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Optimization of Island Integrated Energy System based on Marine Renewable Energy

Fundamental Research 2024年

作者： Wen Zhao Bohong Wang Ting Pan Yujie Chen Hengcong Tao Baoying Guo Petar Sabev Varbanov Jinshu Lu National-Local Joint Engineering Laboratory of Harbour Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control Zhejiang Ocean University No.1 Haida South Road 316022 Zhoushan P.R. China Sustainable Process Integration Laboratory – SPIL NETME Centre Faculty of Mechanical Engineering Brno University of Technology - VUT Technická 2896/2 616 00 Brno Czech Republic School of Mechanical Engineering Beijing Institute of Petrochemical Technology Beijing 102617 China Marine Science and Technology College Zhejiang Ocean University Zhoushan 316022 China Széchenyi István University H-9026 Győr Egyetem tér 1 Hungary School of Naval Architecture and Maritime Zhejiang Ocean University Zhoushan 316022 Zhejiang China

Integrating marine renewable energy (MRE) with conventional energy sources and logically constructing island energy systems is crucial for alleviating island energy supply challenges and helping coastal energy systems achieve a sustainable, low-carbon transition. In this study, the status of marine energy utilisation technologies is reviewed, with a focus on advancements in energy conversion equipment, grid integration, and energy storage. The economic feasibility and environmental sustainability of marine energy systems are comparatively analysed to enhance the development and utilisation of marine energy technology while reducing the economic cost of power generation. Suitable equipment is highlighted for islands, with efficient energy generation strategies proposed to achieve cleaner, localised, and cost-effective island integrated energy system (IIES) design. Island energy facilities vary, and integrated development is crucial for building new energy systems. Based on the types and resources of island energy, IIESs are constructed for hierarchical energy utilisation and multi-energy coupling, coordinating resources to achieve source–grid–load–storage integration. The optimisation of IIESs is reviewed, with a focus on modelling methods, intelligent algorithm development, and system simulation. This study differs from previous research as it considers the integration of marine energy into existing systems to achieve comprehensive integration of multiple energy sources. Additionally, optimisation and solution methods for IIES models are summarised. To integrate complex, multivariable energy systems and create stable and predictable outputs, marine energy and load forecasting methods are explored. Overall, this study supports the advancement of marine energy utilisation, focusing on its progressive integration into island energy systems as the efficiency of marine energy improves. This work aims to inspire the development of new functions and modules based on existi

关键词： Marine Renewable Energy Energy Conversion and Storage Island Integrated Energy System Process Systems engineering Process Integration

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A distributed alternating direction method of multipliers algorithm for consensus optimization

A distributed alternating direction method of multipliers al...

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Chinese Automation Congress (CAC)

作者： Xia Zhang Ding Liu Fei Yu Duqiao Zhao National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xian University of Technology Xian China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian China

The Alternating Directions Methods of Multipliers (ADMM) are widely used in many fields of scientific computing in recent years. This method applies iterative computation to the information exchange between individual agent and neighbor. However, despite the success of traditional centralized ADMM in some application environments, its applicability is limited in global convergence center by its communication requirements. In our paper, we provide the linear convergence rate for this distributed consensus optimization problem, which satisfies strongly convex local objective functions. Then, the properties of the local objective function and the parameters of the algorithm, the theoretical convergence rate is given according to the network topology.

关键词： Convergence Optimization Linear programming Convex functions Numerical models Signal processing algorithms Information processing

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Anti-inflammation effects of highly purified low-M_w RG-I pectins on LPS-activated macrophages

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Bioactive Carbohydrates and Dietary Fibre 2021年 26卷

作者： Wu, Dongmei Zheng, Xiaoliang Hu, Weiwei Zhu, Kai Yu, Chengxiao He, Qiaojun Linhardt, Robert J. Ye, Xingqian Chen, Shiguo College of Biosystems Engineering and Food Science National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang Key Laboratory for Agro-Food Processing Integrated Research Base of Southern Fruit and Vegetable Preservation Technology Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control Zhejiang University Hangzhou 310058 China Fuli Institute of Food Science Zhejiang University Hangzhou 310058 China Ningbo Research Institute Zhejiang University Ningbo 315100 China Center for Molecular Medicine Zhejiang Academy of Medical Sciences Hangzhou 310013 China Institute of Pharmacology & Toxicology College of Pharmaceutical Sciences Zhejiang University Hangzhou 310058 China Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute Troy 12180 NY United States

Pectic oligosaccharides are viewed as excellent immunomodulator, but the specific molecular weight (Mw) range required for anti-inflammatory effects remains unclear. In this study, citrus pectins was depolymerized by ... 详细信息

