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    Supply Chain Resilience: Research Review and Prospects
    Zhong-zhong JIANG, Jia-run GUO, Wei ZHENG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 59-70.   DOI: 10.12068/j.issn.1005-3026.2025.20250055
    Abstract2367)   HTML72)    PDF(pc) (2764KB)(2679)       Save

    In recent years, compounded crises such as geopolitical conflicts (e.g., the Russia-Ukraine conflict) and technological containment (e.g., the China-U.S. trade friction) have continuously exerted a profound impact worldwide, revealing the vulnerabilities of global supply chains. Enhancing the supply chain resilience has become a critical strategy to ensure the sustainable development of countries around the world, especially China, and it serves as a vital foundation for making China strong in manufacturing. On this basis, existing research on supply chain resilience was comprehensively reviewed, with particular focus on its origins, conceptual definitions, and driving factors. The evolution of the research was systematically analyzed, and prospective research directions were explored from four dimensions: collaborative optimization, resource allocation, dynamic response, and risk management. The findings aim to provide theoretical support and decision reference for enhancing supply chain resilience both globally and within China.

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    Development and Prospects for Software‑Defined Intelligent Control Systems
    Tian-you CHAI, Rui ZHENG, Yao JIA, Xin-yu HUANG, Yan-jie SONG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 1-10.   DOI: 10.12068/j.issn.1005-3026.2025.20250079
    Abstract1474)   HTML84)    PDF(pc) (4009KB)(716)       Save

    The current state of research on software-defined control systems was reviewed, and the role and development of control systems throughout the industrial revolutions were analyzed. The intelligent development direction for software-defined control systems was proposed. The case study of a software-defined end-edge-cloud collaborative PID(proportional-integral-derivative) tuning intelligence system was presented, which demonstrates that the tight conjoining and coordination between industrial artificial intelligence, industrial Internet, and other new-generation information technologies with software‐defined control systems has opened up a new way for the development of software-defined intelligent control systems. Finally, the principal research directions for software-defined intelligent control systems were pointed out by considering the challenges faced by software-defined control systems and those specific to their intelligent transformation.

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    Review of Multi-type Energy Routers Research
    Qiu-ye SUN, Rong-da XING, Qian-xiang SHEN, Zhen-ao SUN
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 11-21.   DOI: 10.12068/j.issn.1005-3026.2025.20250063
    Abstract1158)   HTML17)    PDF(pc) (1261KB)(754)       Save

    Energy routers (ERs) are one of the core components of the energy Internet for achieving multi-port energy conversion and active energy flow control. This paper classified ERs into three categories: electrical ERs, information ERs, and multi-energy ERs. Based on the differences between these categories, the research on ERs is divided into four aspects: electrical conversion, focusing on topology and control of multi-port electrical conversion; energy routing control, primarily concerned with the regulation of power flow between ports of ERs; information processing and optimal control, emphasizing the acquisition and transmission of information and optimizing energy flow; and multi-energy coordination, with multi-energy comprehensive utilization as the main goal. Based on these four research aspects, this paper explored topology, control, communication, and multi-energy optimization of ERs, as well as the interrelationships between different aspects.

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    Research Progress on Development and Application of Digital Blast Furnace Ironmaking Technology
    Man-sheng CHU, Guo-dong WANG, Jue TANG, Quan SHI
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 113-130.   DOI: 10.12068/j.issn.1005-3026.2025.20250070
    Abstract1069)   HTML14)    PDF(pc) (4726KB)(2131)       Save

    With the advancement of the digital information era, the digital transformation of blast furnaces has begun. Steel enterprises have applied intelligent closed-loop control, digital twins, and AI-based predictive models to develop intelligent systems for smart blast furnace operation, blast furnace condition assessment, and quality optimization. Research on digital blast furnaces primarily focuses on variable prediction, state diagnosis, and blast furnace condition optimization, with these domains evolving from traditional approaches toward complex optimization modeling, multidimensional comprehensive evaluation, and multi-objective collaborative optimization, respectively. However, current predictive models require enhanced online self-updating and integration of data and mechanisms; evaluation systems need to emphasize multidimensional and fine-grained diagnostics, and blast furnace condition optimization has to overcome single-indicator limitations by focusing on low-risk, low-cost, and multi-objective coupled strategies. According to the actual needs of the blast furnace site, a physical system of blast furnace information was developed, where data, mechanisms, and experience were reasonably matched and called upon to form an integrated technology encompassing data governance, rule mining, intelligent prediction, comprehensive evaluation, multi-objective optimization, and decision feedback, which was identified as one of the key directions for future development of digital blast furnace ironmaking.

