Department of Computer Science and Information Technology La Trobe University College of Electrical and Information Engineering Hunan University

This paper investigates the asymptotic state estimation problem for linear systems with sensor and actuator faults, where the faults are modeled via multiple modes. For the case of sensor faults, we first introduce a new notion of detectability, i.e., detectability of system against sensor faults. The notion helps to address the question of whether it is possible to asymptotically estimate the system state by using the control input and system output, irrespective of which mode the system is in and what values the fault signals are.A necessary and sufficient condition for the system to be detectable against sensor faults is given, and then two switched observers are proposed for asymptotic state estimation with the help of maximin strategy. For the system with ? fault modes, we provide the explicit form of the switched observer, which is based on a bank of ?(?+1)/2 Luenberger-like or sliding-mode observers. Furthermore, extensions to the case of sensor and actuator faults are further studied. Finally, a simulation example of a reduced-order aircraft system is provided to show the effectiveness of the proposed approaches.

关键词： asymptotic state estimation observer linear systems sensor faults actuator faults

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