Station System Research Articles

Research on the Construction of Grassroots Emergency Command System Under the Background of Informatization

Along with our economic development and public safety demand intensify., the task of fire rescue team is further expanded, and the construction of fire rescue command system becomes more and more important. This paper takes the information construction of the fire rescue team as the center, and studies the construction of the grass-roots emergency command system, aiming to promote the construction of the fire rescue system and improve the emergency rescue level of the fire rescue team. This paper takes the emergency rescue command system of the special service station of X Fire rescue detachment as the research object, and studies the construction of the basic emergency command system through literature research method and interview investigation methods.

The station system measures the water level and amount of rain that can save the data and online through Web Server

This research paper, the aim is to create a water level and rain gauge station system that can record data and online through Web Server. To be monitor the water level of the river and the amount of water in the rainy season easily by looking through the website. The design of the water level measurement system and the amount of rain is measured with a hydrostatic sensor, Tipping Bucket. A small measuring station, easy to install, uses solar energy and has a Data Logger to record data in parallel with an online database. In the research process, the principle of correlation is used to find the relationship between two variables to find the error of the sensor, namely the water level sensor (Hydrostatic) and the rain sensor (Tipping Bucket). Through research, it was found that the measuring station, the database system can work correctly and with high stability, the water level measurement sensor is highly accurate, the error value is between 0.01 - 0.36 m, the accuracy rate is 97.38%. 98.97% compared to water level measurement stations in nearby areas. For the rain sensor, from the actual count compared to the count with the program 3 times, it is seen that there is 1 error with a deviation value of 0.3 mm. From the recorded data of 2 months (July and August) it was found that the water level is still low to moderate, away from the dangerous water level of 6.84 meters and the amount of rain is still quite high with the amount of rain. The days account for 64.51% and 54.83% of July and August respectively, but the amount of rain will be moderate to moderate. The speed used to send data from the measuring station to the web server via 3G Internet will be between 4 – 6 seconds.

Research on multi-energy coupled preheating system of city gate station based on Modelica simulation

Natural gas depressurization at city gate stations can cause hydrate formation, necessitating preheating before pressure regulation. To reduce the high energy consumption of traditional city gate station preheating processes, this study introduces four novel multi-energy coupled systems: photovoltaic-air source heat pump (PV-ASHP), solar thermal-combined heat and power-ASHP (ST-CHP-ASHP), CHP-ASHP, and ASHP system, with a typical city gate station in Shanghai taken as the research case. Dynamic energy simulation systems were developed in Dymola software to evaluate the proposed systems from energy, environmental, and economic perspectives, conducting a comparison with conventional boiler preheating systems, including sensitivity analysis of energy prices on system economics. Results indicate PV-ASHP has the lowest annual total cost (ATC) and shortest discounted payback period, while ATC values for ST-CHP-ASHP, CHP-ASHP, and ASHP increase by 6.4 %, 9.6 %, and 12.6 %, respectively. ST-CHP-ASHP and PV-ASHP demonstrate advantages in primary energy rate and carbon emissions, with ST-CHP-ASHP performing slightly better than PV-ASHP. Currently, PV-ASHP demonstrates the highest development potential. If photovoltaic feed-in tariffs remain constant, and natural gas prices decrease by over 12.5 % or CHP feed-in tariffs increase by over 38.4 %, ST-CHP-ASHP becomes more economically viable. This research provides valuable insights for retrofitting China's existing city gate station preheating systems.

Pilot Study on the Possibility of Improving Water Treatment Sludge Management in Almaty

This article presents the results of a pilot study on the treatment of sludge from a water treatment plant in the city of Almaty, Republic of Kazakhstan, to ensure further disposal. The main objective of the study was to compare the efficiency of sludge drying by natural and artificial methods. The qualitative characteristics of the leachate from the dewatering unit, the chemical composition of the dried sludge and the granulometric analysis of the dried sludge were also studied. The greatest reduction in moisture content was recorded for drying in natural conditions (2.1%), but this process required the longest drying time. The leachate obtained from sludge dewatering was characterized by significant contamination (e.g., turbidity—55.65 on average, color—67.7, total Fe—5.15 mg/L, total N—79.6 mg/L, COD—311 mg/L, BOD—336.15 mg/L), which indicates the need for its pretreatment before further management in the technological system of the treatment station. The content of chemical substances contained in the dry residue of the sludge was also determined, of which aluminum was 0.94–13.8 mg/kg, silicon was 50.24–146.3 mg/kg, potassium was 1.72–5.51. mg/kg, calcium was 71.8–79.1 mg/kg, iron was 2.0–7.54 mg/kg and nickel was 0.9–4.4 mg/kg. A particle size analysis of the dried sludge showed that the majority fractions were fine and very fine sand, with a total of 20.2%, and silt and clay, with a total of 78.3%. Such properties justify the rationality of considering the reuse of dried sludge as a raw material for making, for example, construction materials or soil remediation material.