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    Intelligent Identification Method of Industrial Mixed Gases Based on ConvGRU Fusion Attention Mechanism
    Fan-li MENG, Shu-chang LI, Hao WANG, Zhen-yu YUAN
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 37-48.   DOI: 10.12068/j.issn.1005-3026.2025.20240164
    Abstract1058)   HTML22)    PDF(pc) (4835KB)(388)       Save

    To address the issue of high data dependency and insufficient accuracy in mixed gas identification for traditional semiconductor gas sensors, a ConvGRUAttention network model that integrates gated recurrent units (GRU), convolutional layers, and attention mechanism is proposed. Empirical wavelet transform (EWT) is employed to convert raw signals into the time-frequency domain and perform multi-scale decomposition, which suppresses noise, reduces data dependency, and enhances the model’s robustness. The model extracts local dynamic features through convolutional layers, captures long-term dependencies using GRU, and optimizes feature weights across multi-scale signals via the attention mechanism, thereby improving feature extraction and generalization capabilities. Experimental results demonstrate 100% accuracy in qualitative identification and a root mean square error (RMSE) of 3.3×10⁻⁶ in quantitative detection. Compared with the traditional methods, the detection accuracy for mixed gases is significantly improved.

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    Progress and Application of Intelligent Manufacturing Technology
    Ya-dong GONG, Jia-hao GAO, Li-ya JIN, Heng ZHAO
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 84-93.   DOI: 10.12068/j.issn.1005-3026.2025.20250053
    Abstract1056)   HTML37)    PDF(pc) (4156KB)(1503)       Save

    Based on an analysis of the extensive and profound impact of the development of artificial intelligence on manufacturing technology, it is expounded that intelligent manufacturing is the main direction for building a strong manufacturing country, and its important roles are discussed in the construction of a new industrialization system. Through an introduction to intelligent manufacturing empowerment technology, it is claimed that intelligent manufacturing technology by the deep integration of intelligent technology and advanced manufacturing technology will drive the progress of the new round of industrial revolution and industrial transfor-mation. Subsequently, typical research results and application examples of intelligent manufacturing are used to clarify the essential differences and engineering applications between intelligent manufacturing and traditional automation. Finally, it is pointed out that the advanced production mode of the deep integration of new generation information technology and advanced manufacturing technology demonstrates a new form of future manufacturing industry.

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    Research on Localization of Industrial Intelligent Inspection Robots in Cable Tunnel Environment
    Yu-tao WANG, Jun-wei AN, Chang-sheng QIN, Wei-fan GUO
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 49-58.   DOI: 10.12068/j.issn.1005-3026.2025.20240212
    Abstract981)   HTML17)    PDF(pc) (5943KB)(145)       Save

    The cable tunnel is closed and narrow, with repetitively laid cable racks and similar scene textures, which is a degraded scenario. To address this environment, a visual-inertial SLAM (simultaneous localization and mapping) algorithm based on point-line feature fusion is proposed. The algorithm improves the high-dimensional line features through length suppression and short line fitting to make it more effective in describing the structural features of tunnel scene. In addition, for the problem of loop closure detection failure due to feature similarity in cable tunnels, ArUco markers with efficient recognition and accurate pose estimation are introduced to limit the loop closure area, and the optimal loop closure frames are selected using the minimized pose transformation to improve detection accuracy and localization precision. Finally, dataset collection and experimental validation were conducted in actual cable tunnels. The results show that the absolute trajectory accuracy of the algorithm is improved by 69.73% on average relative to VINSMono(visual intertial system-Mono), which meets the application requirements of cable tunnel inspection.

