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2024 China Automation Congress, CAC 2024

作者： Zou, Haipeng Ye, Quanxiang Li, Xiaoyu Kong, Xiangsong Zhao, Dan Shao, Zhijiang School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Zhangzhou Hongxingtai Electronics Co. Ltd China College of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350368604

Injection molding, as a plastic processing technology, is widely used in the mass production of plastic products with high precision and automation. However, the performance optimization process of this system currently faces some prominent issues, such as time consumption, reliance on experience, and difficulty in achieving optimality. Traditional optimization methods typically rely on manual experience and rules, which are inefficient and time-consuming. To enhance the performance of the control system, this paper focuses on utilizing an improved simplex search method based on the data-driven optimization concept to optimize the parameters of the temperature control system of the injection molding machine. The method introduced in this paper is termed the Knowledge-Informed Simplex Search Method Based on Adjacent Historical Centroid Points Information (IK-SS). The IK-SS algorithm is a variant of the simplex search method that utilizes information from the centroids of neighboring simplices to evaluate the current state of the optimization process and dynamically adjust the iteration step size. This approach aims to improve optimization efficiency while reducing computational complexity and time costs. Simulation and experimental validation demonstrate that the IK-SS algorithm significantly enhances the temperature control performance of the injection molding machine (IMM). This demonstrates the potential of the IK-SS algorithm as a tool for optimizing the temperature control system of injection molding machines. © 2024 IEEE.

关键词： Molding

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A Feature-Aided Multiple Model Algorithm for Maneuvering Target Tracking

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 566-568页

作者： Yiwei Tian Meiqin Liu Senlin Zhang Ronghao Zheng Shanling Dong College of Electrical Engineering Zhejiang University National Key Laboratory of Industrial Control Technology Zhejiang University IEEE National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University

Dear Editor,This letter deals with the tracking problem for non-cooperative maneuvering targets based on the underwater sensor networks. Considering the acoustic intensity feature of underwater targets, a feature-aide... 详细信息

关键词： underwater Aided letter

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Adsorption and membrane separation for removal and recovery of volatile organic compounds

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Journal of Environmental sciences 2023年第1期123卷 96-115页

作者： Guoqiang Gan Shiying Fan Xinyong Li Zhongshen Zhang Zhengping Hao State Key Laboratory of Fine Chemicals Key Laboratory of Industrial Ecology and Environmental Engineering(MOE)School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China National Engineering Laboratory for VOCs Pollution Control Material and Technology University of Chinese Academy of SciencesBeijing 101408China

Volatile organic compounds(VOCs)are a crucial kind of pollutants in the environment due to their obvious features of severe toxicity,high volatility,and poor *** is particularly urgent to control the emission of VOCs due to the persistent increase of concentration and the stringent *** China,clear directions and requirements for reduction of VOCs have been given in the“national plan on environmental improvement for the 13th Five-Year Plan period”.Therefore,the development of efficient technologies for removal and recovery of VOCs is of great *** technologies are favored by researchers due to their advantages in both recycling VOCs and reducing carbon *** them,adsorption and membrane separation processes have been extensively studied due to their remarkable industrial *** overview was to provide an up-to-date progress of adsorption and membrane separation for removal and recovery of ***,adsorption and membrane separation were found to be the research hotspots through bibliometric ***,a comprehensive understanding of their mechanisms,factors,and current application statuses was ***,the challenges and perspectives in this emerging field were briefly highlighted.

关键词： Volatile organic compounds(VOCs) Removal and recovery technology Adsorption Membrane separation Bibliometric analysis

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In-situ forming ultra-mechanically sensitive materials for high-sensitivity stretchable fiber strain sensors

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National science Review 2024年第6期11卷 254-263页

作者： Rouhui Yu Changxian Wang Xiangheng Du Xiaowen Bai Yongzhong Tong Huifang Chen Xuemei Sun Jing Yang Naoji Matsuhisa Huisheng Peng Meifang Zhu Shaowu Pan State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and Engineering Donghua University MOE Key Lab of Disaster Forecast and Control in Engineering School of Mechanics and Construction Engineering Jinan University State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular Science and Institute of Fiber Materials and Devices Fudan University Department of Cardiology Shanghai Xuhui Central HospitalZhongshan-Xuhui Hospital Fudan University Research Center for Advanced Science and Technology and Institute of Industrial Science The University of Tokyo