Application of Single Neuron Adaptive PID Algorithm in Natural Gas Automatic Distribution Control System

Abstract Aiming at the most important control link of electric regulating valve in automatic distribution control system of natural gas station, this paper proposed to use single neuron adaptive Proportional-Integral-Derivative (SNA-PID) algorithm to make up for the problems of the traditional Proportional-Integral-Derivative (PID) algorithm, such as the difficulty of parameter setting and the inability to adapt to system interference. In order to compare the performance of the algorithm, the transfer function between the valve opening of the electric regulating valve on the branch of the gas transmission system of a natural gas station was taken as an example to simulate various practical application scenarios, and the control performance of the two algorithms was studied in detail. Finally, the practical engineering application and the performance verification of the algorithm were carried out in a natural gas distribution station in Jiangxi Province. The results showed that, compared with the PID algorithm, the SNA-PID algorithm can automatically adjust its own algorithm parameters, greatly reduce the number of valve operations during the adjustment process, and control the valve opening more smoothly, adjust the branch pressure more quickly and effectively, and also has a strong anti-interference ability. Therefore, SNA-PID algorithm is more suitable for the automatic distribution control system of natural gas station than the PID algorithm.

Application on ac station service system for gathering station based on roof photovoltaic system

Abstract With the increasingly severe global energy crisis and environmental pollution, it is imperative to develop and utilize clean and renewable energy. In order to achieve carbon peak and carbon neutrality as soon as possible, and reduce the proportion of fossil energy generation, decarbonization in the power industry is imperative. This article focuses on the design of rooftop photovoltaic systems at gathering stations. By installing solar photovoltaic panels on the roof of the gathering station building, a photovoltaic power generation system is constructed to convert solar energy radiated into electricity. The DC power generated by the solar photovoltaic power generation system is converted by an inverter and connected to the 380V bus of the station power system as a supplementary power supply for the station load, thereby increasing the proportion of green electricity consumption in the collection station and achieving the goal of carbon reduction

Passenger Emergency Evacuation in Subway Station Systems: A Bibliometric Analysis and Systematic Review

Passenger Emergency Evacuation in Subway Station Systems: A Bibliometric Analysis and Systematic Review

Enhancement of heat dissipation efficiency in the CSNS target through the innovative design of a serial cooling water channel

This paper presents a coupling of the Monte Carlo method with computational fluid dynamics (CFD) to analyze the flow channel design of an irradiated target through numerical simulations. A novel series flow channel configuration is proposed, which effectively facilitates the removal of heat generated by high-power irradiation from the target without necessitating an increase in the cooling water flow rate. The research assesses the performance of both parallel and serial cooling channels within the target, revealing that, when subjected to equivalent cooling water flow rates, the maximum temperature observed in the target employing the serial channel configuration is lower. This reduction in temperature is ascribed to the accelerated flow of cooling water within the serial channel, which subsequently elevates both the Reynolds number and the Nusselt number, leading to enhanced heat transfer efficiency. Furthermore, the maximum temperature is observed to occur further downstream, thereby circumventing areas of peak heat generation. This phenomenon arises because the cooling water traverses the target plates with the highest internal heat generation at a lower temperature when the flow channels are arranged in series, optimizing the cooling effect on these targets. However, it is crucial to note that the pressure loss associated with the serial structure is two orders of magnitude greater than that of the parallel structure, necessitating increased pump power and imposing stricter requirements on the target container and cooling water pipeline. These findings can serve as a reference for the design of the cooling channels in the target station system, particularly in light of the anticipated increase in beam power during the second phase of the China Spallation Neutron Source (CSNS Ⅱ).

Evaluate user satisfaction for urban design of railway station areas: An assessment framework using agent-based simulation

AimsRailway station areas can play a crucial role in promoting sustainable development if integrated with cities and be fluctuation-responsive through effective urban design. However, during the design stage, assessing the station areas' performance, of which user satisfaction is indicative, is challenging due to methodological limitations. Agent-based simulation (ABS) is promising as it can link spatial features with agents' behavior features. This research questions to what extent ABS can help assess the urban design of station areas. MethodsThis paper adopts the user pyramid as the theoretical framework, which outlines five types of user needs: safety, speed, ease, comfort, and experience. The paper selects indicators linking satisfaction and spatial features at the district and building levels. These indicators are measured in the simulation of the station system using digital tools, including MassMotion and Python scripts. The theory, indicators, and tools, in combination, serve as an assessment framework. Rotterdam Central Station is used as a case to demonstrate how the framework works. ResultsThe framework is capable of assessing design alternatives by identifying changes in user satisfaction. It can be applied on the district level (at a scale of 250 m) with substantial details to inform design decision-making, and it is useful during the design stage when only limited data is available. This paper strengthens the scientific knowledge of railway station areas through the multidisciplinary literature review that translates user needs for urban design use, and it advances the digital means to visualize user satisfaction affected by design.

