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Large-Scale Carbon Dioxide Storage in Salt Caverns:Evaluation of Operation,Safety,and Potential in China

engineering 2024年第9期40卷 226-246页

作者： Wei Liu Xiong Zhang Jifang Wan Chunhe Yang Liangliang Jiang Zhangxin Chen Maria Jose Jurado Xilin Shi Deyi Jiang Wendong Ji Qihang Li State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing UniversityChongqing 400044China China Energy Digital Technology Group Co.Ltd. Beijing 100044China Institute of Rock and Soil Mechanics Chinese Academy of SciencesWuhan 430064China Department of Chemical and Petroleum Engineering University of CalgaryCalgaryAB T2N 1N4Canada Eastern Institute of Technology Ningbo 315200China Geosciences Barcelona CSIC Spanish National Research CouncilBarcelona 08028Spain

Underground salt cavern CO_(2) storage(SCCS)offers the dual benefits of enabling extensive CO_(2) storage and facilitating the utilization of CO_(2) resources while contributing the regulation of the carbon *** economic and operational advantages over traditional carbon capture,utilization,and storage(CCUS)projects make SCCS a more cost-effective and flexible *** the widespread use of salt caverns for storing various substances,differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness,carbon injection,brine extraction control,long-term carbon storage stability,and site selection *** distinctions stem from the unique phase change characteristics of CO_(2) and the application scenarios of ***,targeted and forward-looking scientific research on SCCS is *** paper introduces the implementation principles and application scenarios of SCCS,emphasizing its connections with carbon emissions,carbon utilization,and renewable energy peak *** delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods,and addresses associated scientific *** this paper,we establish a pressure equation for carbon injection and brine extraction,that considers the phase change characteristics of CO_(2),and we analyze the pressure during carbon *** comparing the viscosities of CO_(2) and other gases,SCCS’s excellent sealing performance is *** on this,we develop a long-term stability evaluation model and associated indices,which analyze the impact of the injection speed and minimum operating pressure on *** countermeasures to ensure stability are *** selection criteria for SCCS are established,preliminary salt mine sites suitable for SCCS are identified in China,and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons,utilizing only 20%-30%volume

关键词： Carbon-neutrality Salt cavern Large-scale CO_(2)storage Injection and withdrawal Stability analysis

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A MPC-based Planner Applied on a Parallel Wheel-legged Robot for Obstacle Avoidance 25

A MPC-based Planner Applied on a Parallel Wheel-legged Robot...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and control engineering

作者： Fei Guo Huan Yu Wanhong Lin Shoukun Wang National Key Laboratory of Human Factors Engineering China Astronaut Research and Training Center Beijing China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400712647

A wheel-legged robot is equipped with Stewart parallel mechanism, constituting a reconfigurable robot which can change its wheelbase, robot body height, and achieve omnidirectional steering. The legged character effectively improves the terrain adaptability, which concerns our planning concentration. We introduced an optimization-based whole-body trajectory planning algorithm to navigate robot in rugged terrain. The planner combines terrain data and stability, allowing lower-level motion generator and controller to operate more efficiently. The Model Predictive control(MPC)-based method updates the footholds and CoG trajectories, which builds upon the support polygon constraints on optimization. The simulations of methodology working in several structure-obstacle scene demonstrated and compared the availability of approach.

关键词： Reconfigurable robot

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Modeling method of transformer sensor communication model based on intelligent algorithm 10

Modeling method of transformer sensor communication model ba...

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10th International Forum on Electrical engineering and Automation, IFEEA 2023

作者： Yi, Chenying Zhou, Yangjun Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Guangxi Power Grid Equipment Monitoring and Diagnosis Engineering Technology Research Center Nanning China

ISBN: (纸本)9798350309379

In this paper, a modeling method of sensor communication model of distribution transformer based on intelligent algorithm is studied. By this method, the reliability of distribution transformer can be improved, thus the reliability of distribution network with distributed energy access can be improved. This paper includes communication model establishment and solution, improved communication model establishment, improved communication model solution, improved communication model verification and comparison. As the test time becomes longer, the sensor itself will heat up, which will lead to the temperature rise of the surface and its surrounding environment. The traditional communication model does not take these excellent factors into account, and ignores the AC characteristics based on the DC characteristics. The improved communication model of the method in this paper takes into account the influence of temperature, taking into account both DC characteristics and AC characteristics, and accurately calculates the use limit temperature of the sensor, thus improving the reliability. The methods of error and goodness-of-fit test show that the method is far superior to the traditional methods. © 2023 IEEE.

关键词： Distributed energy access Distribution transformer Establishment of communication model Genetic algorithm Reliability of distribution network

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Optimization for Pickup Vehicle Scheduling in Steel Logistics Park Based on Enhanced SPEA2

Optimization for Pickup Vehicle Scheduling in Steel Logistic...

