Actual Unit Research Articles

A Comprehensive Parcel-Level Dataset on Farmland Assessment: Addressing Grid-Cell Data Bias Estimation

Grid-cell data are increasingly used in research due to the growing availability and accessibility of remote sensing products. However, grid-cell data often fails to represent the actual decision-making unit, leading to biased estimates in socio-economic analysis. To this end, this paper presents a comprehensive parcel-level dataset for Salt Lake County, Utah, spanning from 2008 to 2018. This dataset combines detailed spatial and temporal data on land ownership, land use, and preferential farmland tax assessments under the Greenbelt program. Compiled from multiple geospatial sources, the dataset includes nearly 200,000 parcel-year observations, providing valuable insights into landowner decision-making and the impact of tax abatement incentives at the decision-making level. This resource is beneficial for researchers, educators, and practitioners in sustainable development, environmental studies, and farmland conservation.

Modeling of Spiral Wound Membranes for Gas Separations—Part IV: Real-Time Monitoring Based on Detailed Phenomenological Model

The present study presents, for the first time, the real-time monitoring of an actual spiral-wound membrane unit used for CO2 removal from natural gas in an actual industrial offshore platform, utilizing a detailed phenomenological model. An Object-Oriented Programming (OOP) paradigm was employed to simulate the offshore membrane separation unit, accounting for the diverse levels of the membrane separation setup. A parameter estimation procedure was implemented to fit the phenomenological model to the real industrial data in real-time, for the first time. In addition, estimated permeance parameters and calculated unmeasured variables (soft sensor) were used for monitoring Key Performance Indicators (KPIs), such as membrane selectivity, dew point temperature, and hydrocarbon loss. Finally, a reparametrization of the parameters was implemented to improve the robustness of the optimization procedure. Thus, the model variables presented good adjustments to the data, indicating the satisfactory performance of the estimation. Consequently, the good accuracy of the model provided reliable information to the soft sensors and KPIs.

Determination of Turbulent Prandtl Number for Thermal Fluid Dynamics Simulation of HVAC Unit by Data Assimilation

Abstract Regarding high-precision temperature field prediction of a heating ventilation and air conditioning (HVAC) unit using thermal fluid dynamics simulation, the determination of the turbulent Prandtl number (Prt) was a key issue. In this study, we present an attempt to improve the accuracy of thermal fluid dynamics simulations in HVAC units by adjusting the Prt using data assimilation techniques. First, we simultaneously measured the velocity and temperature in the hot and cold air mixing zone of a simple HVAC model using particle image velocimetry and thermocouples. Second, we coupled data assimilation to the thermal fluid simulation model to determine the Prt in the mixing field. Finally, we proposed two functions of the Prt for high velocity and low velocity regions using multidimensional analysis. In the future, we believe that the Prt functions can be applied to thermal fluid simulations of actual HVAC unit to accurately predict performance without conducting prototype experiments, thereby contributing to reducing development cost and time of HVAC unit.

Can the Federal Budget Process Promote Fiscal Sustainability? Evidence from the Budget Enforcement Act of 1990

The Budgetary Enforcement Act of 1990 (BEA90), in effect from 1992 to 2002, is frequently held up as an example of a congressional control that constrained U.S. federal spending. This paper provides a formal test of this conventional wisdom using synthetic control on federal non-defense discretionary expenditures, which is the category of spending BEA90 was considered the most binding. A BEA90-retaining Synthetic United States generated from a lasso regression on a large panel of donors high-income national economic and government finance indicators ultimately reach 2006 at about the same level as the actual United States despite temporarily showing some more restraint immediately upon expiration. We conclude that BEA90 effect on federal non-defense discretionary spending was small, short-lived, statistically likely to have emerged from chance, and more generally did not bend the curve of federal expenditure growth.

