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Electroreduction of hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a back propagation neural network

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Frontiers of Environmental science & engineering 2023年第8期17卷 69-79页

作者： Xinwan Zhang Guangyuan Meng Jinwen Hu Wanzi Xiao Tong Li Lehua Zhang Peng Chen National Engineering Laboratory for Industrial Wastewater Treatment School of Resources and Environmental EngineeringEast China University of Science and TechnologyShanghai 200237China Continuing Education Center Bozhou UniversityBozhou 236800China State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process East China University of Science and TechnologyShanghai 200237China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Flow-through electrodes have been demonstrated to be effective for electroreduction of Cr(VI),but shortcomings are tedious preparation and short ***,porous titanium available in the market was studied as a flow-through electrode for Cr(VI)*** addition,the intelligent prediction of electrolytic performance based on a back propagation neural network(BPNN)was *** studies revealed that Cr(VI)electroreduction was a diffusion-controlled *** of the flow-through mode achieved a high limiting diffusion current as a result of enhanced mass transfer and favorable *** of Cr(VI)in the flow-through system was 1.95 times higher than in a parallel-plate electrode *** the influent(initial pH 2.0 and 106 mg/L Cr(VI))was treated at 5.0 V and a flux of 51 L/(h·m2),a reduction efficiency of~99.9%was obtained without cyclic electrolysis *** served as the supporting electrolyte and pH regulator,as reactive CrSO72−species were formed as a result of feeding HSO4−.Cr(III)was confirmed as the final product due to the sequential three-electron transport or disproportionation of the *** developed BPNN model achieved good prediction accuracy with respect to Cr(VI)electroreduction with a high correlation coefficient(R2=0.943).Additionally,the electroreduction efficiencies for various operating inputs were predicted based on the BPNN model,which demonstrates the evolutionary role of intelligent systems in future electrochemical technologies.

关键词： Flow-through electrode Hexavalent chromium Heavy metals Neural network Artificial intelligence

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Hydrophilic polyanionic hydrogel electrolyte for anti-freezing and bending resistant zinc-ion hybrid supercapacitors

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Journal of Materiomics 2024年第6期10卷 1299-1307页

作者： Xuejuan Wan Hangqi Song Zejia Zhao Zuocai Zhang Jingwei Wang Junye Cheng Shenzhen Key Laboratory of Polymer Science and Technology Guangdong Research Center for Interfacial Engineering of Functional MaterialsCollege of Materials Science and EngineeringShenzhen UniversityShenzhen518060China Department of Materials Science Shenzhen MSU-BIT UniversityShenzhen517182China Institute of Semiconductor Manufacturing Research College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen518060China

Zinc-ion hybrid supercapacitors(ZHSCs)have been widely considered as promising candidates for flexible electrochemical energy storage *** key challenge is to develop hydrogel electrolytes with high hydrophilicity,anti-freezing,bending resistance,and stable interface with *** study reported a hydrogel electrolyte system that can meet the above functions,in which the zincophilic and negatively charged SO_(3)^(−),migratable Na^(+),abundant hydrophilic functional groups,gum xanthan,and porous architecture could effectively promote the electrochemical performance of *** with such hydrogel electrolytes not only exhibited good low-temperature performance but also showed excellent bending resistance ability.A high specific capacitance could be kept after a long air-working lifespan over 10,000 cycles under a wide operation voltage of 1.85 V at−10℃.Furthermore,flexible ZHSCs could maintain the capacitance retention of 93.18%even after continuous 500 bends at an angle of 180°.The designed hydrogel electrolytes could be also used for other electrochemical energy storage devices with anti-freezing and bending resistance by changing electrolyte salt.

关键词： Zn-ion hybrid supercapacitors Polyanionic Anti-freezing Bending resistance Porous architecture

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Research on river surface velocimetry monitoring method Based on video surveillance

Research on river surface velocimetry monitoring method Base...

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第35届中国过程控制会议

作者： Jiaqi Xu Yining Zhao Xinao Jin Yuqi Cao Guangxin Zhang Dibo Hou State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University

The allocation and utilization of water resources have always been the focus of social development concerns. River flow velocity is the basis for flow rate estimation and is very important hydrological information. The most widely used contact flowmeters require manual wading to place the instrument in water, making them difficult to deploy over large areas. In this paper, an optical flow estimation method of river surface velocity detection based on RAFT is constructed to obtain flow velocity and direction information. To solve the interference problem caused by flying insects and birds, a flow velocity and direction outlier identification method based on Gaussian Mixture Model is investigated. Results demonstrate that the method can effectively and accurately realize real-time monitoring of river surface velocity, providing important information for water allocation, flood prevention and control.

