Configuration Model Research Articles

Peatland aquifer zone modeling via Wenner and Schlumberger configuration geoelectric strategies in Tarai Bangun Village, Riau Province, Indonesia

The quality of freshwater in peatland areas poses significant concerns for both governmental bodies and local communities. Challenges arise during well drilling activities, where individuals often encounter difficulties in accessing fresh water, either due to its absence or contamination with peat-infused water. Tarai Bangun Village, situated in the Kampar Regency of Riau, represents a critical peatland region warranting thorough investigation, particularly along street of Sarana Utama. Despite lacking social amenities such as markets, the area is equipped with essential facilities like schools and places of worship. This study aims to assess the efficacy of Wenner and Schlumberger configuration modeling in horizontal and vertical soil mapping, thereby facilitating a comprehensive understanding of groundwater distribution and aquifer zoning within the peatland areas under examination. Analysis conducted utilizing Res2DInv software for horizontal modeling revealed significant findings. In track 1, groundwater layers were identified at depths of 6.50 meters and 19 meters, comprising gravel, sandstone, and limestone. Track 2 exhibited an aquifer layer spanning depths from 2.50 meters to 24.9 meters, consisting of alluvium, gravel, and limestone. Furthermore, interpretation of 1D geoelectric vertical model cross-sections using Progress software unveiled additional insights. Path 1 delineated depths of 0.12 meters, 19.30 meters, and beyond 41.28 meters, featuring a lithological composition of sandstone, limestone, and dry gravel. Path 2 showcased depths of 0.14 meters, 9.43 meters, and exceeding 12.02 meters, characterized by dry sand and gravel formations.

Focusing on the individual in cross‐sectoral collaboration: A configurational approach

AbstractResearch focuses on various macro and meso aspects of collaboration and less on the individuals who make decisions about their organizations' collaborations. Organizational leaders make these decisions based on their interpretations, influenced by their personal characteristics. Existing studies examining organizational outcomes such as a decision to collaborate typically consider these characteristics separately and independently, ignoring the reality that a leader's characteristics jointly and interactively shape perspectives, attitudes, and behaviors. We focus on five prominent characteristics in the literature—gender, educational attainment, prior cross‐sector experience, current cross‐sector affiliation, and tenure in position—and study their configurational dynamics regarding the organization‐level outcome—the propensity of cross‐sector collaboration. We employ qualitative comparative analysis to develop and test a configurational model for cross‐sector collaboration, using survey data of local government and nonprofit leaders in Lebanon. The analysis offers exploratory insights into four configurational types of leaders whose organizations opt to collaborate at the local level.

Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System

The quadruple-tank system (QTS) is a popular educational resource in universities for studying multivariable control systems. It enables the analysis of the interaction between variables and the limitations imposed by multivariable non-minimum phase zeros, as well as the evaluation of new multivariable control methodologies. The works utilizing this system present a theoretical model that may be too idealistic and based on erroneous assumptions in real-world implementations, such as the linear behavior of the actuators. In other cases, an identified linear model is directly provided. This study outlines the practical grey-box modeling procedure conducted for the QTS at the University of Cordoba and provides guidance for its implementation. A configurable nonlinear model was developed and controlled in a closed loop using different controllers. Specifically, decentralized control, static decoupling control, and simplified decoupling control were compared. The simulation designs were experimentally validated with high accuracy, demonstrating that the conclusions reached with the developed model can be extrapolated to the real system. The comparison of these three control designs illustrates the advantages and disadvantages of decoupling in certain situations, especially in the presence of non-minimum phase zeros.