Sensor fusion of robotic total station and inertial navigation system for 6DoF tracking applications

This paper presents a novel approach for sensor fusion of robotic total station (RTS) and inertial navigation system (INS) to enable 6-degree-of-freedom (6DoF) pose estimation. Tight coupling of a spherical measurement model for RTS is developed, providing advantages over the traditional cartesian 3D-position measurement model, including supporting INS solution when distance measurements are unavailable and performing outlier detection in spherical observation space. Simulation studies demonstrate that replacing Global Navigation Satellite Systems (GNSS) with RTS for fusion with INS is beneficial in any environment (given line-of-sight (LOS) availability), even under ideal GNSS conditions. Furthermore, investigations on measurement models and failure identification over the entire range of RTS measurements reveal that the spherical model is advantageous over the cartesian model in certain regions. The developed methods are validated in a practical application for tilt compensation of an RTS pole, indicating a base 2D-RMSE of 3.8 mm for almost static and almost vertical poles, increasing with tilt and velocity.

High-Precision Bi-Directional Beam-Pointing Measurement Method Based on Space Solar Power Station System.

In the process of wireless energy transmission from a Space Solar Power Station (SSPS) to a satellite, the efficiency of energy transmission is closely related to the accuracy of beam control. The existing methods commonly ignore the impact of array position, structural deviation of the transmitting antenna, and modulation errors, which leads to the deviation error in actual energy transmission beams and the reduction of energy transmission efficiency. This paper innovatively proposes a high-precision bi-directional beam-pointing measurement method, which provides a technical basis for advancing the beam-pointing control accuracy from the perspective of improving the beam-pointing measurement accuracy. The method consists of (1) the interferometer goniometry method to realize high-precision guiding beam pointing measurement; and (2) the power field reconstruction method to realize offset angle measurement of the energy-transmitting beam. Simulation results demonstrate that under dynamic conditions, the guiding beam-pointing measurement accuracy of this method reaches 0.05°, which is better than the traditional 0.1° measurement accuracy based on the guiding beam. The measurement accuracy of the offset distance of the energy center is better than 0.11 m, and the measurement accuracy of the offset angle is better than 0.012°.

A risk assessment of a gas pressure reduction station system with confidence for dealing with imprecisions and unknowns

Process systems are sensitive and vital industrial facilities. Disturbances in their performance may cause harm to the environment,humans,or significant economic damage. In risk assessment of chemical process industries, the available data, information, and knowledge are typically rare, limited, and often unrealistic. This issue poses a challenge to conducting a credible quantitative risk assessment and effects the robustness of the results. To address these challenges, this work proposes a methodology based on the Dempster-Shafer theory of evidence as the reasoning framework. It incorporates risk identification, analysis, and mitigation phases to ensure a thorough analysis of risks and the integration of proactive risk reduction strategies. The approach aims to model the worst-case hazard scenario and assess associated risks using various methods such as FMECA, Bow-Tie, Credal Network, and Dempster-Shafer theory. The proposed approach models imprecision and data ambiguity using intervals and associated belief mass. This extension provides a basis for addressing the fundamental problem of prior ignorance about the distribution of the observed data, which is prevalent in data mining applications. A new approach is proposed that utilizes Belief and Plausibility curves, similar to a Cumulative Distribution Function, to propagate uncertainty, enhance criticality discrimination, and determine cumulated belief measures. This approach is applied in analyzing the failure modes identified in FMECA and is further extended through the credal network for comprehensive risk assessment. Results show how to express irrelevant and independent judgments, and how to work out with inferences in credal networks. This issue is often overlooked, but if properly addressed it represents the key to ultimately drawing reliable conclusions and fully utilizing the system's available data. A case study of the City Gate Station system was used to verify the application potential of the proposed approach.

Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a Bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.

Enhanced luminescence efficiency in GaN-based blue laser diodes by H plasma technology

To the best of our knowledge, this paper is the first to report the application of H plasma treatment technology to the treatment of laser diode ridge. Through the H plasma passivation on the ridge of the laser diode, a neutral complexes layer (i.e., Mg-H) is formed on the ridge, which effectively reduces ridge leakage current, thus reducing the threshold current of the laser diode and significantly improving the slope efficiency. The ridge were treated with H plasma using the Oxford Plasmalab System 100 ICP 180. The lasers' leakage current, optical power, emission wavelength, and other parameters were measured using a Cascade150 + B1505A probe station system, along with matched optical power meters and fiber optic spectrometers. Specifically, this study successfully fabricates a GaN-based blue laser diode characterized by a threshold current as low as 0.42 A and a slope efficiency as high as 1.96 W/A. Compared with the traditional silicon oxide-mediated ridge treated laser, the threshold current of the laser passivated by H plasma is reduced by 0.13 A, and the slope efficiency is increased by 0.56 W/A. This research not only enhances the performance of laser diodes but also has the potential to expand their application in multiple fields.

