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2023 China Automation Congress, CAC 2023

作者： Lei, Jiahui Liu, Hanwen Zhao, Jun Wang, Wei Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China Chn Energy Wuda Coal Processing Co. Ltd. Wuhai China

ISBN: (纸本)9798350303759

In the process of coal blending and washing in coal preparation plants, there are various raw coals with different quality that changes greatly, which results in poor quality and low profit. To solve these problems, a novel optimization method for raw coal blending based on interval programming is proposed. Interval numbers are introduced to represent the fluctuation range of ash and sulfur content at each density level of raw coals. A predictive model of quantity and quality of dense medium cyclone products is constructed, which uses Hermite interpolation to obtain washability curves of each raw coal and adopts fitting methods to obtain the distribution curve. In order to obtain optimal coal blending schemes, considering operation and maintenance costs of raw coal feeding line, a mixed integer nonlinear programming (MINLP) model is built, whose objective is to maximize profit with the main constraints of clean coal quality and inventory of raw coal. Interval possibility is introduced to transform constraints with interval number and a customized branch and bound algorithm is used to solve it. In order to verify the effectiveness of the method in this paper, experiments are conducted using manufacturing data from a domestic coal preparation plant. The experimental results indicate that the proposed method is practical and has better economic performance. © 2023 IEEE.

关键词： Profitability

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A Generalized Wind Turbine Anomaly Detection Method Based on Combined Probability Estimation Model

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1136-1148页

作者： Xiangjun Zeng Ming Yang Chen Feng Yaohua Tang Key Laboratory of Power System Intelligent Dispatch and Control Shandong UniversityJinan 250061Chinaand X.Zeng is also with the College of Electrical Engineering&New EnergyChina Three Gorges UniversityYichang 443002China Rundian Energy Science and Technology Co. Ltd.Zhengzhou 450046China

Anomaly detection based on the data collected from the supervisory control and data acquisition(SCADA)system is crucial to reduce the failure rate of wind turbines(WTs).The difficulty of this kind of methods is to dynamically identify the threshold for anomaly detection under changing operating *** this paper,a generalized WT anomaly detection method based on the combined probability estimation model(CPEM)is *** CPEM can estimate the conditional probability density function(PDF)of the target variable given changing *** generalization and accuracy are better than those of the independent probability estimation model because it combines the advantages of various kinds of probability estimation models through linear *** using the CPEM,the normal operating bounds under different operating conditions can be obtained,which dynamically form the thresholds for anomaly ***,with respect to the thresholds,hypothesis testing(HT)is adopted to identify the anomaly by inspecting whether the observations exceed the thresholds at a given significance level,providing sound mathematical support for anomaly detection and making the detection results more *** effectiveness of the proposed method is tested by using the actual data of WTs with known *** results illustrate that the proposed method can detect the abnormal operating state of the gearbox and generator much more early than the system fault alarm.

关键词： Anomaly detection combined probability estimation generalization hypothesis testing(HT)

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Dynamic Multivariate Alarm Threshold Optimization for Nonstationary Processes Subject to Varying Conditions 10

Dynamic Multivariate Alarm Threshold Optimization for Nonsta...

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10th IEEE Data Driven control and Learning Systems Conference, DDCLS 2021

作者： Zhao, Yi Zhao, Chunhui Sun, Youxian State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665424233

For nonstationary processes working upon varying conditions, the conventional alarm thresholds such as 3sigma rules configured for one single operational zone often result in false and missed alarms. Besides, the interactions among process variables are also isolated. To address these issues, this paper proposes a novel dynamic multivariate alarm threshold optimization algorithm. Firstly, the process correlation variations can be identified by Toeplitz inverse covariance-based clustering method, pointing to the changes of operating conditions. Each condition is structural interpreted by a covariance inverse matrix of the multivariate Gaussian distribution, revealing similar within-time and cross-time variable interactions. Therefore, it provides a promising foundation to capture the conditional Gaussian distribution and design the corresponding thresholds of each variable, which finely covers the current normal operational zone. Then, offline threshold optimization and online strategy are developed and discussed in details, which can timely adapt to varying conditions, promoting accurate and sensitive alarming performance. Finally, the experimental results on a real thermal power plant show that the proposed threshold can ease operators from nuisance, thus improving the reliability of alarm system. © 2021 IEEE.