Toward cyber-physical systems for monitoring and analyzing energy consumption of machine tools

ABSTRACT This paper presents a digital twin and cyber-physical systems (CPS) environment for monitoring and analyzing the energy consumption of machine tools. Machine tools, which account for a large portion of the manufacturing industry, should be developed in response to environmental issues through carbon neutrality and digital transformation for the Fourth Industrial Revolution. The digital twin, which is at the core of CPS, must support the visualization and analysis of energy consumption issues from various perspectives, and at the same time, support multi-faceted analyses. In this paper, a digital twin that supports the monitoring and analysis of the energy consumption of machine tools and an established CPS environment are proposed. The digital twin consists of a static model containing structural information, a dynamic model expressing the behavior of the object, a visualization model containing a three-dimensional model, and a configuration model linking those three together. In addition, a CPS environment that supports this is implemented through a commercial internet-of-things platform. Lastly, the practicality of the proposed model and system are verified through case studies and a comparative analysis. This research is expected to prompt innovation in the development of cutting-edge machine tools that are more intelligent and closer to carbon neutrality.

Space Layout Simulation of Assembled Nanoarchitecture Based on Improved Particle Swarm Optimization

In order to solve the problem that the traditional building space configuration model cannot meet the optimization of building space characteristics, the author proposes the optimization of building space utilization based on spatialized particle swarm optimization. First, to solve the problem of optimal allocation of space units, the PSO algorithm is modified to encode the space units by means of character coding.Secondly, the maximum standardization method is used for data processing, and the factors affecting space utilization are summarized, the objective function of optimal allocation of architectural space is given from three aspects: economic benefits, social benefits and ecological benefits; Finally, by analyzing the advantages and disadvantages of master-slave parallel model and point-to-point parallel model, a chained parallel structure is proposed. The experimental results show that: The experimental data is based on the utilization of building space in these three regions in 2015, and the vector map is divided into 30 m × A grid of 30 m in size, and all statistical data and spatial data are projected on each grid cell. The difference between the fitness values of the final convergence of the three parallel models is small, and the main difference is the convergence speed. During the run time test, set the three parallel models to run under the conditions of 8, 16, 32 and 64 nodes respectively. Because of the combination of the advantages of master-slave model and point-to-point model, the running time of chained parallel model is significantly lower than that of the other two parallel models. Conclusion: Through data simulation test, it is verified that the chained parallel model has higher fitness, convergence speed and shorter running time, and its performance is better than the other two, indicating that the optimization algorithm proposed by the author has good performance.

How can feature usage be tracked across product variants? Implicit Feedback in Software Product Lines

Implicit feedback involves gathering information about software usage to grasp how and when the software is utilized. This study investigates the integration of implicit feedback mechanisms into Software Product Line Engineering (SPLE). While common in product-based development for identifying bugs, usability issues, and informing requirement prioritization, its adoption in SPLs has been limited due to the unique challenges posed by SPLE, such as the emphasis on Domain Engineering over Application Engineering, and the need for systematic reuse of shared assets. We propose a novel approach to incorporate feedback practices into Domain Engineering, thereby shifting the focus from individual product variants to the SPL platform, and specifically moving from product-based feedback to feature-based feedback. Based on a case study, we suggest that product derivation includes a second step that injects the trackers at the time of derivation, using a Feedback Model that complements the Configuration Model for feedback analysis.To test this approach, we introduce FEACKER, an extension to pure::variants as the variability manager. FEACKER injects trackers when the product variant is derived. The findings are validated through a Technology Acceptance Model (TAM) evaluation and a focus group discussion, providing insights into the feasibility, acceptance, and potential impact of platform-based feedback in SPLE. The results indicate agreement on the benefits of conducting feedback analysis at the platform level and the perception that FEACKER seamlessly extends the capabilities of pure::variants.

