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Chinese Journal of Chemical Engineering 2023年第5期57卷 151-161页

作者： Xiongzhuo Zhu Dali Gao Chong yang chunjie yang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace ***, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify *** with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

关键词： Chemical processes Principal component analysis Gaussian mixture model Process monitoring Ensemble Process control

Coordination Control for a Class of Multi-Agent Systems Under Asynchronous Switching

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Journal of Systems Science & Complexity 2019年第4期32卷 1019-1038页

作者： ZHAO Xiaodan LIU Wenhui yang chunjie College of Automation Hangzhou Dianzi UniversityHangzhou 310027China Big Date Decision Institute Jinan UniversityGuangzhou 510632China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

This paper studies the coordination control of nonlinear multi-agent systems under asynchronous switching,including consensus,tracking control,and *** asynchronous switching considered here means that the switching of the controller lags behind the mode’s switching for each *** the matched controller is interrupted by the delayed *** the situation,the authors give some new results by applying the conventional distributed control *** authors show that all agents can achieve ***,the authors show that all followers can track the actual ***,the authors show that all followers will converge to the convex hull spanned by the dynamic leaders as time goes *** simulations are also provided and the results show highly consistent with the theoretical results.

关键词： Asynchronous switching consensus containment coordination control switched systems tracking control

Intelligent Ironmaking Optimization Service on a Cloud Computing Platform by Digital Twin

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Engineering 2021年第9期7卷 1274-1281页

作者： Heng Zhou chunjie yang Youxian Sun The State Key Laboratory of Industrial Control Technology&College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial *** improve the operational levels of the process industries,we propose a multi-objective optimization framework based on cloud services and a cloud distribution ***-time data from manufacturing procedures are first temporarily stored in a local database,and then transferred to the relational database in the ***,a distribution system with elastic compute power is set up for the optimization ***,a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking *** the application of this optimization service in a cloud factory,iron production was found to increase by 83.91 t∙d^(-1),the coke ratio decreased 13.50 kg∙t^(-1),and the silicon content decreased by an average of 0.047%.

关键词： Cloud factory Blast furnace Multi-objective optimization Distributed computation

Minimax entropy-based co-training for fault diagnosis of blast furnace

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Chinese Journal of Chemical Engineering 2023年第7期59卷 231-239页

作者： Dali Gao chunjie yang Bo yang Yu Chen Ruilong Deng The State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Jianwei Digital Sphere Co. LtdLianyungang 222113China Jiangsu Binxin Iron and Steel Group Co. LtdChongqing 401220China

Due to the problems of few fault samples and large data fluctuations in the blast furnace(BF)ironmaking process,some transfer learning-based fault diagnosis methods are *** vast majority of such methods perform distribution adaptation by reducing the distance between data distributions and applying a classifier to generate pseudo-labels for ***,since the training data is dominated by labeled source domain data,such classifiers tend to be weak classifiers in the target *** addition,the features generated after domain adaptation are likely to be at the decision boundary,resulting in a loss of classification ***,we propose a novel method called minimax entropy-based co-training(MMEC)that adversarially optimizes a transferable fault diagnosis model for the *** structure of MMEC includes a dual-view feature extractor,followed by two classifiers that compute the feature's cosine similarity to representative vector of each *** transfer is achieved by alternately increasing and decreasing the entropy of unlabeled target samples with the classifier and the feature extractor,*** BF fault diagnosis experiments show that our method improves accuracy by about 5%over state-of-the-art methods.

关键词： Co-training Fault diagnosis Blast furnace Minimax entropy Transfer learning

Observer-Based Event-Triggered Tracking Control of Leader-Follower Systems with Time Delay

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Journal of Systems Science & Complexity 2016年第4期29卷 865-880页

作者： LIU Wenhui yang chunjie SUN Youxian QIN Jiaxiang College of Control Science and Engineering Zhejiang University

This paper studies the tracking control of general linear multi-agent systems with out time delays. The observer-based event-triggered control schemes will be considered or with- For the conventional distributed tracking protocol, the authors will not update the relative state in continuous time, i.e., the relative state will be updated by some events which happened in discrete time. A completely decentralized event-trigger will be designed for leader-follower systems. It is shown that all followers can track the leader asymptotically by the proposed protocol, if the feedback gain matrices are designed appropriately. Numerical simulations are also provided and the results show highly consistent with the theoretical results.

关键词： Consensus distributed control event-triggered control multi-agent systems time delay tracking control.

