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Simulation of Gas Oil Hydrotreater Heat Exchange Tube and Crystallization Prediction of NH_(4)Cl by Thermodynamic Equilibrium

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China Petroleum Processing & Petrochemical technology 2023年第2期25卷 142-154页

作者： Jin Haozhe Liu Xinyu Liu Xiaofei Gu Youjie Li Xiaojun Fu Dexiao Institute of Flow-Induced Corrosion Zhejiang Sci-Tech UniversityHangzhou 310018 Key Laboratory of Fluid Transmission Technology of Zhejiang Province Zhejiang Sci-Tech UniversityHangzhou 310018 Institute of Flow-Induced Corrosion and Intelligent Prevention and Control Changzhou UniversityChangzhou 213164

The hydrotreater system heat exchanger is one of the main pieces of heat exchange equipment in petrochemical *** recent years,oil resources have shown a deterioration trend of high sulfur and high acid content,with corrosion risk being prominent in oil *** the multi-medium flow corrosion risk of the hydrotreater heat exchanger pipeline in a petrochemical enterprise as the research object,based on the parameter characteristics of corrosive NH_(3) and HCl media under a high-temperature and high-pressure environment,the ammonium salt crystallization and deposition mechanism under multi-phase flow is *** thermodynamic equilibrium curve is modified based on the thermodynamic principle and fugacity coefficient variation,and the prediction model of ammonium chloride crystallization in hydrotreater heat exchanger under high temperature and high pressure is constructed according to the *** study uses the mixture model,the flow-thermal coupling method,and the discrete phase model method to carry out the numerical simulation of multiphase flow and the numerical prediction of particle distribution characteristics in the heat exchanger pipeline of the hydrotreater heat exchange equipment,so as to realize the quantitative prediction of the particle crystallization deposition distribution in the *** results show that with the decrease of temperature,the crystallization occurs first on both sides of the center of the tube bundle,and more crystallization occurs in the lower half of the U-shaped tube,which may seriously lead to problems such as pipe blockage and under-deposit corrosion.

关键词： hydrotreating processing process modeling thermodynamic equilibrium ammonium salt crystallization numerical simulation and prediction

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One-Stage Object Detector Using Feature Fusion and Dual Attention 8

One-Stage Object Detector Using Feature Fusion and Dual Atte...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Di Zhang, Weimin Li, Fangxing Liang, Kaiwen Yang, Yuhang Zhong, Zhide School of Mechatronical Engineering Beijing Institute of Technology China Beijing Institute of Technology Ministry of Education Key Laboratory of Biomimetic Robots and Systems China Beijing Advanced Innovation Center for Intelligent Robots and Systems China

ISBN: (纸本)9798350300178

Multi-scale object detection and small object detection are important tasks in computer vision and robotics. Traditional one-stage object detection algorithms are fast, but cannot adapt to changes in object scale perfectly and have low accuracy for small object detection. In this paper, we proposed a one-stage object detector using feature fusion and attentional mechanisms. This detector has better adaptability and higher accuracy with marginal extra cost. Our algorithm is based on the CenterNet framework, with modifications and enhancements. First, we used a feature pyramid network with bidirectional cross-scale connections to enable the full integration of lower layers and topmost features, which improved the network's adaptability to scale changes. Second, we proposed a dual attention module that integrates spatial attention and channel attention and added it to every layer of the pyramid, so that the network can focus more on useful information, which enhances the network's capability to detect small objects. Using a lightweight backbone, our algorithm achieves 40.2 mAP on COCO at 33 fps on a Titan Xp GPU, up to 3 mAP compared to CenterNet with the same backbone. Besides, for the small targets in COCO, our algorithm achieves 20.63 mAP, Which is 30% higher than CenterNet. We also tested our algorithm on a body part detection dataset of Pascal, Voc,which further demonstrates the effectiveness of our algorithm in terms of application. © 2023 IEEE.

关键词： Object detection

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Autonomous drilling and deburring processing planning method of multi-robot systems

Autonomous drilling and deburring processing planning method...

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2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous robots, SIMPAR 2025

作者： Zhang, Tianyi Wang, Yongqing Li, Te Liang, Bochao Shi, Shaohan Liu, Haibo Dalian University of Technology State Key Laboratory of High-Performance Precision Manufacturing Dalian116024 China Dalian University of Technology Dalian 116024 State Key Laboratory of High-Performance Precision Manufacturing Intelligent Manufacturing Longcheng Laboratory Changzhou213164 China

ISBN: (纸本)9798331516857

Large workpieces are characterized by enormous size and complex structures. Traditional single-machine manufacturing methodologies encounter challenges such as inefficiency, inconsistent quality, and spatial constraints. Conversely, multi-robot systems exhibit high processing flexibility, providing a suitable foundation for producing large workpieces. However, as the number of tasks and robots increases, programming methods that rely on human instruction will become increasingly complex and inefficient. Consequently, this paper proposes a multi-process trajectory planning procedure for multi-robot systems. This paper realizes multi-robot and multi-task allocation, path planning, and collision avoidance based on the distributed optimization strategy of predicting subsequent constraints. The gray wolf algorithm, augmented with local search strategies, is utilized for task allocation and path planning. The processing trajectory is further optimized by rotating the starting point and task process direction. Ultimately, a delay-waiting-based time scheduling algorithm is employed to ensure the safety and reliability of the trajectory. This paper validates the planning efficacy of the entire process through simulations. The results validate the effectiveness of the proposed procedure in planning processing trajectories for large workpieces, ensuring optimal processing efficiency while maintaining the safety and reliability of the trajectory. © 2025 IEEE.