Hydrothermal synthesis of tetragonal FeS: Dome-shaped superconductivity vs Fe:S actual molar ratios

We successfully synthesized tetragonal FeS with various Fe:S molar ratios using a hydrothermal method, and the EDS and XRF results confirmed that FeS exhibits superconductivity within the Fe and S element ratio range of 1.155–1.274, rather than a strict stoichiometric ratio. The reaction temperature can not only play a crucial role in determining the purity of the obtained FeS phase but also significantly alter its micromorphology. Significantly, by analyzing the relationship between the Fe:S actual molar ratio and Tc as well as the Fe:S actual molar ratio and unit cell volume, FeS exhibits dome-shaped superconductivity and shows a negative correlation with Tc and chemical pressure. Furthermore, considering the correlation between anion height (hanion) and Tc, Tc reaches the maximum value of 4.53 K for hanion ≈ 1.28 Å, a behavior distinct from that of observed in Fe-pnictide superconductors. In short, our experimental results provide a unique perspective to deepen our understanding of FeS superconductors.

Cyber-physical recreation of six DOF industrial robot arm

AbstractThe Cyber-Physical and Vehicle Manufacturing Laboratory, a model Industry 4.0 laboratory, is applying new innovative solutions to improve the quality of education. As part of this, a digital twin of the lab was designed and built, where users can practice. In the virtual space, it is possible to apply the known robot motion types, and the tool centre and wrist speed have been measured virtually. Robot control tasks can be performed “offline” using parameters. This information can then be transferred to the actual physical robot unit. The stable diffusion 1.5 deep learning model generates 2D geometric shapes for trajectory, allowing users to perform unique tasks during education. The Google Colab cloud-based service was used to teach our rendered-type dataset. For the 3D simulation frame, we used V-REP, which was developed on a desktop PC equipped with an Intel Core i5 7600K processor, Nvidia GTX1070 VGA with 8 GB of DDR5 VRAM, and 64 GB of DDR4 memory modules. The following material describes an existing industrial six-axis robot arm and its implementation, which can be controlled and programmed while performing virtual measurements after integrating into a Cyber-Physical system and using deep learning techniques.

Data-model Interactive Health Condition Assessment for Hydropower Unit

Abstract The health condition assessment (HCA) of hydropower unit plays an important role in enhancing safe operation of hydropower stations and reducing maintenance costs. Due to the harsh environment, there are some problems of sensor data during the unit operations, including abnormal data, missing data and high-noise data. Also, it is difficult to obtain complete monitoring data during run-to-failure maintenance. These issues definitely cause the low-confidence HCA. Thus, a data-model-interactive HCA for hydropower unit was proposed. First, a high-fidelity 3D mechanism simulation model, interacting with the actual unit, was built using unit drawing data provided by manufacturers. Average values of the volute inlet pressure and draft tube outlet pressure were fed into the dynamics simulation model to obtain the simulated pressure pulsation data under working conditions (water head H and power P). Then, a long short-term memory based healthy condition model was constructed using the power parameters, simulation and normal pressure pulsation. After model construction, difference values between simulated pressure pulsation dataset and degradation state pressure pulsation dataset were calculated to build the performance degradation index (PDI), describing the HCA of units. Finally, the PDI was fed into the convolutional neural networks and long short-term memory model to achieve degradation trend prediction. Validation experiment was conducted to verify the effectiveness of proposed method using actual monitoring data and operating parameters of 6# unit in the hydropower station.

Vibration of Variable Speed Pump-Turbine in Turbine Operating Range: Simulation by CFD+FEM