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2023 China Automation Congress, CAC 2023

作者： Xu, Hongjie Wang, Jinlong Xu, Zhezhuang Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming353000 China

ISBN: (纸本)9798350303759

Pickup vehicle scheduling in the steel logistics park is a critical issue in determining the outbound effciency of steel products. Steel products are distributed in the yards of the steel logistics park with mixed storage, and the optional yards for each pickup operation are not unique, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize makespan of pickup vehicle and makespan of steel logistics park simultaneously. The optimization problem is described as a multi-objective problem, and an enhanced Strength Pareto Evolutionary Algorithm 2 (ESPEA) is proposed to solve the problem with high efficiency. Experiments are executed based on data collected from a real steel logistics park. The results confirm that ESPEA can significantly reduce both makespan of each pickup vehicle and makespan of steel logistics park. © 2023 IEEE.

关键词： Pickups

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VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness

arXiv

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arXiv 2024年

作者： Cai, Xuan Cui, Zhiyong Bai, Xuesong Ke, Ruimin Ma, Zhenshu Yu, Haiyang Ren, Yilong State Key Laboratory of Intelligent Transportation Systems School of Transportation Science and Technology Beihang University Beijing100191 China Department of Civil and Environmental Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Zhongguancun Laboratory Beijing100191 China

Autonomous vehicles (AVs) face significant threats to their safe operation in complex traffic environments. Adversarial training has emerged as an effective method of enabling AVs to preemptively fortify their robustness against malicious attacks. Train an attacker using an adversarial policy, allowing the AV to learn robust driving through interaction with this attacker. However, adversarial policies in existing methodologies often get stuck in a loop of overexploiting established vulnerabilities, resulting in poor improvement for AVs. To overcome the limitations, we introduce a pioneering framework termed Vulnerability-aware and Curiosity-driven Adversarial Training (VCAT). Specifically, during the traffic vehicle attacker training phase, a surrogate network is employed to fit the value function of the AV victim, providing dense information about the victim’s inherent vulnerabilities. Subsequently, random network distillation is used to characterize the novelty of the environment, constructing an intrinsic reward to guide the attacker in exploring unexplored territories. In the victim defense training phase, the AV is trained in critical scenarios in which the pretrained attacker is positioned around the victim to generate attack behaviors. Experimental results revealed that the training methodology provided by VCAT significantly improved the robust control capabilities of learning-based AVs, outperforming both conventional training modalities and alternative reinforcement learning counterparts, with a marked reduction in crash rates. The code is available at https://***/caixxuan/VCAT. © 2024, CC BY.

关键词： Adversarial machine learning

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Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning

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Journal of Central South University 2022年第12期29卷 3956-3973页

作者： LI Xian-ling ZHANG Jian-feng ZHAO Chun-hui DING Jin-liang SUN You-xian Science and Technology on Thermal Energy and Power Laboratory Wuhan 2nd Ship Design and Research InstituteWuhan 430205China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China State Key Laboratory of Synthetical Automation for Process Industries(Northeastern University) Shenyang 110819China

With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficiently extract deep meaningful features that are crucial for fault diagnosis, a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label dimension. The feature space is described by the one-hot encoding of the fault category label as an orthogonal basis. In this way, the deep sparse Gaussian features related to fault categories can be gradually learned from the raw data by SGFE. In the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian distributions. The sparse Gaussian features are linearly separable in the feature space, which is conducive to improving the accuracy of the downstream fault classification task. The feasibility and practical utility of the proposed SGFE are verified by the handwritten digits MNIST benchmark and Tennessee-Eastman(TE) benchmark process,respectively.

关键词： nonlinear fault diagnosis multiple multivariate Gaussian distributions sparse Gaussian feature learning Gaussian feature extractor

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Classification of Dunhuang Mural Image Based on Small-sample and Semi-supervised Learning 6

Classification of Dunhuang Mural Image Based on Small-sample...

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6th IEEE Information technology, Networking, Electronic and Automation control Conference, ITNEC 2023

作者： Cui, Hui Su, Zhibin Zhang, Luyue Bai, Liyang Communication University of China School of Information and Engineering China Key Laboratory of Acoustic Visual Technology and Intelligent Control System China Ministry of Culture and Tourism P.R.C. Center for Ethnic and Folk Literature and Art Development China

ISBN: (纸本)9781665460033

Dunhuang mural images classification belongs to the research task in the field of image recognition. In this paper, the semi-supervised model is established with multidimensional features extracted by transfer learning. A small number of labeled samples were used to obtain a large number of unlabeled data, combined with Active Learning and iterative strategy for multiple rounds of label transfer of selected samples. After several rounds of iterations, we can get a more powerful classification learner. Experiments on the self-built Dunhuang mural dataset have shown that the results can approach or even exceed some supervised learning methods when the number of known label samples is less than 4%. Our research implements the cultural resource image classification algorithm based on small samples, which is conductive to improving the accuracy when label samples are scarce. © 2023 IEEE.