关键词： Inverse problems

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The effects of prey lipid on female mating and reproduction of a wolf spider

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Current Zoology 2022年第6期68卷 726-733页

作者： Qingguo FENG Lelei WEN Jiayi MA Long YU Changchun LI Xiaoguo JIAO State Key Laboratory of Biocatalysis and Enzyme Engineering Center for Behavioral Ecology and EvolutionSchool of Life SciencesHubei UniversityWuhan 430062China Hubei Key Laboratory of Quality Control of Characteristic Fruits and Vegetables College of Life Science and TechnologyHubei Engineering UniversityXiaogan 432000China

As predators,the macronutrients spiders extract from their prey play important roles in their mating and *** studies of macronutrients on spider mating and reproduction focus on protein,the potential impact of prey lipid content on spider mating and reproduction remains largely ***,we tested the influence of prey varying in lipid content on female mating,sexual cannibalism,reproduction,and offspring fitness in the wolf spider Pardosa *** acquired 2 groups of fruit fly Drosophila melanogaster that differed significantly in lipid but not protein content by supplementing cultural media with a high or low dose of sucrose on which the fruit flies were reared(HL:high lipid and LL:low lipid).Subadult(i.e.,1 molt before adult)female spiders that fed HL flies matured with significantly higher lipid content than those fed LL *** found that the mated females fed with HL flies significantly shortened pre-oviposition time and resulted in a significantly higher ***,there was no significant difference in female spiders varying in lipid content on other behaviors and traits,including the latency to courtship,courtship duration,mating,copulation duration,sexual cannibalism,offspring body size,and ***,our results suggest that the lipid content of prey may be a limiting factor for female reproduction,but not for other behavioral traits in the wolf spiders ***.

关键词： lipid mating macronutrients Pardosa pseudoannulata reproduction survival

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Optimization Scheduling of Multi-Energy Microgrid Based on the QUBO Quantum Computing Model 19th

Optimization Scheduling of Multi-Energy Microgrid Based on t...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Baonan Wang, Hui Zhang, Dan College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9789819608966

Current optimization methods for microgrid scheduling face issues such as insufficient precision in energy distribution, high operational costs, and inefficiency. In response to these challenges, an optimization scheduling method for multi-energy microgrids based on the QUBO (Quadratic Unconstrained Binary Optimization) quantum computing model is proposed in this paper, alongside a discretization approach based on a grid partitioning strategy. To validate the feasibility of addressing microgrid optimization scheduling problems in a quantum computing environment, a QUBO model is constructed and simulation studies are conducted in the qbsolv environment provided by D-Wave. The results indicate that operational costs are significantly reduced with an increase in the number of grids, particularly a reduction of 4.52% when the number of grids increases from five to ten. This method not only effectively enhances the precision of electricity supply and energy utilization efficiency but also reduces unnecessary electrical waste and economic costs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Microgrids

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Multi-time Scale Scheduling of Gas System Based on Equipment Dynamic Modeling in Iron and Steel Industry

Multi-time Scale Scheduling of Gas System Based on Equipment...

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第43届中国控制会议

作者： Qi Wang Fan Zhou Ying Liu Zhongyang Han Jun Zhao Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology) Ministry of Education School of Control Science and Engineering Dalian University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the iron and steel industry, the optimal scheduling of by-product gas is crucial to achieve safe and efficient energy operation. However, the scheduling method with a single-time scale cannot cope with the impact of forecast uncertainty. It is difficult to use static modeling to solve the impact of the multi-working condition characteristics of energy coupling equipment and the complex and changeable state of energy on scheduling. For solving the above problems, a multi-time scale scheduling method based on equipment dynamic modeling is proposed in this paper. The upper layer is day-ahead scheduling layer, which considers long-term energy information to establish an economically optimal rolling optimization scheduling model. The lower layer is intraday real-time scheduling layer, which is based on the solution results of day-ahead scheduling and combined with short-time scale energy information. The dynamic model of each energy coupling equipment is used for real-time scheduling. In order to verify the validity of the proposed method in this paper, an experiment was carried out on the operation data of the by-product gas system in the iron and steel industrial industry. The results show that the proposed method reduces the operating cost and has better application value compared with the traditional MPC optimal scheduling method that does not consider the dynamic characteristics of equipment.

关键词： Iron and Steel Industry Dynamic Characteristics Multi-time Scale Scheduling Model Predictive control

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Dual-Path GAN:A Method for Enhancing Small-scale Defect Detection on Metal Images 41

Dual-Path GAN:A Method for Enhancing Small-scale Defect Dete...