关键词： River Surface Velocity Optical Flow Estimation Adaptive Histogram Equalization Gaussian Mixture Model Pixel-Based Adaptive Segmenter

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Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 2011-2026页

作者： Xiaolong Chen Biao Xu Manjiang Hu Yougang Bian Yang Li Xin Xu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082 Wuxi Intelligent Control Research Institute(WICRI)of Hunan University Wuxi 214115China College of Intelligence Science and Technology Institute of Unmanned SystemsNational University of Defense TechnologyChangsha 410073China

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and *** paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and *** method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at *** algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization *** proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning *** intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic *** algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

关键词： Autonomous driving decision-making reinforcement learning(RL) unsignalized intersection

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Screening of indicator pharmaceuticals and personal care products in landfill leachates: a case study in Shanghai, China

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Frontiers of Environmental science & engineering 2023年第9期17卷 245-256页

作者： Xiping Kan Xia Yu Wentao Zhao Shuguang Lyu Shuying Sun Gang Yu Qian Sui State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process School of Resources and Environmental EngineeringEast China University of Science and TechnologyShanghai 200237China State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China Advanced Interdisciplinary Institute of Environment and Ecology Beijing Normal University at ZhuhaiZhuhai 519087China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Identifying potential sources of pharmaceuticals and personal care products(PPCPs)in the environment is critical for the effective control of PPCP *** leachate is an important source of PPCPs in water;however,it has barely been involved in source apportionment due to the lack of indicator-PPCPs(i-PPCPs)in landfill *** study provides the first systematic framework for identifying i-PPCPs for landfill leachates based on the wide-scope target monitoring of *** number of target PPCPs increased from<20 in previous studies to 68 in the present ***-nine PPCPs were detected,with median concentrations in leachate samples ranging from below the method quantification limit(MQL)to 41μg/L,and 19 of them were rarely reported previously.A total of 29 target compounds were determined to be PPCPs of high concern by principal component analysis according to multiple criteria,including occurrence,exposure potential,and ecological *** with source-specificity and representativeness analysis,erythromycin,gemfibrozil,and albendazole showed a significant difference in their occurrence in leachate compared to other potential sources(untreated and treated municipal wastewater and livestock wastewater)and correlated with total PPCP concentrations;these were recommended as i-PPCPs for *** screening procedure can be used to develop a sophisticated source apportionment method to identify sources of PPCPs from adjacent landfills.

关键词： Landfill leachates PPCPs Indicator Screening criteria Source-specificity

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Separation of oil debris aliasing signals based on fractional-order integral filter and auto encoder

Separation of oil debris aliasing signals based on fractiona...

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第35届中国过程控制会议

作者： Minglv Jin Ning Wang Baiyu Zhu Dibo Hou Pingjie Huang Hongjian Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University

Inductive oil debris monitoring has played an important role in monitoring the working conditions of mechanical systems. However, when two or more wear debris pass through the sensor at a close distance, the sensor's signal experiences aliasing, impacting the peak-to-peak value of the wear particle signals, which can lead to false alarms from the sensor. A framework is proposed for separating aliased signals, which includes a fractional-order integral filter and a model based on auto encoder and convolutional neural networks. Experimental results show that, compared to the unseparated signals, the method proposed in this paper reduces the average error rate of peak-to-peak value from 53.05% to 17.21%.

关键词： aliasing signal separation oil debris sensor auto encoder convolutional neural networks

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Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1465-1476页

作者： Donglei Zheng Le Zhou Zhihuan Song State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Automation and Electrical Engineering Zhejiang University of Science&TechnologyHangzhou 310023China

In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling *** build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and *** this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling *** the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)***,the corresponding fault detection methods based on the nonlinear features are ***,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.

关键词： Fault detection kernel method multi-rate process probability principal component analysis(PPCA)

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Fine-grained Defect Information Prediction Based key Factors Identification Method for Strip Steel Manufacturing

Fine-grained Defect Information Prediction Based Key Factors...

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第35届中国过程控制会议

作者： Jinchuan Qian Xuerui Zhang Songyan Pi Zhihuan Song Bojian Chen Xinmin Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University

The surface defect is a key factor affecting the quality of steel products, and have attracted great attention in practical strip steel manufacturing. To accurately analyze key factor for improving product quality, this paper proposes a fine-grained defect information prediction based key factors identification method(FDP). The training of the proposed FDP is supervised by fine-gained defect-related information, which is obtained by a designed defect rate calculation strategy. In addition, after combing with data mode clustering, the final identification results are presented by a two-stage explainable method indicated by importance scores.

关键词： Explainable AI Surface Defects Steelmaking Process key Factors Identification

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Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method

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Chinese Journal of Aeronautics 2022年第9期35卷 314-332页

作者： Yanhua MA Xian DU Ximing SUN School of Microelectronics Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of EducationDalian University of TechnologyDalian 116024China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.

关键词： Hybrid optimization Long short-term memory Model modification and testing Sensitivity analysis Turbofan engines

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Round-robin Multivariate State Estimation Fault Detection Method based on Cross-domain Collaboration Learning

Round-robin Multivariate State Estimation Fault Detection Me...

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第35届中国过程控制会议

作者： Yan Liu Yiyang Shou Zuhua Xu Jun Zhao Chunyue Song State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University

industrial process data exhibits multi-mode and nonstationary characteristics in the spatial and temporal domain, causing unsatisfactory performance of conventional multivariate statistical fault detection methods. Therefore, we propose a novel round-robin multivariate state estimation fault detection method based on cross-domain collaboration learning, which aims to fully excavate the collaborative information for accurate fault detection. Moreover, to overcome the underdetermined problem of conventional multivariate state estimation fault detection methods, the round-robin multivariate state estimation model in collaborative feature space is constructed for fault detection. Simulation on the Tennessee Eastman process has validated the effectiveness of the proposed method.

关键词： Fault detection Feature collaboration learning Cross-domain transfer network Round-robin multivariate state estimation method

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