Generation of Scale-Free Assortative Networks via Newman Rewiring for Simulation of Diffusion Phenomena

By collecting and expanding several numerical recipes developed in previous work, we implement an object-oriented Python code, based on the networkX library, for the realization of the configuration model and Newman rewiring. The software can be applied to any kind of network and “target” correlations, but it is tested with focus on scale-free networks and assortative correlations. In order to generate the degree sequence we use the method of “random hubs”, which gives networks with minimal fluctuations. For the assortative rewiring we use the simple Vazquez-Weigt matrix as a test in the case of random networks; since it does not appear to be effective in the case of scale-free networks, we subsequently turn to another recipe which generates matrices with decreasing off-diagonal elements. The rewiring procedure is also important at the theoretical level, in order to test which types of statistically acceptable correlations can actually be realized in concrete networks. From the point of view of applications, its main use is in the construction of correlated networks for the solution of dynamical or diffusion processes through an analysis of the evolution of single nodes, i.e., beyond the Heterogeneous Mean Field approximation. As an example, we report on an application to the Bass diffusion model, with calculations of the time tmax of the diffusion peak. The same networks can additionally be exported in environments for agent-based simulations like NetLogo.

Photovoltaic power generation prediction and optimization configuration model based on GPR and improved PSO algorithm

As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.

Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm

The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system.

Environmental assessment and CO2emissions of Brayton cycle configurations based on exergo-sustainability, economic and ecological efficiency using multi-criteria optimization technique

The performance indicators of Brayton cycle configurations (BCY) for the topping cycle application are presented. The indicators include exergy efficiency, ecological efficiency, sustainability index, environmental impact, and economic parameters. The study's objective is to access the key indicators of sustainability and investment cost to provide valid information for the choice of GT configuration as topping cycles. Five BCY configurations (Model 1 to Model 5) were studied. The maximum exergy efficiency of 28 % was obtained across the studied models. In addition, the waste exergy ratios, environmental effect factors, and CO2 emissions were determined for each model. The CO2 emissions were found to vary from 102.8 to 168 kg/MWh. Model 1 and Model 5 had the highest payback periods of 2.3 and 3.6 years, respectively, with the least unit cost of energy. Similarly, the highest cost of investment was obtained with Model 5. Results from the TOPPIS analysis show that the closeness to the final positive ideal solution varied from 0.218 to 0. 56 across the BCYs. The best model close to ideality was model 5 and thus ranked first and based principally on economic, technical, and environmental sustainability. Furthermore, the optimization results show that there are prospects for system retrofitting.

Evaluation Model of Human Resource Allocation Balance for Healthy Environment in Organizations Based on Big Data Driven and Internet of Things

Abstract To improve the ability of balanced evaluation, dynamic allocation, level of output, and profitability of human resources, a balanced evaluation model of human resources allocation is proposed based on big data driven and the Internet of Things. An evaluation architecture model of human resource allocation balance in view of big data driven and the Internet of Things is established; a multithread big data driven configuration model and the Internet of Things are used to construct a table model of enterprise human resource optimal allocation in the form of reports; statistical regression analysis method is used to detect the risks and process parameters in the process of enterprise human resource optimal allocation; and employment elasticity theory analysis is taken to establish a resource factor analysis model of human resource balanced allocation under the constraint of economic growth mode. According to the changing trend of the scale and structure of labor resources, statistical regression analysis is adopted to make big data driven analysis in the process of balanced evaluation of human resources allocation, and balanced game control is adopted to analyze the relevant factors affecting human resources allocation to realize balanced scheduling of population flow and human resources allocation and automatic post adjustment. The empirical analysis results show that the system has a good balance in human resource allocation, the output efficiency among the various elements of human resource allocation is improved, and there is a high level of efficiency in human resource output.

Multi-objective particle swarm optimization algorithm based on multi-strategy improvement for hybrid energy storage optimization configuration