关键词： Image recognition

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Universal Trajectory Optimization Framework for Differential-Driven Robot Class

arXiv

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arXiv 2024年

作者： Zhang, Mengke Chen, Nanhe Wang, Hu Qiu, Jianxiong Han, Zhichao Ren, Qiuyu Xu, Chao Gao, Fei Cao, Yanjun The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China

Differential-driven robots are widely used in various scenarios thanks to their straightforward principle, from household service robots to disaster response field robots. There are several different types of deriving mechanisms considering the real-world applications, including two-wheeled, four-wheeled skid-steering, tracked robots, etc. The differences in the driving mechanism usually require specific kinematic modeling when precise controlling is desired. Furthermore, the nonholonomic dynamics and possible lateral slip lead to different degrees of difficulty in getting feasible and high-quality trajectories. Therefore, a comprehensive trajectory optimization framework to compute trajectories efficiently for various kinds of differential-driven robots is highly desirable. In this paper, we propose a universal trajectory optimization framework that can be applied to differential-driven robot class, enabling the generation of high-quality trajectories within a restricted computational time-frame. We introduce a novel trajectory representation based on polynomial parameterization of motion states or their integrals, such as angular and linear velocities, that inherently matching robots’ motion to the control principle for differential-driven robot class. The trajectory optimization problem is formulated to minimize complexity while prioritizing safety and operational efficiency. We then build a full-stack autonomous planning and control system to show the feasibility and robustness. We conduct extensive simulations and real-world testing in crowded environments with three kinds of differential-driven robots to validate the effectiveness of our approach. We will release our method as an open-source package Copyright © 2024, The Authors. All rights reserved.

关键词： Four wheel steering

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Prolonged Microcystis Restraint Through Allelochemicals Sustained-Release Microspheres Regulated by Carbon Material (Cm-Ac@Sm): Optimal Construction, Characterization, Effects and Synergistic Inhibition Mechanisms

SSRN

引用

SSRN 2024年

作者： Sun, Lei Zhang, Liqiu Han, Qi Feng, Li Beijing Key Laboratory for Source Control Technology of Water Pollution Engineering Research Center for Water Pollution Source Control and Ecoremediation Beijing Forestry University Beijing100083 China

The M. aeruginosa blooms have caused serious damage to ecological environment. Microspheres containing allelochemicals with sustained-release properties have the potential to function as a cost-effective and environmentally friendly algaecide against M. aeruginosa. In the current investigation, we successfully optimized the construction of allelochemicals sustained-release microspheres regulated by carbon material (CM-AC@SM), which demonstrated a high embedding rate (90.17%) and loading rate (0.65%), with an accumulative release rate of 53.27% on day 30. To investigate the sustained-release mechanism of CM-AC@SM, the sustained-release process of allelochemicals was determined using the Folin-Phenol method and the immersion behavior of the CM-AC@SM was characterized through SEM and XPS. Results showed that allelochemicals were released in the delayed-dissolution mode. In addition, to elucidate the synergistic mechanism of CM-AC@SM towards the inhibition of M. aeruginosa, this study comprehensively assessed the effects of allelochemicals, carbon material and CM-AC@SM on the morphology, antioxidant system activity and photosynthetic activity of M. aeruginosa. The findings indicated that allelochemicals and carbon material induced intracellular protein and nucleic acid leakage by increasing cell membrane permeability, disrupted the extracellular and intracellular morphology of algae, triggered peroxidative damage and restrained antioxidant system activity by stimulating the generation of reactive oxygen species. Simultaneously, the activity of photosystem II was inhibited by allelochemicals and carbon material, substantiated by the reduction in Fv/Fo and Fv/Fm ratios. Hence, CM-AC@SM shows promise in inhibiting M. aeruginosa, offering an efficient approach for the future large-scale control of harmful algal blooms (HABs). © 2024, The Authors. All rights reserved.

关键词： Microspheres

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Model-Based Reinforcement Learning for Robotic Arm control with Limited Environment Interaction 13

Model-Based Reinforcement Learning for Robotic Arm Control w...

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13th IEEE International Conference on CYBER technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Chen, Yi Sun, Xiao Zheng, Song Huang, Weibing Zeng, Sheng Chen, Dan Xu, Zhezhuang College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou University Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Shenzhen Guangdong518000 China

ISBN: (纸本)9798350315196

Reinforcement Learning (RL) has been applied to robotic arm control, which enables the agent to learn an effective policy to solve complex tasks. However, it requires constant interaction with the environment leading to low sample efficiency. In this paper, we propose a robotic arm control approach based on planning via lookahead search, which is a model-based RL algorithm to improve the sample efficiency. The approach builds an environment model in order to obtain the dynamics of the environment. Thus the model can be used to plan future actions by a tree-based search. The experiments show that our approach can solve the task of robotic arm control with less environmental samples. © 2023 IEEE.

关键词： Robotic arms

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