Abstract Variable speed pumped-storage unit can operate efficiently and stably in wide range of power and head through the coordinated control of the rotational speed and guide vanes. The regulation in rotational speed brings about frequency and amplitude changes of pressure pulsations, which further influence the avoidance margin of vibration and the dynamic response of the runner. The vibration characteristics of the variable-speed pump-turbine need to be studied when it runs in the wider operating region. Selecting an actual pumped-storage unit as the subject and using the CFD+FEM coupled simulation method as a tool, we conducted three-dimensional pump-turbine vibration simulations and analyse the flow patterns, pressure pulsations, and runner vibration characteristics of several typical operating points in turbine mode. The study focused on the variation laws of pressure pulsations and dynamic stresses when rotational speed is regulated. Comparison with the results for the constant speed unit under the corresponding working conditions was conducted. It is shown that the variable speed operation can effectively avoid the severe vibration conditions that the constant speed unit encounters; the amplitudes of pressure pulsations and dynamic stresses of the runner are small at low rotational speed; in the condition of high speed, low head, and small load, the vibration is the most obvious. This study can provide a reference for the design and operation of variable speed pump-turbine.

An Automated Method for Pipe Routing in Ship Unit Modules

This study addresses challenges in ship arrangement design by proposing an automated pipe routing method in ship unit modules. Currently, designers rely on experience, lacking quantitative assessments and causing difficulties for non-experts. The proposed method incorporates expert knowledge and design rules into an expert system, evaluating expertise and adherence to rules. The system’s evaluation result was used as the objective function of an optimization problem formulated for pipe routing in conjunction with metrics such as total pipe length, the number of bends, and space availability. Through validation by comparing actual unit module designs, it is demonstrated that the proposed method suggested improved pipe routing design while adhering to expert knowledge.

2D And 3D Physical Model Testing For The Rehabilitation On The Frioul Port Breakwater (France)

A white archipelago anchored in the Mediterranean Sea 2 km off the coast of Marseille (France), the port of Frioul is made up of the Pomègues islands (to the south) and Ratonneau (to the north). It is protected on the west side by the Berry breakwater (renovated in 1984), and on the east side by the Condorcet breakwater (figure 1.a). The port was built in the early 1820s (Berry breakwater) to take care of the quarantine of ships coming from areas infected by yellow fever, then developed in the 1850s (Condorcet breakwater in the East) to make it a military port. The current findings highlight that the eastern breakwater is seriously damaged and must be rehabilitated (figure 1.b). Accordingly, the rehabilitation solution, which consists to replace the actual rock armour unit, was physically modelled, and tested for its hydraulic stability and the overtopping performance as well as the forces and pressures acting on the crown wall. The process includes recreation of breakwater cross sections in a 2D wave flume at a scale of 1:35 (figure 2.a), and a optimized breakwater configuration proposed in a 3D wave basin at a scale of 1:50 (figure 2.b). These two campaigns made it possible to compare and optimize the design, first with the state of the art (Van Gent, M., et van der Werf, I., 2019) (Mares-Nasarre and van Gent, M., 2020), then with the observations and measurements collected from the modeling.

A robust state estimation by optimal placement of measurement units considering loads/renewable generations and measurements uncertainty

AbstractThe supervisory control and data acquisition (SCADA) system is one of the key requirements for monitoring the power systems state. The power system state is estimated from the state estimation (SE) function, which uses the measurements results from different points. Inaccurate estimation may cause inefficient management of power systems and making decisions. Usually, the number of measurement units (MUs) is limited so the optimal location of MUs is very important in successful and accurate state estimation. The optimal placement of MUs gets more complicated when the actual state of the power system is uncertain due to the high penetration of renewable generation and load uncertainties. Considering the power system's actual state uncertainty and measurement units’ inherent error, this work presents a probabilistic model for the uncertainty of measuring based on the K‐medoids data clustering technique. Then the probabilistic optimal placement of MUs is solved by a binary particle swarm optimization (PSO) method. Considering the mentioned uncertainties makes the obtained solution robust against all the probable scenarios. The proposed technique is implemented on the IEEE 14‐bus test system and the results are discussed. Results show the effectiveness of the proposed method in improving the accuracy of power system state estimation.

