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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Peng, Lei Fu, Qiang Lin, Musong Wu, Zaikun Xu, Zhihua Zhu, Tianrong Guangdong Key Laboratory of Electric Power Equipment Reliability Electric Power Research Institute of Guangdong Power Grid Co. Ltd Guangzhou China Wuhan Institute of Technology Key Laboratory of Green Chemical Process of Ministry of Education Key Laboratory of Novel Reactor and Chemical Technology of Hubei Province School of Chemical Engineering & Pharmacy Wuhan430073 China Jianghan University Hubei Key Laboratory of Industrial Fume & Dust Pollution Control Wuhan430056 China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

Ionic liquid was known as room temperature molten salts. It is a new material composed by cation and anion, and exists as liquid at or near room temperature. Due to its strong interaction with CO2, it generally has a high solubility for CO2. In this work, the classification and synthesis of ionic liquids were summarized. Meanwhile, the absorption performance of CO2 into traditional ionic liquids, functionalized ionic liquids, polymeric ionic liquids and other forms of ionic liquids was discussed, respectively. Based on the relative study, the advantages and disadvantages of ionic liquids as carbon dioxide absorbents were analyzed in the end. It has the potential for substituting the traditional carbon dioxide absorbents. © 2022 IEEE.

关键词： Carbon dioxide

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zibo Miao Yu Pan Qing Gao Guangdong Key Laboratory of Intelligent Morphing Mechanisms School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

关键词： Perturbation methods Design methodology Quantum optics Robustness Feedback control Closed loop systems Tuning Cybernetics

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Tendencies of alloyed engineering in BiOX-based photocatalysts:a state-of-the-art review

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Rare Metals 2024年第4期43卷 1488-1512页

作者： Yan-Dong Sun Chao Zeng Xue Zhang Zi-Qi Zhang Bo Yang Sheng-Qi Guo Tianjin Key Laboratory of Clean Energy and Pollutant Control School of Energy and Environmental EngineeringHebei University of TechnologyTianjin300401China State-Province Joint Engineering Laboratory of Zeolite Membrane Materials Institute of Advanced MaterialsCollege of Chemistry and Chemical EngineeringJiangxi Normal UniversityNanchang330022China Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation School of Physics and Electronic InformationAnhui Province Industrial Generic Technology Research Center for Alumics MaterialsHuaibei Normal UniversityHuaibei235000China

Energy-saving and environmentally friendly photocatalysis has emerged as a popular research area in response to issues with energy scarcity and environmental *** to the unique layer-like structure,BiOX(Cl,Br,I)is frequently used in ***,inherent flaws in BiOX,such as an inappropriate band gap and low carrier separation efficiency,restrict its capacity for *** to the tunable grouping layer,alloying engineering is employed to optimize the intrinsic properties of BiOX and alloyed BiOX becomes a promising photocatalytic *** review describes the structure of BiOX,where tunable halogen layers provide favorable conditions for the implementation of alloying engineering to improve intrinsic *** article compares the effects and mechanisms of alloying engineering on the optimization of the energy band structure and carrier behavior of BiOX,and lists various modification methods used to improve the optimization of the intrinsic properties by alloying engineering,including defect engineering,morphology control as well as the synergy between alloying and other modification methods(bismuth-rich strategies,cation doping,construction of heterojunctions and plasma resonance effects).Subsequently,applications of alloyed BiOX in energy and environmental fields are summarized,including contaminant degradation,antibacterial,CO_(2)reduction,nitrogen fixation and organic ***,we summarize the current challenges and future directions of alloyed *** is expected that this work will provide guidance and assistance for an in-depth study and understanding of the mechanisms of alloying engineering to optimize intrinsic properties and design alloyed BiOX with higher photocatalytic activity.