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    Digital Twin Fault Diagnosis Method of Power Transformer Based on Industrial Intelligence
    Jian FENG, Bo-wen ZHANG, Ning ZHAO, Hui-jie JIANG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 22-29.   DOI: 10.12068/j.issn.1005-3026.2025.20240218
    Abstract921)   HTML18)    PDF(pc) (4393KB)(261)       Save

    As a key development direction integrating new-generation information technology with advanced manufacturing techniques, industrial intelligence leverages intelligent, digital, and automated methods to significantly enhance industrial production efficiency and optimize the prediction and maintenance management of industrial equipment. This paper focuses on the intelligentization of industrial equipment, with the goal of ensuring the efficient and stable operation of power transformers within power systems. A digital twin model for transformer inter-turn short circuit faults is constructed based on electromagnetic field equations and equivalent circuit models. The model analyzes the symmetry of the transformer in both normal and fault conditions from an electromagnetic field perspective, thereby integrating digital twin technology with fault diagnosis. Furthermore, through in-depth analysis of the virtual model of the transformer, the location of faults is accurately identified, ensuring the safe operation of the transformer, improving its reliability and efficiency, and advancing the intelligentization and modernization of the entire power system.

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    Electric Vehicle Charging Scheduling Strategy Based on Safe Reinforcement Learning Algorithm
    Heng-xin PAN, Run-da JIA, Shu-lei ZHANG
    Journal of Northeastern University(Natural Science)    2025, 46 (5): 1-9.   DOI: 10.12068/j.issn.1005-3026.2025.20230183
    Abstract888)   HTML37)    PDF(pc) (1237KB)(1087)       Save

    As the number of electric vehicles (EVs) increases, reinforcement learning (RL) in EV charging scheduling faces challenges, particularly uncertainties and the curse of dimensionality from large‑scale applications. A microgrid model for residential areas, considering the vehicle‑to‑grid (V2G) mode and various nonlinear charging models is developed. The problem is formulated as a constrained Markov decision process (CMDP), with a model‑free RL framework to handle uncertainties. To address the curse of dimensionality, a strategy is designed where EVs are grouped by states, and agents send control signals to these sets, thus reducing the dimensionality of the action space. A Lagrangian deep deterministic policy gradient (LDDPG) algorithm is employed to solve the charging scheduling problem, with a safety filter ensuring constraint compliance. Numerical simulations validate the strategy’s effectiveness.

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    Intelligent Microseismic Monitoring and Early Warning for Rock Burst During TBM Excavation of Deep Metal Mines
    Bing-rui CHEN, Xu WANG, Gui-peng JIANG, Fei HE, Jia-lin HAN, Jian-jun HAO
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 148-162.   DOI: 10.12068/j.issn.1005-3026.2025.20250085
    Abstract843)   HTML15)    PDF(pc) (10803KB)(1027)       Save

    In response to the problem of insufficient automation and intelligence in the microseismic monitoring and early warning for rock bursts during tunnelling boring machine (TBM) excavation of deep metal mines, research on multi-dimensional parameter recognition of drilling holes based on deep machine vision DPED-AT method was conducted; automatic disassembly and assembly device for microseismic sensors was developed, and the decision-making system was designed, achieving automatic disassembly and assembly of microseismic sensors during TBM excavation. Microseismic intelligent frequency conversion acquisition technology was developed, realizing continuous and high-fidelity acquisition of rock rupture information during the rock burst incubation process. An improved neural network algorithm for identifying and picking up rupture signals was proposed, as well as a three-dimensional characterization algorithm for the probability field of microseismic sources induced by rock bursts incubation. Intelligent interpretation and refined early warning of rock burst incubation information during TBM excavation were preliminarily realized, and an intelligent monitoring and early warning technology system for rock burst that integrated intelligent drilling hole recognition, automatic sensor disassembly and assembly, and intelligent signal acquisition and interpretation was ultimately established. The application in Ruihai Gold Mine shows that it has achieved automatic microseismic monitoring, interpretation, and early warning of rock burst, providing strong support for less manned and unmanned TBM excavation in deep metal mines.

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    Research on Detection of Alzheimer Disease Based on Image Fusion Technology
    Zhi-gang LI, Ming-kai MU, De-an HU, Nan XIANG
    Journal of Northeastern University(Natural Science)    2025, 46 (6): 1-7.   DOI: 10.12068/j.issn.1005-3026.2025.20230338
    Abstract783)   HTML41)    PDF(pc) (2142KB)(508)       Save

    The plasma samples of Alzheimer disease(AD) patients are collected using Fourier transform infrared-attenuated total reflection (FTIR-ATR) spectroscopy technology. Based on the FTIR-ATR spectral data of the plasma membrane samples, the spectral data are encoded into two-dimensional images by utilizing the Gram angular field (GAF) and Markov transition field (MTF). Meanwhile, a neural network model based on the deep residual networks and attention mechanism is combined to conduct the screening and classification research on Alzheimer disease. The experimental results show that the GAF-MTF-CNN model can effectively improve the accuracy of spectral feature extraction. Additionally, the method of combining two-dimensional data with deep learning has better classification accuracy compared with traditional classification methods. Encoding spectrum into images using GAF and MTF techniques, and combining them with an improved residual neural network, effectively enhances the generalization ability and diagnostic accuracy of AD screening models, optimizing the screening performance.