Analisa dan Perancangan Sistem Informasi Service Control Board System pada PT. XYZ berbasis Web

The service control board is a tool used to monitor workshop conditions according to actual conditions. there is a difference in the status of the service unit on the service control board manual with the actual service unit status in the workshop, the distance between the foreman's office and the service advisor's office is quite far so the foreman has to come to the service registration office every time to update the service unit's status on the service control board manual, the customer has difficulty in read the service control board manual because it uses certain symbols. The service control board system information system is a solution to solve existing problems. With the creation of a service control board system, the processes and procedures for updating service unit status will be more effective and efficient and the information displayed will be more accurate and real-time.

A hybrid deep learning modeling based on lumped parameter model of coal-fired circulating fluidized beds for real-time prediction

The integration of large-scale renewable energy sources into the electrical grid has presented challenges that dynamic characteristics and mechanisms of thermal power plants are incapable of accomplishing, particularly circulating fluidized beds. Traditional mechanism models make it difficult to obtain precise parameter monitoring because of the complicated mechanism process. Accurate parameter monitoring in the circulating fluidized bed unit, assisted by the advent of artificial intelligence, significantly increases the unit's operating stability. The combination of artificial intelligence technology and mechanism model research provides a new solution to improve the accuracy of parameter monitoring. The bed temperature parameter of circulating fluidized bed units is taken as one of the most predominant characteristics of CFB operational stability. By analyzing and simplifying the bed temperature lumped mechanism model, this research identified the expression form of the feedforward neural network model and the calculation form of the convolutional neural network model and proposed a deep learning model based on the bed temperature lumped parameter model. The superior effectiveness and precision of the model are verified by the operation data of two actual units in this paper. The prediction model in this chapter achieved mean absolute error (MAE) and mean absolute percentage error (MAPE) values of 2.2579 °C, 1.5453 °C, 0.2417%, and 0.1736%, respectively. Finally, the model has also been successfully applied in the prediction task of in-situ sulfur dioxide of circulating fluidized bed units.

Transformational leadership for deeper learning: shaping innovative school practices for enhanced learning

PurposeThis research aims to explore leadership approaches that foster deeper learning and facilitate the transition from traditional schooling to a model aligned with the demands of the post-industrial digital knowledge society.Design/methodology/approachEmploying a mixed-methods approach, the authors conducted surveys among school principals within a network of schools embracing deeper learning based on ten distinct but interlocking criteria that define this particular model of deeper learning. Through in-depth follow-up interviews with school leaders, the authors investigated the factors and obstacles that support sustainable implementation and scalability of deeper learning, with a specific focus on the role of transformational leadership.FindingsDuring the implementation of transformative practices like deeper learning, school leaders demonstrate diverse perspectives on the necessary changes for their successful integration. Leaders inclined toward a “transactional” leadership style concentrate on changes within individual classrooms. Conversely, leaders exemplifying “transformational leadership” possess a broader vision and address systemic factors such as teacher collaboration, assessment regulations and the effective utilization of time and space within schools. To achieve widespread adoption of deeper learning across schools and the education system, it is essential to recruit more transformational leaders for formal leadership positions and reorient leadership training toward transformational approaches.Practical implicationsThe deeper learning model developed for this intervention encompasses a four-stage process: Teachers initially collaborate in small teams to co-design interdisciplinary, deeper learning units. The actual units consist of three sequences: knowledge acquisition, where students gain knowledge through direct instruction supplemented by personalized learning on digital platforms; team-based co-creative and co-constructive tasks facilitated by teachers once students have acquired a solid knowledge base and the completion of authentic tasks, products or performances in sequence III. While small groups of intrinsically motivated teachers have successfully implemented the model, achieving broader scalability and dissemination across schools requires significant “transformational leadership” to challenge traditional norms regarding teacher collaboration, assessment practices and the efficient use of time and space in schools.Originality/valueThis paper presents a structured model of deeper learning based on ten distinct but interlocking quality criteria tested within a network of 26 schools. The model has demonstrated transformative effects on participating schools, albeit primarily observed in smaller substructures of large secondary schools. Teachers who previously worked independently have begun to collaboratively design learning experiences, resulting in “hybrid” classrooms where physical and digital spaces merge and extend to include maker spaces and out-of-school learning environments. Traditional summative assessments have been replaced by various forms of embedded formative assessment. However, these innovations are currently driven by small groups of intrinsically motivated teachers. The research provides insights into the type of school leadership necessary for comprehensive scaling and system-wide dissemination of deeper learning.