Pectic oligosaccharides are viewed as excellent immunomodulator, but the specific molecular weight (M_w) range required for anti-inflammatory effects remains unclear. In this study, citrus pectins was depolymerized by the ultrasound/H2O2/Vc system and the pectic mixtures was separated through the superdex 30 chromatography column. Three highly purified pectin fractions (USP1, USP2, USP3) were obtained, with the M_w of 2713 g/mol, 3683 g/mol, and 7469 g/mol. All of them were RG-I pectins and USP3 contained more arabinan sidechains than USP1 and USP2. In vitro anti-inflammation assays based on LPS-induced RAW264.7 macrophages indicated that these pectins could suppress the NO release, ROS production and down-regulated the level of pro-inflammatory cytokines IL-1β, IL-6 and TNF-α in a M_w-dependent manner. The inhibitory effects increased in the order of USP1w pectins with M_w>3KD should be effective as a prophylactic agent against inflammatory diseases in functional food. © 2021

关键词： Anti-inflammatory Arabinan IL-6 Molecular weight Pro-inflammatory cytokines RG-I pectin Sidechains

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Support Vector Regression Modeling based Data-Driven Iterative Learning control for Czochralski Crystal Growth Process

Support Vector Regression Modeling based Data-Driven Iterati...

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第三十八届中国控制会议

作者： Junchao Ren Ding Liu Yin Wan Ni Zhang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In view of the previous Czochralski(Cz) monocrystalline silicon growth control method based on mechanism model, it is difficult to accurately reflect the nonlinear and real-time variation characteristics of crystal growth process. This paper proposes a data-driven iterative learning control method based on support vector regression, which combines crystal diameter prediction model by support vector regression(SVR) off-line training with crystal growth mechanism model, and then control the crystal diameter and output key variable information in the framework of iterative learning algorithm. Simulation results for diameter control of crystal growth process and the analysis of key variable information show that the proposed method is effective and has a fast crystal diameter learning speed, and good performances of setpoint tracking and disturbance rejection, high crystal growth speed.

关键词： Data-Driven Czochralski Monocrystalline Silicon Support Vector Regression Iterative Learning control

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Ensemble of online sequential extreme learning machine based on cross-validation

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Journal of Physics: Conference Series 2020年第3期1550卷

作者： Liyun Wei Lidan Wang Yunfei Li Shukai Duan College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

The online sequential extreme learning machine (OS-ELM) was proposed as an evolution of the Extreme learning machine (ELM) in 2006. The learning algorithm can learn data by fixing or changing the size of the chunk (a block of data). Compared with other online algorithms, it has faster training speed, and the generalization performance is better. The proposed ensemble of neural networks further demonstrates that the use of the neural network sets with multiple consensus schemes has a greater improvement over the stability of a single network. The basic idea of cross-validation is to group the original data sets, part of which is to train as the training set and the other part of which is to verify as the test set, so the algorithm can reduce the over-fitting problem of the data to a certain extent, and extract more valid information in the limited data set. In this paper, a new ensemble of online sequential extreme learning machine based on cross-validation (ENOS-ELM) is proposed. Through application of cross-validation and integration to the training stage, the accuracy of the algorithm in classification is further improved.

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Research and Implementation of Breast Cancer intelligent Recognition Algorithm Based on Deep Convolutional Neural Network

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Journal of Physics: Conference Series 2020年第1期1634卷

作者： Taixing Chen Lidan Wang Yunfei Li Shukai Duan College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

Breast cancer is one of the most dangerous cancers with a particularly high mortality rate for women. In the face of thousands of female pathological pictures, how to use computer related control algorithms to quickly and efficiently intelligently idently female breast cancer images has become an urgent problem in the world. With the development of computer vision and deep convolutional neural networks, this paper applies the convolutional neural network (CNN) algorithm to solve the classification and recognition computer tasks of histological images for breast cancer, aiming to learn from tens of thousands of histological images features for breast cancer, and to assist doctors in diagnosing patients' disease was found by computer control algorithm. There are many well-known neural network computer algorithms, such as AlexNet, Inception-Net, ResNet. Image recognition for breast is a binary classification problem, and it is not appropriate to use a ResNet network with a large amount of parameters. Therefore, this paper borrowed Alexnet algorithm and redesigned the algorithm, then used it for breast cancer experiments. We compared with the capsule algorithm used by Anupama authors. Our experiment improves the accuracy rate by 2%, and reduces the amount of neural network parameters, which greatly speeds up the training time.