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    Analytical Modeling and Performance Evaluation of Multi-stage Assembly Lines with Line-Side Buffers
    Peng-hao CUI, Qi-man ZHANG, Zhong-zhong JIANG, Guo-jun SHENG
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 71-83.   DOI: 10.12068/j.issn.1005-3026.2025.20240193
    Abstract769)   HTML5)    PDF(pc) (2384KB)(428)       Save

    The output performance of assembly lines is not only affected by machine unreliability and limited buffer capacity but also constrained by line-side buffers. The analytical modeling and performance evaluation of multi-stage assembly lines with line-side buffers were investigated. Firstly, for the single-stage assembly lines, the steady-state probability distribution of system states was derived based on Markov chains. Secondly, for the two-stage assembly lines, each single-stage subsystem was modeled as a machine with one operational state and one failure state. A performance evaluation model was then established using Markov chains, and closed-form expressions for performance indicators were obtained. Thirdly, for the multi-stage assembly lines, an aggregation method was proposed to approximate the performance indicators. Furthermore, the accuracy of the performance evaluation method was validated through numerical experiments. Finally, utilizing the proposed method, numerical experiments were conducted to examine system properties, such as reversibility and monotonicity in the multi-stage assembly lines.

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    Perception Technology and Application of Complex Urban Traffic Environment Based on Target Detection
    Aisan XIERAILI, De-fu CHE, Duo WANG, Tian YU
    Journal of Northeastern University(Natural Science)    2025, 46 (5): 29-36.   DOI: 10.12068/j.issn.1005-3026.2025.20230297
    Abstract742)   HTML14)    PDF(pc) (4528KB)(244)       Save

    Machine vision-based environmental perception technology is one of the key tasks in the field of intelligent transportation. Traditional deep learning algorithms typically meet the detection needs of individual targets in simple scenarios. However, they are not capable of addressing the intelligent perception requirements in complex traffic environment. To improve the intelligent perception capability of vehicles in such environment, this paper proposes an improved YOLOv8 object detection network model, integrating attention mechanisms, optimizers, and deformable convolutional layers to achieve multi-target detection in complex urban traffic environment. To verify the effectiveness of the algorithm, comparative experiment were conducted using YOLOv4, YOLOv8, and the improved YOLOv8 algorithm on sample images from complex traffic environments. The results show that, compared to YOLOv4 and YOLOv8, the improved YOLOv8 algorithm increased the average accuracy by 40.76% and 16.92%, respectively. The detection accuracy and real-time performance of the improved YOLOv8 algorithm meet the practical application requirements, and through multi-sensor information fusion, it can realize intelligent perception in complex urban traffic environment.

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    Charging and Discharging Scheduling for Electric Vehicles Based on Improved Multi-objective Chaotic Particle Swarm Optimization
    Zhi-ao CAO, Chen-shuo MA
    Journal of Northeastern University(Natural Science)    2025, 46 (9): 1-8.   DOI: 10.12068/j.issn.1005-3026.2025.20240014
    Abstract738)   HTML66)    PDF(pc) (1428KB)(325)       Save

    To address the issue of charging and discharging scheduling for EVs(electric vehicles), an orderly charging and discharging algorithm that considered users’ comprehensive satisfaction was proposed. Firstly, a large-scale orderly charging and discharging model for EVs was constructed, and users’ comprehensive satisfaction was quantified. Secondly, an improved multi-objective role partitioning chaotic particle swarm optimization(IMRPC-PSO) algorithm was proposed to solve the problems of insufficient diversity and being trapped in local optimal in traditional methods. According to the performance of particles, the roles of elite particles, general particles, and learning particles were assigned, which respectively implement diversity strategies of maintaining search, developing search, and learning search. Each particle searched the optimization space according to its assigned role. To avoid falling into local optimal, a chaotic sequence perturbation was added after the initialization of each iteration. Finally, the performance of the proposed algorithm was compared with that of the other five multi-objective optimization algorithms through case simulation. The results show that IMRPC-PSO is superior to other algorithms in solving the problem of orderly charging and discharging of EVs, verifying the effectiveness and feasibility of the proposed algorithm.