Computationally efficient approach for risk-informed decision making

Probabilistic risk assessment (PRA) is used as an essential tool for risk-informed decision-making in the nuclear industry. The fault and event trees play a crucial role in PRA to estimate the probability of system failure based on the failure probabilities of components. The fault trees or event trees for an actual power plant unit can be fairly large in size with several different types of logic gates, interconnected events, dependent events, etc. A large fault tree can include hundreds of gates, basic events (BEs), multiple occurring events (MOEs), and dependent events. Complex connectivities can give rise to excessive computational demand and storage requirements for the analysis. Fault and event trees can be solved using the minimal cut-set approaches, or advanced quantification techniques such as Binary decision diagrams or Bayesian networks. However, these techniques can be computationally inefficient for larger fault trees and can run out of memory/storage space. This study focuses on developing and proposing a new approach for accurate estimation of the system-level risk while improving the computational efficiency significantly. More specifically, an attempt is made to reduce the complexity of the analysis of MOEs and dependent events in fault trees. The proposed algorithms in this study present a significant improvement over traditional approaches which makes it highly promising for additional development. The computational efficiency of the proposed approach over the traditional approach is illustrated for fault trees with a varying number of events and different types of logic gate connections.

Dynamic simulation‐based quantitative hazard and operability process hazard analysis for a hydrocracking unit

AbstractA hydrocracking unit operates at high temperature, high pressure, and near hydrogen, which can be susceptible to fire and explosions accidents due to equipment failure. Therefore, it is essential to identify and analyze the risk factors. The dynamic simulation‐based quantitative hazard and operability (HAZOP) study can effectively and purposefully quantify deviations and consequences as well as reduce the redundancy of safety analysis results. Furthermore, the time parameter is introduced through dynamic simulation, which reflects more realistically the dynamic characteristics of the system in the event of a fault. In this paper, we take the process flow of the absorbing‐stabilizing system of an actual hydrocracking unit in a refining company as an example and carry out steady‐state and dynamic simulations for the deviations of pressure, temperature, and liquid level with the help of Aspen Plus V11 and Aspen Plus Dynamics V11 software to complete the dynamic simulation‐based quantitative HAZOP analysis.

Design of a progressive fault diagnosis system for hydropower units considering unknown faults

To address the misidentification problem of signals containing unknown faults for hydropower units, a progressive fault diagnosis system is designed. Firstly, in view of the non-stationary and nonlinear vibration signals of hydropower units, the method of complementary ensemble empirical mode decomposition is used to process the normal and fault vibration signal samples, and the intrinsic mode function (IMF) and residual components with different frequencies are obtained. Then the IMF energy moment is calculated and used as the feature vector. Furthermore, a classifier (IMF-K1) is constructed based on the feature vector samples of the normal vibration signals of hydropower units, fault symptom indicators, and K-means algorithm to determine whether the hydropower unit is faulty; a classifier (IMF-K2) is constructed based on the feature vector samples of the fault vibration signals of hydropower units, fault symptom indicators, and K-means algorithm to determine whether the hydropower unit has the known fault; a classifier (IMF-bidirectional long short-term memory neural network (BiLSTMNN)) is constructed to distinguish the fault type of hydropower units by combining the eigenvector samples of known fault vibration signals, fault symptom indicators, and BiLSTMNN. Finally, a progressive fault diagnosis system for hydropower units is constructed using IMF-K1, IMF-K2, and IMF-BiLSTMNN, and comparative experiments are designed using the sample data from the rotor test bench and actual hydropower unit. The results show that the designed progressive fault diagnosis system has greater effectiveness in mining signal features and high fault diagnosis accuracy.