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Car Plate Detection Based on Yolov3

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Journal of Physics: Conference Series 2020年第1期1544卷

作者： Siyuan Shen Lidan Wang Shukai Duan Xin He College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

An intelligent car plate detection method can make the travel more convenient and efficient. However, traditional methods are reasonably effective under the specific circumstances or strong assumptions only, and there are few databases for car plate detection. Therefore, a novel real-time car plate detection method based on improved Yolov3 has been proposed. In order to select the more precise number of candidate anchor boxed and aspect ratio dimensions, the K-Means algorithm is utilized. To solve the short of the available car plate database, a car plate database which has 6668 pictures has been established. As shown in the experimental results, the method which is proposed by this paper is better than original Yolov3. Thanks to the car plate database, the proposed method obtained better results even in the situation of inclination, too bright or too dark, different weather and so on.

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Rain Removal from images based on Computer Vision and conditional Generative Adversarial Networks

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Journal of Physics: Conference Series 2020年第1期1570卷

作者： Yunfei Li Lidan Wang Taixing Chen Ziyuan Wang Shukai Duan College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China ChongqingBrain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

In view of the quick development of modern computer technology, more pictures of natural scenes have been collected and analyzed for application in military fields, scientific research, and computer vision fields related to object detection. However, the sharpness of these pictures collected will be affected by rain and snow, which will reduce the visibility of things in the pictures. This will not only affect the viewing, but also affect scientific research fields such as autonomous driving and video surveillance. Therefore, the research on image rain removing is very practical. An improved algorithm model named pixel-GAN is proposed in this article, which based on conditional Generative Adversarial Networks (cGAN). This new method regards the problem of removing rain from specific images as the style conversion problem. What we improved is that a VGG-16 module is added to the original network to extract perceptual loss as a new part of the loss function. We show the excellent rain-removing ability of the improved algorithm and also illustrate the reason why we add the perceptual loss. Compared with other existing methods, there is no need to set a prior model, the training speed is faster and the proposed model finally generates higher quality pictures.

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Robust correlation filter that combined temporal regularization with spatial weight L1 constraint

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Journal of Physics: Conference Series 2020年第4期1550卷

作者： Xin He Shukai Duan Lidan Wang College of Electronic and Information Engineering Southwest University Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China

The parameter optimization of the Object tracking algorithms has achieved excellent performance based on parameter regularization. Adaptive spatial regularization correlation filter can obtain the optimal weight coefficient for a specific target. Inspired by this, we propose a filter combined temporal regularity with spatial L1 regularization (LTASRCF). The time regularizer is used to remember the data of the previous frame to update the current target position. It performs well in the process of occlusion, deformation and motion blur. Meanwhile, L1 regularization of spatial weights reduces the computational complexity of the coefficients, and improves the robustness of the spatial weight matrix. We have conducted comparative experiments on the OTB-2015 dataset, and the proposed LTASRCF model ranks first among the five popular algorithms, which outperforms by 9.2% and 18.9% than KCF, respectively, and reaches real-time performance.

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开式泵控非对称缸负载容腔独立控制系统（英文）

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2018年第3期19卷 203-210页

作者： Jing YAO Pei WANG Xiao-ming CAO Zhuo WANG The Laboratory of Heavy Machinery Fluid Power Transmission and Control in Hebei Yanshan University Qinhuangdao 066004 China National and Local Joint Engineering Center for Advanced Forging Press Forming Technology and Equipment Faculty of Engineering Yanshan University Qinhuangdao 066004 China Yanshan University Qinhuangdao 066004 China

目的:为减少能量排放和提升节能效果,并解决非对称缸系统的流量不对称问题,本文对开式泵控非对称缸负载容腔独立控制系统的控制特性及能耗特性进行了深入的研究,以期为其实际应用提供理论支撑。创新点:1.提出开式泵控非对称缸负载容腔... 详细信息

目的:为减少能量排放和提升节能效果,并解决非对称缸系统的流量不对称问题,本文对开式泵控非对称缸负载容腔独立控制系统的控制特性及能耗特性进行了深入的研究,以期为其实际应用提供理论支撑。创新点:1.提出开式泵控非对称缸负载容腔独立控制系统,建立其能量传输模型;2.以压机为对象进行实验研究,采用位置压力负载容腔独立控制方法,获得其能耗与控制特性。方法:1.介绍开式泵控非对称缸负载容腔独立控制系统的构型;2.通过理论推导,建立能量传输模型,得到具有能量回收功能的系统;3.通过实验研究和分析,验证所提系统和方法的有效性。结论:1.基于能量传输模型得到的系统具有较好的节能特性;2.开式泵控非对称缸负载容腔独立控制系统增加了系统控制自由度,验证了负载容腔独立控制方法的有效性;3.开式泵控非对称缸负载容腔独立控制系统采用无杆腔位置控制和有杆腔压力控制组合的控制方法;随着有杆腔压力的提高,在不增加系统能耗的前提下该方法改善了系统的位置控制精度。

关键词：泵控系统非对称缸能耗位置压力组合控制负载容腔独立控制

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