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第41届中国控制会议

作者： Zhuoxun Ye Meiqin Liu Senlin Zhang Ping Wei State Key Laboratory of Industrial Control Technology Zhejiang University College of Electrical Engineering Zhejiang University Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Automated surface inspection(ASI) is an important research content in computer *** recent years,with the application of deep learning models represented by convolutional neural networks(CNN) in computer vision,surface defect detection based on computer vision has made impressive ***,compared with a few or dozens of pictures in real industrial scenes,traditional deep learning methods require a large amount of annotation data for training,so it is quite difficult to adapt to the complex industrial scenarios with varying surface profiles,lighting conditions,imaging angles and *** these difficulties,there is still a wide gap in the performance between the detection of small-scale and large-scale *** Generative Adversarial Nets(GANs) based augmentation method can only be used for classification networks or unsupervised learning,and if applied to detection networks,they need to be labeled,which requires much labor and *** order to solve these problems,a metal surface small-scale defect images augmentation method is *** method contains two parts:generation part and augmentation *** the generation part,two pairs of generators and discriminators are used to generate the defect areas and the background areas of the image,which can not only generate more realistic defect images than others but also save the training process for surface *** the augmentation part,the real defect images and the generated defect images are concatenated before copy-pasting them on background images to generate annotated dataset for *** conduct experiments on our metal surface *** experimental results show that our method can generate highquality defect samples and background samples,which greatly enriches the original *** evaluate different augmentation strategies,and ultimately,we achieve 6.2% improvement on baseline and 4.4% on copy-pasting method.

关键词： Generative adversarial nets automated surface inspection data augmentation few-shot learning small-scale object detection

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VFM-based modelling of burden descent under varying burden charging matrix for blast furnace

VFM-based modelling of burden descent under varying burden c...

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2021 China Automation Congress, CAC 2021

作者： Hao, Zheng Tang, Xiao-Yu Jin, Wangzhen Wang, Xin Yang, Chunjie State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665426473

The blast furnace (BF) is the main equipment in ironmaking industry in China. For the BF, the production and the smooth operation largely depend on a reasonable burden structure. In the burden structure modelling, the burden descent is the weak link due to the black-box nature of the burden descending process. The viscous flow model (VFM) is a widely used burden descent model with relatively high accuracy. However, when adopted to determine the burden structure, the VFM can only perform under a constant charging matrix. In this work, we incorporate the VFM into the burden structure model which is capable of utilizing varying charging matrixes. First, to incorporate the VFM, a new layout of the burden structure model is designed. Then, to address the multiple burden descents problem caused by the VFM, a new strategy implementing the VFM is proposed. With this strategy, the multiple burden descents are compact into one solution process, and the model efficiency is thereby increased. Finally, the case study on the performance of the proposed burden structure model is carried out. Results show that the proposed model is able to capture the layer details in the burden. Results also show that, unlike the VFM alone, this VFM-based model is able to utilize varying charging matrix and depict structure of each individual layer. © 2021 IEEE

关键词： Blast furnaces

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Data-driven soft sensors in blast furnace ironmaking:a survey

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Frontiers of Information technology & Electronic engineering 2023年第3期24卷 327-354页

作者： Yueyang LUO Xinmin ZHANG Manabu KANO Long DENG Chunjie YANG Zhihuan SONG State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Systems Science Kyoto UniversityKyoto 606-8501Japan Research Institute Baoshan Iron&Steel Co.Ltd.Shanghai 201900China

The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking *** sensors are a key technology for predicting molten iron quality indices reﬂecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic *** the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking *** review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking ***,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ***,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are ***,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.

关键词： Soft sensors Data-driven modeling Machine learning Deep learning Blast furnace Ironmaking

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Distributed model predictive control based on adaptive sampling mechanism

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Chinese Journal of Chemical engineering 2021年第11期34卷 193-204页

作者： Zhen Wang Aimin An Qianrong Li College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou 730050China National Experimental Teaching Center of Electrical and Control Engineering Lanzhou 730050China

In this work,an adaptive sampling control strategy for distributed predictive control is *** to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation ***,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period *** the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is *** an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled *** can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

关键词： Chemical process Distributed model predictive control Adaptive sampling mechanism Optimal sampling interval System dynamic behavior

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