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    Feature Extraction and Motion Tracking of Planar Fillet Weld Seams Based on 3D Point Cloud
    Hai-bin WU, Wu-kai HUANG
    Journal of Northeastern University(Natural Science)    2025, 46 (6): 93-101.   DOI: 10.12068/j.issn.1005-3026.2025.20239074
    Abstract697)   HTML22)    PDF(pc) (2906KB)(957)       Save

    A feature extraction and trajectory planning strategy for planar fillet welds based on 3D point cloud was proposed to solve the automatic identification of weld seams and automatic robot tracking welding. Firstly, the workpiece to be welded was extracted based on the difference point cloud segmentation method, and the point cloud pre-processing was performed. Secondly, in order to obtain the feature points of the weld seam, the workpiece structure segmentation feature extraction algorithm was proposed. Then a path fitting method based on NURBS curves was fitted. Finally, a robot position estimation method for welding points was proposed to obtain the position of each path point for welding. This strategy is applicable to the weld seams of straight lines and various planar curves. The experimental results showed that the strategy can accurately extract the position of the fillet weld seam and generate the required position of track points, with the maximum error of each axis controlled within 1 mm and the total time consumed no more than 18 s, which provides a valuable reference for efficient automated welding.

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    Numerical Simulation Analysis of Prefabricated Steel-Tubular and Larsen Steel-Sheet Pile Cofferdam Structure
    Bai-ling CHEN, Jin-hui NIU, Lian-guang WANG, Gang XU
    Journal of Northeastern University(Natural Science)    2025, 46 (6): 102-112.   DOI: 10.12068/j.issn.1005-3026.2025.20230335
    Abstract688)   HTML10)    PDF(pc) (14725KB)(490)       Save

    To address the challenges faced by conventional steel-sheet pile cofferdams when driving into hard soil layers and the poor water-stopping effect of interlocking steel-tubular piles, a prefabricated steel-tubular and steel-sheet pile cofferdam was proposed, which fully utilized the advantages of high stiffness of steel-tubular piles and good water-stopping effect of steel-sheet piles. The numerical analysis of the structure using Abaqus shows that the steel-tubular piles at the corners greatly improve the peeling of the steel-sheet pile cofferdam corners and the high dependence on support. Increasing the number of steel-tubular piles can greatly improve the structural stress condition. Three supports, especially the bottom sealing concrete, are the most effective in suppressing the development of steel-sheet pile deformation. At the same time, this structure can effectively suppress the deformation of the riverbed soil. The maximum horizontal and vertical deformation of the soil occur mainly during the initial pumping stage and the dredging stage, accounting for 89.9% and 65.2% of the maximum deformation, respectively. The three supports and bottom sealing concrete of the structure can effectively suppress deformation during the pumping and dredging stages.

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    Defect Detection on PCB Based on Improved YOLOv8
    Zhen-zhen LYU, Li-jin FANG, Qian-kun ZHAO, Ying-cai WAN
    Journal of Northeastern University(Natural Science)    2025, 46 (10): 1-9.   DOI: 10.12068/j.issn.1005-3026.2025.20240038
    Abstract685)   HTML36)    PDF(pc) (10197KB)(134)       Save

    Due to the high integration, complex circuits, and increasing parameters of printed circuit boards (PCBs), defects in PCBs directly affect production efficiency, making computer vision-based defect detection crucial for PCB manufacturing. A self-attention-based PCB defect detection algorithm was proposed based on the YOLO object detection algorithm. First, a polarized self-attention (PSA) mechanism was introduced in the feature extraction stage to separately extract spatial and semantic features of PCBs, which were combined with input raw features to enhance the network’s feature representation capability. Then, a small-object detection head was added in the decoding stage, which fully utilized low-resolution features from the YOLO network Backbone module to enable the network to focus on local details of PCBs and improve defect positioning accuracy. Experiments show that the proposed method achieves 95.5% accuracy on the PCB dataset, 4% higher than the original YOLOv8 method, with the mAP0.5∶0.95 metric increased by 2.8%.