关键词： Multipurpose robots

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Distributed Solver for Discrete-Time Lyapunov Equations over Dynamic Networks with Linear Convergence Rate

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IEEE Transactions on Cybernetics 2022年第2期52卷 937-946页

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

The problem of solving discrete-Time Lyapunov equations (DTLEs) is investigated over multiagent network systems, where each agent has access to its local information and communicates with its neighbors. To obtain a solution to DTLE, a distributed algorithm with uncoordinated constant step sizes is proposed over time-varying topologies. The convergence properties and the range of constant step sizes of the proposed algorithm are analyzed. Moreover, a linear convergence rate is proved and the convergence performances over dynamic networks are verified by numerical simulations. © 2013 IEEE.

关键词： Convex optimization

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Swin-BFB-UNet: A swin transformer-based UNet combined with bottleneck fusion block for vertebra segmentation

Swin-BFB-UNet: A swin transformer-based UNet combined with b...

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2024 International Conference on Computer Vision and Image Processing, CVIP 2024

作者： Gao, Yifan Li, Min Miao, Hongxia Jiang, Junfeng College of Information Science and Engineering Hohai University Jiangsu Changzhou China College of Artificial Intelligence and Automation Hohai University Jiangsu Changzhou China Key Laboratory of Maritime Intelligent Cyberspace Technology Hohai University Ministry of Education Jiangsu Changzhou China

ISBN: (纸本)9781510688384

Segmenting the vertebrae is an important part of diagnosing spine diseases and planning surgery. The vertebra's complex anatomy, featuring similar shapes of neighboring bones and vague boundaries, presents substantial challenges for precise semantic segmentation. Traditional methods often depend heavily on local features, making it difficult to capture global context and resulting in less accurate recognition. Consequently, automated and efficient segmentation approaches are crucial to overcoming these issues. This study introduces an innovative Swin-BFB-UNet architecture derived from UNet to enhance vertebra segmentation. We include the Swin Transformer into the encoder to capture both global and local information, hence enhancing vertebra segmentation accuracy. The bottleneck fusion block (BFB) fuses multi-scale features and semantic information, enhancing the model's feature representation. Furthermore, we employ both Dice Loss and Cross-Entropy Loss to alleviate the adverse effects of foreground-background area imbalance. We conducted annotations and experiments on the lateral X-ray images in the BUU-LSPINE dataset. The experimental findings indicate that our model exceeds other leading methodologies, attaining a mDice of 90.3%, hence evidencing its proficiency in precise vertebra segmentation. © 2025 SPIE.

关键词： Semantic Segmentation

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Environment-Tolerant Trust Opportunity Routing based on Reinforcement Learning for Internet of Underwater Things

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6348-6360页

作者： He, Yu Han, Guangjie Hou, Yun Lin, Chuan Hohai University College of Information Science and Engineering Changzhou213200 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Changzhou213200 China Hohai University College of Computer Science and Software Engineering Nanjing211100 China Northeastern University Software College Shenyang110819 China

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing method

关键词： Reinforcement learning

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Enhancing lithium-ion and electric conductive Li_(2)FeSiO_(4)cathode through in situ boron-doping and carbon-coating strategy

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Rare Metals 2022年第12期41卷 4055-4064页

作者： Hao-Xiang Li Jia-Hui Zhu Xiao-Bing Huang Tao Zhou Yu-Rong Ren Hunan Provincial Key Laboratory of Chemical Power Sources College of Chemistry and Chemical EngineeringCentral South UniversityChangsha 410083China Hunan Provincial Key Laboratory of Water Treatment Functional Materials Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion AircraftCollege of Chemistry and Materials EngineeringHunan University of Arts and ScienceChangde 415000China Jiangsu Province Engineering Research Center of Intelligent Manufacturing Technology for the New Energy Vehicle Power Battery Changzhou Key Laboratory of Intelligent Manufacturing and Advanced Technology for Power BatterySchool of Materials Science and EngineeringChangzhou UniversityChangzhou 213164China