Simulation optimization of the key design parameters of the deodorization system in municipal solid waste transfer station

Simulation optimization of the key design parameters of the deodorization system in municipal solid waste transfer station

Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.

Predicting and Forecasting of Vehicle Charging Station Using ECNN with DHFO Algorithm

The forecast of the optimal placement of a charging station (CS) according to the real-time consumption of electric vehicles is a subject of urgency in this new era. The demand of a charging station in an area based on the trend of consumption can be predicted by means of interpolation and the extrapolation of historical data using a linear function of prediction model. The prediction of the charging station system was performed with distance relevancy methods. An adaptive optimal learning model was proposed to enhance the prediction performance for charging station management and to represent the pattern of vehicles’ travelling directions. The proposed model uses Distributional Homogeneity Feature Optimization (DHFO) using artificial intelligence (AI) to categorize and forecast the charging station from the database. The prediction performance of this model is improved more than the conventional classification model by filtering the apt features from all the electric vehicular and charging station attributes in the database. The Enhanced Cladistic Neural Network (ECNN) is used to improve the pattern learning model and increase learning accuracy. By comparing statistical parameters with other state-of-the-art methodologies, the suggested model’s overall findings were verified.

Data Source Importance Evaluation for Highway Networks: A Complex Network-Based Approach

Data collection technologies or data sources are critical for highway network management. However, due to the limitations on available management resources, determining the importance of these data sources is necessary to allocate these resources reasonably. This study proposes a complex network based method for evaluating the importance of multiple data sources in highway networks. This method includes mainly three steps. First, the business-data source relation will be identified and formulated for the highway network. Second, a business data source complex network is built from the previously identified business-data relationship. Third, an entropy weight method is used to compute and rank the importance of data source nodes by combining three indexes of degree centrality (DC), closeness centrality (CC) and structural holes (SC) computed based on the complex network. The proposed method is applied and illustrated using the highway network of Xuzhou City, Jiangsu Province, China. The results show that among the data sources, the most important data source is the continuous traffic survey station, followed by an automatic gantry-based station and vehicle detectors-based system. Discussions on the limitations, applications and future studies are provided for the proposed approach.

Hybrid edge–cloud energy management system for an industrial-scale green hydrogen refilling station: Lessons learned and findings

Green hydrogen is a potential contributor to a more sustainable transportation system. In this paper, we present our experiences and insights gained from an industrial-scale green hydrogen refilling station, marked by the innovative hybrid Edge–Cloud electrolyser system (EMS) employed in this context. Hybrid EMS can improve system performance, adapt to dynamic conditions, and continuously improve efficiency, even under unpredictable load and low storage situations. It also allows the centralized monitoring of future hydrogen production sites to facilitate the operation and maintenance and, in the long-term vision, to enable the joint coordination of the plants flexibilities in the service market as a virtual plant, thus increasing the cost efficiency. Through a real-time digital-model based assessment of the EMS, this study offers valuable lessons learned and findings, contributing to a deeper understanding of EMS in green hydrogen infrastructure.

Effect of connection modes on seismic responses of a diaphragm wall-subway station system subjected to mainshock-aftershock sequences

Most existing seismic behavior analyses of underground structures simply consider a single earthquake. Meanwhile, the diaphragm wall, as an enclosure structure, is regarded as a security reserve and is always ignored in current studies. Herein, the characteristics of a diaphragm wall-subway station system with different connection modes under earthquake sequences were investigated using numerical simulation. The damage degree of the structural component was calculated through quantitative analysis of the tensile damage picture. The seismic damage level of the station structure was evaluated to characterize the damage transition effect induced by the aftershock according to the inter-story drift angle. Moreover, an empirical model for predicting the inter-story drift angle with respect to different peak accelerations was proposed. The research results indicate that the effect of the connection mode between the sidewall and the diaphragm wall on the damage evolution and deformation behavior of the station structure is significant. Compared with that of the compound wall structure, the seismic damage to the sidewall of the composite wall structure is much less severe, but the slabs become more vulnerable and suffer more severe damage. The accumulative damage triggered by aftershocks aggravates the extent of structural damage and even leads to damage transition. The conclusions illustrated in this paper contribute to a better understanding of the seismic resistance design of diaphragm wall-subway station systems under earthquake sequences.