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不同金属氯化物催化制备聚甲基硅倍半氧烷气凝胶及其性能研究

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化工新型材料 2024年第2期52卷 139-144,150页

作者：刘青松张桂华李承东江南大学化学与材料工程学院合成与生物胶体教育部重点实验室无锡214122 江南大学化学与材料工程学院“光响应功能分子材料”国家级国际联合研究中心无锡214122

分别以甲基三甲氧基硅烷(MTMS)和甲基三乙氧基硅烷(MTES)为硅源,以氯化镁、氯化铜、氯化铝和氯化铁水溶液为酸催化剂,采用常压干燥工艺制备聚甲基硅倍半氧烷(PMSQ)气凝胶。结果表明:在MTMS溶胶中掺入氯化镁或氯化铜水溶液或在MTES溶胶... 详细信息

分别以甲基三甲氧基硅烷(MTMS)和甲基三乙氧基硅烷(MTES)为硅源,以氯化镁、氯化铜、氯化铝和氯化铁水溶液为酸催化剂,采用常压干燥工艺制备聚甲基硅倍半氧烷(PMSQ)气凝胶。结果表明:在MTMS溶胶中掺入氯化镁或氯化铜水溶液或在MTES溶胶中掺入氯化铝或氯化铁水溶液均可制备出轻质(69.0~96.0mg/cm^(3))、高强度(0.302~0.583MPa)、高热稳定性(≥550℃)和超疏水(145.6~151.6°)的PMSQ气凝胶。以氯化铁水溶液为酸催化剂制备的气凝胶密度最低,线性收缩率(3.2%)最小,压缩强度和最大压缩应变(87.2%±0.2%)最高。以氯化镁水溶液为酸催化剂制备的气凝胶的常温热导率[33.5mW/(m·K)]最低,可在温度低于600℃时不发生—CH_(3)的热分解反应,在800℃时的质量损失仅为7.2%,在100℃和200℃的恒温加热台上加热300s,其表面温度从30℃分别上升至42.6℃和70.6℃,体现出极好的绝热性能。

关键词：甲基三甲氧基硅烷甲基三乙氧基硅烷酸催化剂金属氯化物聚甲基硅倍半氧烷气凝胶常压干燥压缩性能热稳定性

A Hybrid Learning Method for the Data-Driven Design of linguistic Dynamic Systems

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1487-1498页

作者： chengdong li Jianqiang Yi Yisheng Lv Peiyong Duan School of Information and Electrical Engineering Shandong Jianzhu UniversityJinan 250101China Institute of Automation Chinese Academy of SciencesBeijing 100190China Institute of Automation Chinese Academy of SciencesBeijing 100190and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China

In lots of data based prediction or modeling applications,uncertainties and/or noises in the observed data cannot be *** such cases,it is more preferable and reasonable to provide linguistic(fuzzy)predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by *** dynamic system(LDS)provides a powerful tool for yielding linguistic(fuzzy)***,it is still difficult to construct LDS models from observed *** solve this issue,this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form ***,a hybrid learning method is proposed to construct the data-driven LDS *** proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method,then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules,and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy *** proposed approach is successfully applied to three real-world prediction applications which are:prediction of energy consumption of a building,forecasting of the traffic flow,and prediction of the wind *** results show that the uncertainties in the data can be effectively captured by the linguistic(fuzzy)*** can also be extended to some other prediction or modeling problems,in which observed data have high levels of uncertainties.

关键词： Fuzzy set least square method linguistic dynamic system(LDS) multi-objective optimization

Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 751-762页

作者： chengdong li li Wang Guiqing Zhang Huidong Wang Fang Shang IEEE School of Information and Electrical Engineering Shandong Jianzhu University School of Management Science and Engineering Shandong University of Finance and Economics

Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules(FSIRMs) connected fuzzy inference system(FIS) to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system(FSIRMNFS). Further,the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.

关键词： Fuzzy inference system(FIS) Iearning algorithm neural fuzzy system single input rule module wind speed prediction

Pythagorean Uncertain linguistic Variable Hamy Mean Operator and Its Application to Multi-attribute Group Decision Making

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 527-539页

作者： Huidong Wang Shifan He chengdong li Xiaohong Pan IEEE the School of Management Science and Engineering Shandong University of Finance and Economics the School of Information and Electrical Engineering Shandong Jianzhu University

Pythagorean fuzzy set(PFS) can provide more flexibility than intuitionistic fuzzy set(IFS) for handling uncertain information, and PFS has been increasingly used in multi-attribute decision making problems. This paper proposes a new multiattribute group decision making method based on Pythagorean uncertain linguistic variable Hamy mean(PULVHM) operator and VIKOR method. Firstly, we define operation rules and a new aggregation operator of Pythagorean uncertain linguistic variable(PULV) and explore some properties of the ***, taking the decision makers' hesitation degree into account, a new score function is defined, and we further develop a new group decision making approach integrated with VIKOR method. Finally, an investment example is demonstrated to elaborate the validity of the proposed method. Sensibility analysis and comprehensive comparisons with another two methods are performed to show the stability and advantage of our method.

