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Efficient H_(2)O_(2)Electrosynthesis and Its Electro-Fenton Application for Refractory Organics Degradation

engineering 2023年第11期30卷 131-143页

作者： Lei Li Jing Bai Panyu Jiang Yan Zhang Tingsheng Zhou Jiachen Wang Changhui Zhou Jinhua Li Baoxue Zhou School of Environmental Science and Engineering Key Laboratory of Thin Film and Microfabrication Technology(Ministry of Education)Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Hydrogen peroxide(H_(2)O_(2))in situ electrosynthesis by O_(2)reduction reaction is a promising alternative to the conventional Fenton treatment of refractory ***,O_(2)mass transfer limitation,cathodic catalyst selectivity,and electron transfer in O_(2)reduction remain major engineering ***,we have proposed a systematic solution for efficient H_(2)O_(2)generation and its electro-Fenton(EF)application for refractory organic degradation based on the fabrication of a novel ZrO_(2)/CMK-3/PTFE cathode,in which polytetrafluoroethylene(PTFE)acted as a hydrophobic modifier to strengthen the O_(2)mass transfer,ZrO_(2)was adopted as a hydrophilic modifier to enhance the electron transfer of O_(2)reduction,and mesoporous carbon CMK-3 was utilized as a catalyst substrate to provide catalytic active ***,feasible mass transfer of O_(2)from the hydrophobic to the hydrophilic layer was designed to increase the contact between O_(2)and the reaction *** H_(2)O_(2)yield of the ZrO_(2)/CMK-3/PTFE cathode was significantly improved by approximately 7.56 times compared to that of the co nventional gas diffusion cathode under the same *** H_(2)O_(2)generation rate and Faraday efficiency reached125.98 mg·cm^(-2)·h^(-1)(normalized to 5674.04 mmol·g^(-1)·h^(-1)by catalyst loading)and 78.24%at-1.3 V versus standard hydrogen electrode(current density of-252 mA·cm^(-2)),*** high H_(2)O_(2)yield ensured that sufficient OH was produced for excellent EF performance,resulting in a degradation efficiency of over 96%for refractory *** study offers a novel engineering solution for the efficient treatment of refractory wastewater using EF technology based on in situ high-yield H_(2)O_(2)electrosynthesis.

关键词： Hydrogen peroxide Hydrophilic/hydrophobic interface modification Electro-Fenton Refractory organics

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Research on the Prediction of Academic Performance Based on ANN-BiLSTM Hybrid Neural Network Model 4

Research on the Prediction of Academic Performance Based on ...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Song, Wei Xing, Jiaqi Ning, Keqing Guo, Wei North China University of Technology School of Information Science and Technology Beijing China North China University of Technology School of Electrical and Control Engineering Beijing China

ISBN: (纸本)9798350363586

With the popularity of online courses and e-learning, a large amount of data on online learning behaviour has been accumulated. How to use these data to predict early students' performance so as to improve teaching effectiveness and learning experience has become an important issue in educational research. Based on this, this paper constructs a hybrid ANN-BiLSTM neural network model by taking advantage of the feature extraction of Artificial Neural Network (ANN) and the advantages of Bi-directional Long Short-Term Memory (BiLSTM) in temporal feature processing. In order to verify the effectiveness of this model, this paper compares it with the Recurrent Neural Network (RNN) model, the Gate Recurrent Unit (GRU), and the ANN-LSTM hybrid neural network (ANN-LSTM) model. Experiments on the UK Open University Learning Analytics dataset showed that the ANN-BiLSTM model had significantly better prediction accuracy and the best predictive performance. © 2024 IEEE.

关键词： Neural network models

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Molecular Mechanism Behind the Capture of Fluorinated Gases by Metal–Organic Frameworks

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Nano-Micro Letters 2025年第5期17卷 407-462页

作者： Qian Wang Yong Hu Yifan Gu College of Environmental Science and Engineering State Key Laboratory of Pollution Control and Resource ReuseTongji UniversitySiping Rd 1239Shanghai 200092People’s Republic of China Department of Polymeric Materials School of Materials Science and EngineeringTongji UniversityCaoan Road 4800Shanghai 201804People’s Republic of China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092People’s Republic of China Key Laboratory of Cities’Mitigation and Adaptation to Climate Change China Meteorological Administration(CMA)Tongji UniversityShanghai 200092People’s Republic of China

Fluorinated gases(F-gases)play a vital role in the chemical industry and in the fields of air conditioning,refrigeration,health care,and organic ***,the direct emission of waste gases containing F-gases into the atmosphere contributes to greenhouse effects and generates toxic *** porous materials for the energy-efficient capture,separation,and recovery of F-gases is highly ***,as a highly designable porous adsorbents,metal–organic frameworks(MOFs)exhibit excellent selective sorption performance toward F-gases,especially for the recognition and separation of different F-gases with highly similar properties,showing their great potential in F-gases control and *** this review,we discuss the capture and separation of F-gases and their azeotropic,near-azeotropic,and isomeric mixtures in various application scenarios by MOFs,specifically classify and analyze molecular interaction between F-gases and MOFs,and interpret the mechanisms underlying their high performance regarding both adsorption capacity and selectivity,providing a repertoire for future materials *** faced in the transformation research roadmap of MOFs adsorbent separation technologies toward F-gases are also discussed,and areas for future research endeavors are highlighted.