Fiber electronics with flexible and weavable features can be easily integrated into textiles for wearable applications. However, due to small sizes and curved surfaces of fiber materials, it remains challenging to load robust active layers, thus hindering production of high-sensitivity fiber strain sensors. Herein, functional sensing materials are firmly anchored on the fiber surface in-situ through a hydrolytic condensation *** anchoring sensing layer with robust interfacial adhesion is ultra-mechanically sensitive, which significantly improves the sensitivity of strain sensors due to the easy generation of microcracks during stretching. The resulting stretchable fiber sensors simultaneously possess an ultra-low strain detection limit of 0.05%, a high stretchability of 100%, and a high gauge factor of 433.6, giving 254-folds enhancement in sensitivity. Additionally, these fiber sensors are soft and lightweight, enabling them to be attached onto skin or woven into clothes for recording physiological signals, e.g. pulse wave velocity has been effectively obtained by them. As a demonstration, a fiber sensor-based wearable smart healthcare system is designed to monitor and transmit health status for timely intervention. This work presents an effective strategy for developing high-performance fiber strain sensors as well as other stretchable electronic devices.

关键词： fiber strain sensor stretchable wearable healthcare

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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

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Research on the protection mechanism of methane explosion in underground space by flexible construction

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Defence technology(防务技术) 2025年第4期46卷 169-183页

作者： Xianqi Duan Yulong Duan Zishuang Zhang Jun Long Yaqiao Yang Rui Lang College of Safety Engineering Chongqing University of Science and TechnologyChongqing 401331China Chongqing Key Laboratory for Oil and Gas Production Safety and Risk Control Technology Chongqing 401331China

Urban growth has promoted the use of underground spaces,where explosion accidents can be *** this study,we investigated the effect of placing flexible construction in front of rigid obstacles on methane explosion protection by using an experimental platform and adjusting the blockage rate and spacing of the *** aims to reduce the risk of gas explosions in urban underground *** results of the study show that the flame propagation peak speed and peak overpressure are reduced with the decrease in the blocking rate of the flexible obstacle when the blocking rate of the flexible obstacle is less than or equal to the blocking rate of the rigid obstacle,with the decrease in the spacing,the better the protection effect of the methane *** the blockage rate of the flexible obstacle is greater than the blockage rate of the rigid obstacle and spacing is less than the height of the flexible obstacle,rigid and flexible obstacles are connected as a whole,increasing the strength of the *** study can provide a theoretical basis and scientific guidance for optimizing rigid and flexible object hybrid layouts and methane explosion protection technology in urban underground spaces.

关键词： Explosion protection Flexible construction Urban underground space Methane explosions

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High-precision colorimetric-fluorescent dual-mode biosensor for detecting acetylcholinesterase based on a trimetallic nanozyme for efficient peroxidase-mimicking

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Journal of Materials science & technology 2024年第24期191卷 168-180页

作者： Meie Zheng Mingxing Liu Zichen Song Fei Ma Hongda Zhu Huiling Guo Hongmei Sun Cooperative Innovation Center of Industrial Fermentation(Ministry of Education&Hubei Province) National“111”Center for Cellular Regulation and Molecular PharmaceuticsKey Laboratory of Fermentation Engineering(Ministry of Education)Hubei Key Laboratory of Industrial MicrobiologyHubei University of TechnologyWuhan 430068China Hubei Key Laboratory of Quality Control of Characteristic Fruits and Vegetables College of Life Science and TechnologySchool of Mechanical EngineeringHubei Engineering UniversityXiaogan 432000China

Highly sensitive and stable acetylcholinesterase detection is critical for diagnosing and treating various neurotransmission-related *** this study,a novel colorimetric-fluorescent dual-mode biosensor based on highly dispersive trimetal-modified graphite-phase carbon nitride nanocomposites for acetylcholinesterase detection was designed and synthesized by phosphorus doping and a mixed-metal MOF *** specific surface area of trimetal-modified graphite-phase carbon nitride nanocomposites increased from 15.81 to 96.69 g m^(-2),and its thermal stability,interfacial charge transfer,and oxidation-reduction capability were enhanced compared with those of graphite-phase carbon ***-principles density functional theory calculations and steady-state kinetic analysis are applied to investigate the electronic structures and efficient peroxidase-mimicking properties of trimetal-modified graphite-phase carbon nitride *** oxidation of 3,3',5,5'-tetramethylbenzidin was inhibited by thiocholine,which originates from the decomposition of thiocholine iodide by Acetyl-cholinesterase(AChE),resulting in changes in fluorescence and absorbance *** to the independence and complementarity of the signals,a highly precise colorimetric-fluorescent dual-mode biosensor with a linear range for detecting AChE of 4-20μU mL^(-1) and detection limits of 0.13μU mL^(-1)(colorimetric)and 0.04μU mL^(-1)(fluorescence)was *** spiking recovery of AChE in actual samples was 99.0%-100.4%.Therefore,a highly accurate,specific,and stable dual-mode biosensor is available for AChE detection,and this biosensor has the potential for the analysis of other biomarkers.