The extremely low electrical conductivity and ion-diffusion coefficient of Li_(2)FeSiO_(4)limits its application as a cathode material in lithium-ion ***,in situ boron-doped Li_(2)FeSi_(1)-xB_(x)O_(4-δ)/C(x=0,0.01,0.03,0.05 and 0.07)at the Si site was prepared via the solid-state reaction method using pitch as the c arbon source.B doping in the lattice structure and a c arbon coating on the surface of the composites could effectively enhance the Li^(+)/electron ***,the reduced particle size of the active material with the relatively high specific area via borondoped modification could improve the wettability between the electrolyte and *** the synergistic effect of appropriate boron doping and carbon coating,it exhibits a good rate performance,specific capacity,and cycling *** a result,the as-prepared Li_(2)FeSi_(0.95)B_(0.05)O_(4-δ)/C cathode showed a high discharge capacity of 160.7mAh·g^(-1)at 0.2C,and the capacity retention rate was 96%after 100 cycles at *** work presents an effective path for designing advanced cathode materials for lithium-ion batteries.

关键词： Li_(2)FeSiO_(4) Carbon coating Boron doping Synergistic effect Lithium-ion battery

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RS-Fusion: A novel virtual reality localization method based on RTK and visual SLAM

RS-Fusion: A novel virtual reality localization method based...

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2022 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2022

作者： Li, Zhitian Zhang, Weimin Tian, Ye Li, Fangxing School of Mechatronical Engineering Beijing Institute of Technology Beijing100811 China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education China Beijing Advanced Innovation Center for Intelligent Robots and Systems China

ISBN: (纸本)9781510663350

Virtual reality technology is a new multidisciplinary integrated technology, in which localization technology is the key technology to determine the user experience, and is the core of VR technology. RTK and visual slam are two common localization technologies, however they are limited by satellite conditions and rely heavily on feature point extraction and matching effects, which will affect the accuracy of localization process. So as to achieve high-precision virtual reality spatial localization, this paper proposed a novel virtual reality localization method called RS-fusion, which combined the RTK carrier phase difference technology and the visual SLAM technology by Kalman fusion. In addition, the gain matrix is used to calculate the spatial displacement state to meet the accuracy requirements of virtual reality and augmented reality localization in indoor and outdoor environment, and to obtain a more matching virtual reality fusion effect and simulation mapping. Experiment results show the robustness of the method and RS-fusion can realize the simultaneous localization of cameras in real space and virtual space. © 2023 SPIE.

关键词： Virtual reality

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Dynamic Obstacle Avoidance for Magnetic Helical Microrobots Based on Deep Reinforcement Learning

Dynamic Obstacle Avoidance for Magnetic Helical Microrobots ...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Qiu, Yukang Hou, Yaozhen Yang, Haotian Gao, Yigao Huang, Hen-Wei Shi, Qing Huang, Qiang Wang, Huaping Beijing Institute of Technology Intelligent Robotics Institute School of Mechatronical Engineering Beijing100081 China Harvard Medical School Laboratory for Translational Engineering CambridgeMA02139 United States Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350372601

Magnetic helical microrobots hold immense promise in biomedical domains owing to their compact size and efficient propulsion capabilities. However, navigating these microrobots through dynamic and unstructured environments, particularly when encountering numerous dynamic obstacles, remains a formidable challenge. In the study, a control framework based on deep reinforcement learning (DRL) with the objective of guiding a microrobot through dynamic obstacles towards specified target goals is introduced. Initially, we design and fabricate a microdrill capable of propulsion via external magnetic rotating fields produced by our magnetic actuation system. Subsequently, we construct a custom training environment, adhering to the OpenAI gym interface, to serve as the simulator for training purposes. Utilizing the proximal policy optimization algorithm, we conduct training of the navigation policy within this simulator. Simulations and experimental validations conducted in dynamic environments affirms the efficacy of the proposed method. © 2024 IEEE.

关键词： Microrobots

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Design of a Miniature Flapping-Wing Robot Capable of Quick and Energy-Efficient Takeoff

Design of a Miniature Flapping-Wing Robot Capable of Quick a...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Xu, Yi Shen, Yishi Peng, Liang Li, Qi Yu, Jin Zhang, Shi Shi, Qing Beijing Institute of Technology Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing100081 China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781665481090

Micro areal vehicles are widely used in civilian and military applications, but it is still challenging for them to take off in place with both high speed and energy utilization efficiency. Inspired by locusts, one solution is to integrate the flying with the jumping motion, utilizing the speed generated by jumping to provide sufficient aerodynamic forces for the flight. In this paper, we develop a miniature locust-inspired robot capable of jumping and flying. By coupling the jumpingleg mechanism and flapping-wing mechanism, the robot has a compacted size (with a body length of 15 cm and a weight of 23 g). Furthermore, we design a cascaded jump-flying controller to achieve stable transition and maneuverable flight. A series of experimental results reveal that the robot achieves a maximum jumping height of 50 cm and a minimum flying turning radius of 0.8 m. Remarkably, the robot is capable of taking off in place through controlling the hindleg and wings coordinately. © 2024 IEEE.

关键词： Microrobots

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