关键词： Fluorinated gas Metal–organic framework Adsorption Separation Molecular interaction

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A Novel Algorithm for Bayesian Network Parameter Learning with Informative Range Constraints and Sorting Model 41

A Novel Algorithm for Bayesian Network Parameter Learning wi...

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第41届中国控制会议

作者： Wenqiang Guo Lei Hao Jianwang Li Yongyan Hou Qinkun Xiao Zixuan Huang Wei Li School of Information Engineering & Artificial Intelligence Shaanxi University of Science & Technology School of Electrical & Control Engineering Shaanxi University of Science & Technology College of Electronics & Information Xi'an Technological University School of Electronics and Control Engineering Chang'An University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Learning the parameters of Bayesian networks(BNs) is a key challenge in real-world decision support applications,especially when there are small data *** A Posteriori(MAP) estimation is alternate way to compute the *** the hyper parameters in MAP are difficult to determine when the samples are small.A conventional way to address this challenge is to introduce domain knowledge/expert judgments that are encoded as qualitative parameter *** paper proposed a Sorting Constraint Auxiliary Model(SCAM) algorithm for learning BN *** firstly translate informative range constraints into mathematics inequality formulation with regards to the relationships among BN parameters by *** we recruit conjugate distribution fitting using equivalence uniform distribution to generate virtual samplings in parameter *** sampling counts,combined with small data statistics,help to reconstruct the BN parameter in MAP *** experimental results show that the SCAM algorithm can obtain more accurate parameters than the existing parameter learning ***,we demonstrate successful application to real threat assessment case studies in case of scarce data.

关键词： Bayesian networks parameter learning sorting constraint distribution fitting

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A Detection Algorithm Based on Improved Faster R-CNN for Spacecraft Components

A Detection Algorithm Based on Improved Faster R-CNN for Spa...

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2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Wang, Zongqi Cao, Yue Li, Jun School of Control Science and Engineering Shandong University Jinan China School of Electronic Information and Electronic Engineering Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9798350314670

Due to the increasing need of science and technology, more and more spacecraft are in space. Once the spacecraft reaches its useful life, it will become floating junk in space. Active space debris removal technology is an effective method to solve space garbage. With the rapid iteration of deep learning methods, target detection technology in the field of computer vision has made a breakthrough. In this paper, a Faster R-CNN model based on RegNet as the main trunk network is proposed, which can reduce the loss of key information caused by R-CNN sampling and effectively improve the target detection effect. We will collect the photos of space spacecraft as training sets and verification sets sent to the network for iterative training. The test results of the data set in this paper show that: With RegNet as the main backbone network, the Faster R-CNN model has better test results than other target detection algorithms under the same parameters. Compared with other backbone networks added to Faster R-CNN, the mAP value is improved by at least 9.6%. Compared with other advanced target detection algorithms, mAP value increased by at least 26.3%. When processing low Earth orbit target identification tasks, the Faster R-CNN model based on RegNet as the main trunk network has a strong recognition ability for the image targets whose color is similar to the background color, relatively fuzzy and small scale. © 2023 IEEE.

关键词： Spacecraft

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A Novel Defect Inspection Approach Based on Self-attention Convolutional Adversarial Auto-Encoder 5

A Novel Defect Inspection Approach Based on Self-attention C...

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5th International Conference on Robotics, control and Automation engineering, RCAE 2022

作者： Wang, Jian Li, Yakun Han, Zhiyan School of Control Science and Engineering Bohai University Jinzhou China

ISBN: (纸本)9781665459419

Defect inspection based on computer vision has gradually replaced the traditional detection method, which is widely used in industry and greatly improving the efficiency of industrial production. Most defect inspection methods use supervised learning, which require vast amounts of label images for training. However, the complete defect inspection task is challenging due to some problems, such as diversity of defects, high similarity defects (e.g., low contrast, ambiguous boundary and small background difference) and large defect scale. To solve the above problems, a novel unsupervised defect inspection approach with Self-attention Convolutional Adversarial Auto-Encoder (SCAAE) is proposed in this paper, based on the encoder-decoder-discriminator structure. In this approach, we improve the encoder and decoder via the self-attention mechanism to enhance the feature extraction ability of convolutional autoencoder (CAE), and then the discriminator helps SCAAE impose the latent variable to cluster by a prior distribution. Finally, large number of experiments on four datasets demonstrate the effectiveness of SCAAE and outperforms state-of-the-art methods. © 2022 IEEE.