关键词： Bismuth oxyhalide Photocatalytic Alloyed engineering Microstructural regulation Application

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ESO-Based Data Driven Set-Point Learning control for Nonlinear batch processes with PD-Type Feedback control Structure Subject to Nonrepetitive Uncertainties*

ESO-Based Data Driven Set-Point Learning Control for Nonline...

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Data Driven control and Learning Systems (DDCLS)

作者： Naseem Ahmad Shoulin Hao Tao Liu Yihui Gong Jiyan Zhang Haixia Wang Yong Zhu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China Institute of Advanced Control Technology Dalian University of Technology Dalian P. R. China School of Biomedical Engineering Dalian University of Technology Dalian P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. The controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. The effectiveness and superiority of the proposed scheme are verified by an illustrative example.

关键词： Learning systems Uncertainty Heuristic algorithms Batch production systems process control Observers Iterative algorithms

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Simultaneous hydrogen production with photo reforming of lactic acid over MXene derived MoS_(2)/TiO_(2)/Ti_(3)C_(2)nanowires

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Chinese Journal of Catalysis 2025年第4期71卷 234-245页

作者： Seemal Mansoor Zixu Hu Yuxin Zhang Muhammad Tayyab Mazhar Khan Zeeshan Akmal Liang Zhou Juying Lei Muhammad Nasir Jinlong Zhang Key Laboratory for Advanced Materials Shanghai Engineering Research Center for Multi–media Environmental Catalysis and Resource UtilizationJoint International Research Laboratory of Precision Chemistry and Molecular EngineeringFeringa Nobel Prize Scientist Joint Research CenterSchool of Chemistry and Molecular EngineeringEast China University of Science&TechnologyShanghai 200237China National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical ProcessEast China University of Science and TechnologyShanghai 200237China State Key Laboratory of Urban Water Resource and Environment Harbin Institute of TechnologyHarbin 150090HeilongjiangChina Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China Institute of Materials Research Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055GuangdongChina COMSATS Univ Islamabad Interdisciplinary Res Ctr Biomed Mat IRCBMLahore CampusLahore 54000Pakistan

Developing efficient photocatalysts to address collaborative energy and environmental crises still faces significant *** this report,we present a highly efficient MXene–based photocatalyst,which is combined with MoS_(2)nano patches and TiO_(2)/Ti_(3)C_(2)(TTC)nanowires through hydrothermal *** all the composites tested,the optimized photocatalyst gave a remarkable H_(2)and revolving polylactic acid(PLA)into pyruvic acid(PA).Achieving a remarkable H_(2)evolution rate of 637.1 and 243.2μmol g^(−1)h^(−1),in the presence of TEOA and PLA as a sacrificial reagent under UV-vis(λ≥365 nm)light *** improved photocatalytic activity is a result of the combination of dual cocatalyst on the surface of TTC photocatalyst,which create an ideal synergistic effect for the generation of PA and the production of H_(2)*** MoS_(2)TiO_(2)/Ti_(3)C_(2)(MTT)composite can generate more photoexcited charge carriers,leading to the generation of more active radicals,which may enhance the system's photocatalytic *** work aims at demonstrating its future significance and guide the scientific community towards a more efficient approach to commercializing H_(2)through photocatalysis.

关键词： Photo reforming Polylactic acid Hydrogen evolution Lactic acid Pyruvic acid

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Adaptive neural network based boundary control of a flexible marine riser system with output constraints

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Frontiers of Information technology & Electronic Engineering 2022年第8期23卷 1229-1238页

作者： Chuyang YU Xuyang LOU Yifei MA Qian YE Jinqi ZHANG Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi 214122China College of Internet of Things Technology Wuxi Institute of TechnologyWuxi 214122China Wuxi Good Cloud IoT Technology Co. Ltd.Wuxi 214196China

In this study,we develop an adaptive neural network based boundary control method for a flexible marine riser system with unknown nonlinear disturbances and output constraints to suppress *** begin with describing the dynamic behavior of the riser system using a distributed parameter system with partial differential *** compensate for the effect of nonlinear disturbances,we construct a neural network based boundary controller using a radial basis neural network to reduce *** the proposed boundary controller,the state of the riser is guaranteed to be uniformly bounded based on the Lyapunov *** proposed methodology provides a way to integrate neural networks into boundary control for other flexible robotic manipulator ***,numerical simulations are given to demonstrate the effectiveness of the proposed control method.