关键词： Graphitic carbon nitride Zeolitic imidazolate framework-8 Colorimetric-fluorescent dual-mode sensor Density functional theory calculations Acetylcholinesterase

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Flexible Clockwork Recurrent Neural Network for multirate industrial soft sensor

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JOURNAL OF PROCESS control 2022年 119卷 86-100页

作者： [Anonymous] State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou 310027 China Zhejiang University NGICS Platform Hangzhou 310027 China

Data-driven-based soft sensors play significant roles in predicting key quality and optimizing the production process. Considering the difficulty and cost of variable acquisition, these variables are generally collected at different sampling rates. However, traditional soft sensor methods assume that data are uniformly sampled, which cannot be directly applied to multirate industrial scenarios. In this paper, a Flexible Clockwork Recurrent Neural Network (FCW-RNN) is proposed for multirate industrial soft sensors. First, the multirate data are divided into different variable groups according to their sampling rates. Then, an FCW-RNN is developed to map these variable groups into a common hidden space separately. A flexible clockwork mechanism is designed to incrementally update the hidden space at each moment based on the specific variable groups that have been sampled. Considering different sampling rates, the hidden space will be updated with time until all variable groups are sampled. In this way, we integrate the information of multirate data into the uniform hidden space step by step. Finally, a prediction module is established to calculate the hard-to-measure variables based on the hidden space. The effectiveness of FCW-RNN is demonstrated in a real coal mill case. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Multirate industrial processes Soft sensor Recurrent neural network Flexible clockwork mechanism

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Direct generation of Zn metal using laser-induced ZnS to eradicate carbon emissions from electrolysis Zn production

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Frontiers of Environmental science & engineering 2024年第1期18卷 83-95页

作者： Ying Chen Ning Duan Linhua Jiang Fuyuan Xu Guangbin Zhu Yao Wang Yong Liu Wen Cheng Yanli Xu State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China School of Environmental Science and Engineering Tianjin UniversityTianjin 300350China School of Materials Science and Engineering Anhui University of Science&TechnologyHuainan 232001China

In response to the goal of net-zero emissions proposed by Intergovernmental Panel on Climate Change, Chinese government has pledged that carbon emissions will peak by 2030, and achieve carbon neutrality by 2060. However, the high carbon energy structure of traditional industries has aggravated environmental problems, such as greenhouse effect and air pollution. The goal of carbon neutrality will be difficult to achieve without the development of disruptive theories and technologies. The electrolytic zinc industry requires high-temperature roasting at ~1000 ℃, generating large amounts of greenhouse gases and SO_(2). High concentrations of sulfuric acid (200 g/L) are subsequently used for electrolysis, and each ton of zinc produced generates 50 kg of anode slime with lead content of up to 16%, as well as 0.35 m3 of wastewater containing zinc and lead. To solve these problems, an optical metallurgy method is proposed in this study. The proposed method uses laser-induced photoreduction to decompose ZnS and reduce metal ions to metal. Results indicate that Zn0 and S8 can be detected on the surface of ZnS at a specific wavelength and laser fluence. The generation mechanism of Zn0 is such that laser induces an electronic transition that breaks ionic bond in ZnS, resulting in its decomposition and photoreduction to Zn0 under an inert argon gas atmosphere. This method does not reduce other metals in the mineral since it does not use high-temperature roasting, providing a new way of producing high-purity metal without greenhouse gas emissions and heavy metal pollution caused by traditional zinc electrolysis.

关键词： Laser metallurgy ZnS Photochemical reduction Zinc

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Detection of Oscillations in Process control Loops From Visual Image Space Using Deep Convolutional Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 982-995页

作者： Tao Wang Qiming Chen Xun Lang Lei Xie Peng Li Hongye Su the Department of Electronic Engineering the School of InformationYunnan UniversityKunming 650091China DAMO Academy Alibaba GroupHangzhou 311100China the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant *** numerous automatic detection techniques have been proposed,most of them can only address part of the practical *** oscillation is heuristically defined as a visually apparent periodic ***,manual visual inspection is labor-intensive and prone to missed *** neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction *** this work,an exploration of the typical CNN models for visual oscillation detection is ***,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation *** feasibility and validity of this framework are verified utilizing extensive numerical and industrial *** with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and *** addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

关键词： Convolutional neural networks(CNNs) deep learning image processing oscillation detection process industries

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