Dust Distribution Study at the Blast Furnace Top Based on k-Sε-u_(p)Model

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 121-135页

作者： Zhipeng Chen Zhaohui Jiang chunjie yang Weihua Gui Youxian Sun the School of Automation Central South UniversityChangsha 410083China the School of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

The dust distribution law acting at the top of a blast fumace(BF)is of great significance for understanding gas flow distribution and mitigating the negative influence of dust particles on the accuracy and service life of detection *** harsh environment inside a BF makes it difficult to describe the dust *** paper adresses this problem by proposing a dust distribution k-Sε-u_(p)model based on interphase(gas-powder)*** proposed model is coupled with a k-Sεmodel(which describes gas flow movement)and a u_(p)model(which depicts dust movement).First,the kinetic energy equation and turbulent dissipation rate equation in the k-Sεmodel are established based on the modeling theory and single Green-function two scale direct interaction approximation(SGF-TSDIA)***,a dust particle mnovement u_(p)model is built based on a force analysis of the dust and Newton's laws of ***,a coupling factor that descibes the interphase interaction is proposed,and the k-Sε-u_(p)model,with clear physical *** mathematical logic,and adequate generality,is *** results and o-site verification show that the k-Sε-u_(p)model not only has high precision,but also reveals the aggregate distribution features of the dust,which are helpful in optimizing the installation position of the detection equipment and imnproving its accuracy and service life.

关键词： Blast furnace(BF) dust movement interphase interaction modeling theory turbulent flow two-scale direct interaction approximation(TSDIA)

Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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Science China(Information Sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG chunjie yang Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence

Multi-objective Optimization of Operating Parameters Based on Neural Network and Genetic Algorithm in the Blast Furnace 36

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Multi-objective Optimization of Operating Parameters Based o...

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

第36届中国控制会议

作者： Heng Zhou chunjie yang Tian Zhuang Zelong Li Yuxuan Li Lin Wang College of Control Science and Engineering State Key Laboratory of Industrial Control TechnologyZhejiang University

ISBN: (纸本)9781538629185

Addressing the complexity and isolation of the blast furnace, field engineers generally operate the system according to their former experience. While stability and safety are the first priority, it is natural to see extra consumption of ores and *** the recent years, researchers have been searching for the optimal operation point within the blast furnace by mathematical methods that include mechanism and data-driven modeling. The reported models have seldom succeeded in offering proper operation parameters to optimize several goals at the same time, as the factors of the blast furnace are highly correlative, which cannot be analyzed separately. In order to get the conflicting targets such as the lowest production cost and coke ratio as well as the highest iron quality, a multi-objective optimization model for the blast furnace based on BP neural network and genetic algorithm is proposed. The model is basically constructed on two theories. One is the BP neural network which can provide a mapping rule for multiple inputs and multiple outputs, the other is the genetic algorithm whose core contents are NSGA-Ⅱ algorithm and Pareto optimality that are fit for the multi-objective optimization goal. By optimizing the blast furnace system with this model, the plant is feasible to keep the iron making process efficient and stable simultaneously.

关键词： Multi-objective Optimization Neural Network Genetic Algorithm NSGA-Ⅱ Pareto Optimality

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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

Itinerant ferromagnetism entrenched by the anisotropy of spin-orbit coupling in a dipolar Fermi gas

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Frontiers of physics 2023年第5期18卷 227-235页

作者： Xue-Jing Feng Jin-Xin Li Lu Qin Ying-Ying Zhang ShiQiang Xia Lu Zhou chunjie yang ZunLue Zhu Wu-Ming Liu Xing-Dong Zhao School of Physics Henan Normal UniversityXinxiang 453000China Department of Physics School of Physics and Electronic ScienceEast China Normal UniversityShanghai 200241China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China

We investigate the itinerant ferromagnetism in a dipolar Fermi atomic system with the anisotropic spin–orbit coupling(SOC),which is traditionally explored with isotropic contact *** first study the ferromagnetism transition boundaries and the properties of the ground states through the density and spin-flip distribution in momentum space,and we find that both the anisotropy and the magnitude of the SOC play an important role in this *** propose a helpful scheme and a quantum control method which can be applied to conquering the difficulties of previous experimental observation of itinerant *** further study reveals that exotic Fermi surfaces and an abnormal phase region can exist in this system by controlling the anisotropy of SOC,which can provide constructive suggestions for the research and the application of a dipolar Fermi ***,we also calculate the ferromagnetism transition temperature and novel distributions in momentum space at finite temperature beyond the ground states from the perspective of experiment.

关键词： itinerant ferromagnetism spin-orbit coupling cold atom quantum simulation dipolar Fermi gas dipole-dipole interaction

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