Optimization and Performance Improvement of Automatic Generation Control (AGC) for 330 MW Subcritical Thermal Power Unit

With the continuous assessment of the “Two Detailed Rules”, various power generation groups are paying increasing attention to AGC. A 350 MW subcritical unit has been in a poor performance state under AGC-R mode since the 2022 heating season, with a K1 value below 1.2 and a K2 value not reaching the qualified value. It is facing the cancellation of the qualification for AGC-R mode. In the face of the problem with the unit, this article analyzes three reasons. Firstly, the speed setting exceeds the requirements, but the actual unit action does not meet the standards. There is a deviation in power and a significant deterioration in control quality during the turning point and continuous climbing of AGC instructions. Based on the above analysis, this article proposes three renovation plans to optimize the instructions through nonlinear correction functions and achieve the elimination of instruction deviations. It calculates and judges the difference between the corrected AGC load command value and the actual power value of the unit. It calculates the deviation and sends the deviation to a saturated nonlinear link to generate the feedforward of the main turbine control, improving the output of the main turbine control. By setting a variable integral function, the main control output of the boiler is improved. After this technical transformation, the AGC performance of the unit has been significantly improved.

Association of CPR simulation program characteristics with simulated and actual performance during paediatric in-hospital cardiac arrest

AimTo evaluate associations between characteristics of simulated point-of-care cardiopulmonary resuscitation (CPR) training with simulated and actual intensive care unit (ICU) CPR performance, and with outcomes of children after in-hospital cardiac arrest. MethodsThis is a pre-specified secondary analysis of the ICU-RESUScitation Project; a prospective, multicentre cluster randomized interventional trial conducted in 18 ICUs from October 2016–March 2021. Point-of-care bedside simulations with real-time feedback to allow multidisciplinary ICU staff to practice CPR on a portable manikin were performed and quality metrics (rate, depth, release velocity, chest compression fraction) were recorded. Actual CPR performance was recorded for children 37 weeks post-conceptual age to 18 years who received chest compressions of any duration, and included intra-arrest haemodynamics and CPR mechanics. Outcomes included survival to hospital discharge with favourable neurologic status. ResultsOverall, 18,912 point-of-care simulations were included. Simulation characteristics associated with both simulation and actual performance included site, participant discipline, and timing of simulation training. Simulation characteristics were not associated with survival with favourable neurologic outcome. However, participants in the top 3 sites for improvement in survival with favourable neurologic outcome were more likely to have participated in a simulation in the past month, on a weekday day, to be nurses, and to achieve targeted depth of compression and chest compression fraction goals during simulations than the bottom 3 sites. ConclusionsPoint-of-care simulation characteristics were associated with both simulated and actual CPR performance. More recent simulation, increased nursing participation, and simulation training during daytime hours may improve CPR performance.

A robust method for transmission line sequence parameter estimation using synchronized phasor measurements

We present a positive and zero sequence line parameter estimation method, that is robust to systematic errors in the instrument transformers, especially when they are within the specified tolerance as per standards. Using Monte Carlo simulations, it is shown that the proposed approach is robust and accurate for all operating conditions, specifically for short length and lightly loaded transmission lines. We also validate the proposed approach on 400 kV and 765 kV transmission lines using actual field phasor measurement unit (PMU) data. Further, estimation of zero sequence line parameter values is somewhat tricky because not enough unbalance exists during the normal operation of the power grid. Therefore, we quantify the minimum percentage unbalance needed in currents to determine zero sequence line parameter values within 1% tolerance. We also present the line length and line loading effects in estimating zero sequence line parameter values using simulations. Simulation and field PMU data results on 765 kV and 400 kV lines of different lengths and loading levels show that the proposed method estimates accurate positive and zero sequence line parameter values.