关键词： Marine riser system Partial differential equation Neural network Output constraint Boundary control Unknown disturbance

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Inhomogeneous strain and doping of transferred CVD-grown graphene

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Rare Metals 2022年第5期41卷 1727-1734页

作者： Yu-Ting Niu Fang-Zhu Qing Xue-Song Li Bo Peng National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of ChinaChengdu 611731China State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of ChinaChengdu 611731China School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China

Chemical vapor deposition(CVD)-grown graphene on copper foils is subject to the Cu substrate, which affects the spatial distribution of strain and doping, thus influencing the electronic properties of graphene. However, plenty of electronic devices based on CVD-grown graphene require transfer process and the distribution of doping and strain in CVD-grown graphene transferred onto the insulating substrates remains elusive.

关键词： graphene transferred strain

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UWIA-AODV:Improved Ad-Hoc On-Demand Distance Vector Routing Protocol for Underwater Acoustic Sensor Networks

UWIA-AODV:Improved Ad-Hoc On-Demand Distance Vector Routing ...

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第42届中国控制会议

作者： Xueyi Wang Meiqin Liu Senlin Zhang Shanling Dong National Key Laboratory of Industrial Control Technology Zhejiang University College of Electrical Engineering Zhejiang University Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University

In the past a few years,underwater acoustic networks have developed as a powerful technique used for variety of ***-Hoc On-Demand Distance Vector(AODV) is a popular and effective routing protocol,while it is not suitable for underwater usage due to the features of underwater acoustic channel such as low data rate,long delay,limited bandwidth and *** this paper,we propose an improved AODV based on implicit acknowledgement which employs broadcast features of nodes to overhear packets and retransmit those that fail to be *** addition,residual energy is considerded for energy ***,the in-field experiment results shows that our proposed protocol can select the route with more residual energy for energy *** our proposed protocol also outperforms AODV in terms of throughput and packet discovery *** it is more suitable for underwater scenarios that require high transmission reliability.

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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A Novel Data-Driven Combustion Modeling and Optimization Approach for Coal-Fired Boiler Under Deep Peak Shaving

SSRN

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SSRN 2024年

作者： Wu, Yixi Wang, Ziqi Shi, Chenli Jin, Xiaohang Xu, Zhengguo State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China College of Mechanical Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education Zhejiang University of Technology Zhejiang Province Hangzhou310023 China

The cost of coal and environmental protection make coal-fired power plants focus on energy conservation and emission reduction in the boiler's combustion process. And in the context of carbon neutrality, the modeling and optimization of combustion process has become more difficult in order to adapt to the wide-load and variable-load operating requirements under deep peak shaving. To address the raised difficulties, this study proposed a data-driven approach for boiler combustion modeling, model tuning and optimization under wide-load and variable-load operating requirements. The proposed approach was experimented on an ultra-supercritical coal-fired boiler with targets of NOx concentration and air pre-heater's temperature, and its superiority was verified. To face the high requirements for model's learning and generalization ability brought considering wide-variable loads, a Gaussian kernel and LSTM based neural network model can capture the work conditions and temporal information was proposed and achieved accurate prediction with comprehensive historical data for training on experimented boiler. To solve the issue of incomplete coverage of historical training data under deep peak shaving, a fine-tuning framework was added and completed the rapid model tuning to adapt to the new steady-state work condition using a small amount of data in experiments. (See text for full abstract.) © 2024, The Authors. All rights reserved.

关键词： Coal fired boilers

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