关键词： Defects

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Hybrid Identification with Multi-Innovation Method Based on Fractional-Order Nonlinear State-Space Model 42

Hybrid Identification with Multi-Innovation Method Based on ...

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42nd Chinese control Conference, CCC 2023

作者： You, Enning Wang, Hongewei Liu, Chunlei Zhang, Qian School of Control Science and Engineering Dalian University of Technology Dalian110024 China School of Electrical Engineering Xinjiang University Urumqi830047 China

ISBN: (纸本)9789887581543

For Hammerstein system with fractional linear state space model, it is very difficult to build its model by identification method. The difficulty lies in that the nonlinear state space equation model describing the system contains unknown model parameters, fractional order of the system and outside noise. In order to overcome these difficulties, we transform the fractional system from an input nonlinear pseudo-state space model to an input-output regression model, and estimate the model parameters with multi-innovation recursive gradient descent algorithm, and confirm the fractional order with the multi-innovation Levenberg-Marquardt algorithm. Finally, the effectiveness of the algorithm is verified by a numerical example. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Nonlinear equations

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Humanoid Biped Robot Locomotion via Quadratic Programming with Compound Constraints

Humanoid Biped Robot Locomotion via Quadratic Programming wi...

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2022 International Symposium on Biomimetic Intelligence and Robotics, ISBIR 2022

作者： Liu, Hongji Zhu, Zhengguo Sun, Zhongkai Li, Yibin Rong, Xuewen Chen, Teng Zhang, Guoteng School of Control Science and Engineering Shandong University Jinan China

This paper introduces a quadratic programming-based stable motion control method for humanoid robots. This method is based on the virtual model and the optimal force distribution. We added a six-dimensional mixed constraint on the sole of the foot to prevent the robot from sliding and overturning. At the same time, a more comprehensive swing planning and pre-touchdown planning were added when the robot's legs were swinging to improve the stability of the robot's walking on the ground. Finally we verified the algorithm in the robot simulation software and proved the feasibility of the algorithm. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and Robotics.

关键词： Anthropomorphic robots

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Research on Cooperative control Technology of New Energy Fusion Energy Storage System

Research on Cooperative Control Technology of New Energy Fus...

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2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Tian, Biao Zhang, Yun Chao, Mengyao Jia, Zimeng Zhang, Kailun School of Mechano-Electronic Engineering Xidian University Shaanxi China School of Electrical and Control Engineering Xi'an University of Science and Technology Shaanxi China

ISBN: (纸本)9798350318012

With the rapid development of global industry, photovoltaic (PV) power generation has become a research hotspot for new energy applications. Due to the limitations of the environment, the output power of PV power generation is random and fluctuating, and if directly connected to the grid, it will have a greater impact on the stability of the microgrid and power quality. The global maximum power tracking algorithm is constructed by combining artificial intelligence algorithm to track the maximum power point of PV output;based on the operating characteristics of both battery and supercapacitor, the two-stage power allocation control strategy based on fuzzy control algorithm is put forward, which improves the dynamic response speed of the system and ensures the cycle life of the system at the same time. The effectiveness of the strategy is verified by building a simulation model through Simulink. © 2023 IEEE.

关键词： Fuzzy control

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Imbalance Data Classification Method Based on Improved SMOTE Algorithm and Granular Computing 41

Imbalance Data Classification Method Based on Improved SMOTE...

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第41届中国控制会议

作者： QiLiang Dong Wei Lu School of Control Science and Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Classification of imbalanced data is a problem to be solved in practical applications,and sampling algorithm is a more effective *** SMOTE algorithm is a classical oversampling algorithm that provides a strong guideline for dealing with imbalanced datasets,but there are still some limitations,such as blurring the boundary between positive class samples and negative class *** alleviate the limitations,in this study,a new oversampling method called KB-SMOTE(K-means++Borderline-SMOTE) is *** new algorithm first clusters the minority class,next synthesizes new samples between cluster center and borderline points in minority samples,which solves the problem of blurred class boundary of imbalanced datasets,avoids the interference of noisy samples and reduces the impact on the original ***,this paper uses KB-SMOTE as a data preprocessing algorithm,combined with a hypersphere information granular classifier to classify the imbalanced *** the experimental part,the combined classifier of this paper is compared with other classification algorithms on several imbalanced datasets,and the effectiveness of the algorithm is verified.

关键词： Imbalance Data Classification SMOTE Algorithm Granular Computing

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