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    Multi-objective Optimization of Fiber Foam Concrete Based on Response Surface Analysis
    Shu-hong WANG, Hao-ran LI, Hong YIN, Fan GONG
    Journal of Northeastern University(Natural Science)    2025, 46 (6): 122-130.   DOI: 10.12068/j.issn.1005-3026.2025.20230340
    Abstract674)   HTML5)    PDF(pc) (3246KB)(740)       Save

    Foam concrete severs as a deformation reserve layer between the initial support and the secondary lining, effectively resisting the rheological deformation of the surrounding rock in deeply buried tunnels with high-ground stress. The incorporation of fibers enhances its compressive performance and ductility, addressing the issue of low compressive strength under specific fiber characteristics. Orthogonal test and Box-Behnken design method of response surface were used to systematically investigate the effects of various characteristics such as fiber mass fraction, length, and types, on the compressive strength and elastic modulus of foam concrete. A characteristic regression model was developed to optimize the mix ratio. The results show that the regression model established by the response surface method demonstrates high accuracy and reliability. Among the various fiber characteristics, the fiber mass fraction has the greatest impact on both the compressive strength and elastic modulus of foam concrete. Meanwhile, the interaction among multiple characteristics significantly influences compressive strength while slightly impacts elastic modulus. Particularly, the interaction between fiber length and fiber type has the most obvious impact on compressive strength. Furthermore, by maximizing the compressive strength and minimizing the elastic modulus, the optimized mix ratio result derived from the model shows that the absolute values of the relative errors are less than 5%. The small relative errors indicate that the proposed model can provide support for the multi-objective optimization of foam concrete with different fiber characteristics in the project application.

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    Optimal Control of Hydrogen Production by Renewable Energy Source Considering State of Charge of Energy Storage
    Zhi-liang WANG, Liang-liang GUO, Xin-yu LI, Xin-rui LIU
    Journal of Northeastern University(Natural Science)    2025, 46 (7): 30-36.   DOI: 10.12068/j.issn.1005-3026.2025.20240184
    Abstract660)   HTML11)    PDF(pc) (1978KB)(253)       Save

    An optimal control strategy considering the state of charge (SoC) of energy storage is proposed for an isolated DC microgrid for hydrogen production system composed of renewable energy, electrolytic cell, and energy storage equipment. Firstly, the characteristics of hydrogen production efficiency of alkaline electrolyzers are analyzed, and an optimal control method for adaptive adjustment of hydrogen production efficiency with bus voltage change is proposed. By coordinating with the energy storage system, the hydrogen production efficiency is kept within a high range. When the SoC of energy storage violates the upper and lower limits, a communication-independent SoC active recovery control strategy is designed to ensure the safe operation of the energy storage system. Secondly, a power coordinated control strategy considering extreme conditions is designed to ensure the stable operation of the DC microgrid through flexible switching between various operating modes. Finally, the effectiveness of the proposed control strategy is verified by MATLAB/Simulink simulation platform.

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    Floatation Effect and Mechanism of Hydroxamic Acid Collectors for Ilmenite and Titanaugite
    Qian-wen LI, Qing-you MENG, Zhi-tao YUAN, Sai-nan QI
    Journal of Northeastern University(Natural Science)    2025, 46 (6): 138-146.   DOI: 10.12068/j.issn.1005-3026.2025.20240005
    Abstract654)   HTML7)    PDF(pc) (2487KB)(642)       Save

    The flotation separation behaviors of ilmenite and titanaugite with benzohydroxamic acid (BHA), salicylhydroxamic acid (SHA), octylhydroxamic acid (OHA) and butoxylate acid (BFXA) were studied by mineral flotation test. The interaction mechanisms of four hydroxamic acids with different structures on ilmenite and titanaugite surfaces were analyzed by these means of contact angle, Zeta potential, Fourier transform infrared spectra and density functional theory (DFT). The results indicate that the separation effect for ilmenite with four hydroxamic acids is listed in the order: BFXA>OHA>SHA>BHA. The adsorption of hydroxamic acids on the surface of ilmenite is stronger than that of titanaugite, and the hydrophobicity of ilmenite is significantly improved by adding hydroxamic acid. The two oxygen atoms within oxyoxime groups of hydroxamic acid collectors are their reaction sites. Compared with the other three hydroxamic acids, BFXA has the highest HOMO orbital energy and thus exhibits stronger adsorption performance and collection capability.

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