关键词： Hamy mean(HM) operator multi-attribute group decision making(MAGDM) Pythagorean fuzzy set Pythagorean uncertain linguistic variable

Robust Formation Maneuvers Through Sliding Mode for Multi-agent Systems With Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 342-351页

作者： Dianwei Qian chengdong li Suk Gyu Lee Chao Ma School of Control and Computer Engineering North China Electric Power University School of Information and Electrical Engineering Shandong Jianzhu University Department of Electrical Engineering Yeungnam University

This paper develops a robust control method for formation maneuvers of a multi-agent system. The multi-agent system is leader-follower-based, where the graph theory is utilized to describe the information exchange among the agents. The control method is exercised via sliding mode methodology where each agent is subjected to uncertainties. The technique of nonlinear disturbance observer is adopted in order to overcome the adverse effects of the uncertainties. Assuming that the uncertainties have an unknown bound, the formation stability conditions are investigated according to a given communication topology. In the sense of Lyapunov, not only the formation maneuvers of the multi-agent system have guaranteed stability, but the desired formations of the agents are also realized. Compared with other two control approaches, i.e., the basic sliding mode approach and the fuzzy sliding mode approach, some numerical results are presented to illustrate the effectiveness, performance and validity of the robust control method for formation maneuvers in the presence of uncertainties.

关键词： Formation control multi-agent systems nonlinear disturbance observer sliding mode uncertainties

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三元锂正极材料电化学性能及回收技术分析

产业科技创新 2024年第2期6卷 112-116页

作者：陈尚逸李成冬刘霜盐城工学院汽车工程学院江苏盐城224051

“双碳”目标出台后,新型能源在能源消耗结构中的占比迅速提高,锂离子电池(liBs)储能市场也稳步发展,动力电池也进入了报废高潮。而正极作为电池性能提升的关键性因素更是受到了国内外的广泛关注。为了兼顾正极材料的容量、结构稳定性... 详细信息

“双碳”目标出台后,新型能源在能源消耗结构中的占比迅速提高,锂离子电池(liBs)储能市场也稳步发展,动力电池也进入了报废高潮。而正极作为电池性能提升的关键性因素更是受到了国内外的广泛关注。为了兼顾正极材料的容量、结构稳定性、安全性,以及回收其中的有价金属,本文特针对NCM三元正极材料的现存问题进行分析,深入学习了电化学性能改性和回收等技术,并针对现有技术的不足提出改进思路,为之后的研究提供参考。

关键词： NCM三元正极材料表面包覆电化学性能回收技术

动力电池SOC估计研究方法及梯次利用方法的发展现状

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黑龙江科学 2024年第4期15卷 30-33页

作者：仇顺荣李成冬田旭盐城工学院汽车工程学院江苏盐城224051

近年来,随着新能源的大力发展,动力电池成为新能源汽车的动力源,其中锂离子具有优秀的性能及较高的容量受到大量车厂的青睐。而动力电池的荷电状态即SOC作为动力电池能量管理系统中的重要组成,对其精确的评估具有重要影响。目前,国内外... 详细信息

近年来,随着新能源的大力发展,动力电池成为新能源汽车的动力源,其中锂离子具有优秀的性能及较高的容量受到大量车厂的青睐。而动力电池的荷电状态即SOC作为动力电池能量管理系统中的重要组成,对其精确的评估具有重要影响。目前,国内外针对动力电池荷电状态的估计已有大量研究。随着动力电池的发展,动力电池的退役处理成为急需解决的重大问题。针对退役电池的处理主要有梯次利用及回收拆解,针对动力电池荷电状态检测方法及梯次利用方法进行综述,为其深入发展奠定基础。

关键词：动力电池 SOC估计安时积分卡尔曼滤波梯次利用

Evolution of Hydrogen Storage Alloys Prepared by Special Methods

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Journal of Rare Earths 2004年第Z1期22卷 222-227页

作者： Guo Hong Zhang Ximin Jing Hai li chengdong Xu Jun National Engineering and Technology Research Center for Non-Ferrous Metals Composites General Research Institute for Non-Ferrous Metals Beijing 100088 China National Engineering and Technology Research Center for Non-Ferrous Metals Composites General Research Institute for Non-Ferrous Metals Beijing 100088 China National Engineering and Technology Research Center for Non-Ferrous Metals Composites General Research Institute for Non-Ferrous Metals Beijing 100088 China National Engineering and Technology Research Center for Non-Ferrous Metals Composites General Research Institute for Non-Ferrous Metals Beijing 100088 China National Engineering and Technology Research Center for Non-Ferrous Metals Composites General Research Institute for Non-Ferrous Metals Beijing 100088 China

Microstructure characteristics and electrochemical properties of hydrogen storage alloys prepared by gas atomization, melt spinning and strip casting respectively were *** advantages, disadvantages and research development of the above methods for preparing hydrogen storage alloys were *** strip casting is a new special means for preparing AB5 rare earth hydrogen storage alloys of high performance and low cost, and the study of the strip casting for preparing hydrogen storage alloys is presented specially.

关键词： hydrogen storage alloys gas atomization melt spinning strip casting rare earths

Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network

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Building Simulation 2022年第9期15卷 1685-1701页

作者： Chenlu Tian Yunyang Ye Yingli Lou Wangda Zuo Guiqing Zhang chengdong li Shandong Key Laboratory of Intelligent Building Technology School of Information and Electrical EngineeringShandong Jianzhu UniversityJinan 250101China Pacific Northwest National Laboratory RichlandWA 99354USA Department of Civil Environmental and Architectural EngineeringUniversity of Colorado BoulderBoulderCO 80309USA

Power demand prediction for buildings at a large scale is required for power grid *** bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing ***-down methods based on data driven models are fast,but less *** the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large *** new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power *** the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of *** verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential *** results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.

关键词： large-scale simulation power demand generative adversarial networks building energy model