In order to fully leverage the advantages of hybrid energy storage systems in mitigating voltage fluctuations, reducing curtailment rates of wind and solar power, minimizing active power losses, and enhancing power quality within distributed generation systems, while effectively balancing the economic and security aspects of the system, this paper establishes a multi-objective hybrid energy storage optimization configuration model that comprehensively considers the lifecycle economic costs, node voltage deviations, and system active power losses. To address the limitations of single-objective solution algorithms and the lack of diversity and premature convergence in multi-objective optimization processes, a multi-objective particle swarm optimization by multi-strategy improvements (CMOPSO-MSI) is proposed to solve the model. By introducing adaptive grid crowding distance and roulette wheel selection strategies to dynamically update the Pareto solution set, population uniformity and diversity are ensured. An improved dynamic nonlinear weighting strategy is introduced to enhance the algorithm's global search capability and convergence performance. A Gaussian mutation strategy is incorporated into the iteration process to enhance the uniformity of non-dominated solutions and particle search capabilities The aim is to achieve an optimal balance between the configuration cost and stability of hybrid energy storage systems. A simulation analysis under multiple cases is performed, taking an IEEE 33-node power distribution system as an example, with introducing photovoltaic and wind energy sources. The results demonstrate that the proposed algorithm achieves better Pareto frontiers and convergence performance compared to conventional multi-objective particle swarm optimization algorithms MOPSO, and classical multi-objective optimization algorithm NSGA-II, validating the effectiveness and accuracy of the proposed model and algorithm strategies in solving the hybrid energy storage optimization configuration problems. The approach concurrently considers system economics and grid stability while improving power quality.

Photovoltaic Power Generation Forecasting with Hidden Markov Model and Long Short-Term Memory in MISO and SISO Configurations

Photovoltaic (PV) power generation forecasting is an important research topic, aiming to mitigate variability caused by weather conditions and improve power generation planning. Climate factors, including solar irradiance, temperature, and cloud cover, influence the energy conversion achieved by PV systems. Long-term weather forecasting improves PV power generation planning, while short-term forecasting enhances control methods, such as managing ramp rates. The stochastic nature of weather variables poses a challenge for linear regression methods. Consequently, advanced, state-of-the-art machine learning (ML) approaches capable of handling non-linear data, such as long short-term memory (LSTM), have emerged. This paper introduces the implementation of a multivariate machine learning model to forecast PV power generation, considering multiple weather variables. A deep learning solution was implemented to analyze weather variables in a short time horizon. Utilizing a hidden Markov model for data preprocessing, an LSTM model was trained using the Alice Spring dataset provided by DKA Solar Center. The proposed workflow demonstrated superior performance compared to the results obtained by state-of-the-art methods, including support vector machine, radiation classification coordinate with LSTM (RCC-LSTM), and ESNCNN specifically concerning the proposed multi-input single-output LSTM model. This improvement is attributed to incorporating input features such as active power, temperature, humidity, horizontal and diffuse irradiance, and wind direction, with active power serving as the output variable. The proposed workflow achieved a mean square error (MSE) of 2.17×10−7, a root mean square error (RMSE) of 4.65×10−4, and a mean absolute error (MAE) of 4.04×10−4.

Optimal allocation method of shared energy storage in multienergy coupled microgrid with carbon emission constraint considering dual uncertainty of source and load

Abstract In order to realize the stable operation of the multienergy coupled microgrid under the low-carbon constraint, a carbon emission constrained multienergy coupled microgrid shared energy storage optimization configuration method considering the dual uncertainty of source and load is proposed. This method constructs a source load double uncertainty and carbon emission model, and it analyzes the output characteristics and load demand characteristics of multiple coupled energy sources and the carbon emission results under these characteristics. According to the analysis results, the multienergy coupling microgrid shared energy storage optimization configuration model is constructed, and after the optimization configuration objective function and constraint conditions are determined, the bat algorithm is used to solve it, and the microgrid shared energy storage optimization configuration scheme that meets the objective function and constraint conditions is obtained. The test results show that after the method is applied, the utilization rate of multi-energy sources reaches 90.6%, and the expected carbon emissions are lower than 2.04 × 104kg. The microgrid fluctuation suppression rate reached 92.7%, and the net load fluctuation range was significantly reduced. Thus, it ensures reliable power supply of the microgrid and has good applicability.

A joint configuration method for multiple flexible resources in low carbon distribution networks based on massive scene dimensionality reduction

Abstract Due to the extensive access of renewable sources in distribution networks, this research proposes a joint configuration method for multiple flexible resources in low carbon distribution networks based on massive scene dimensionality reduction to maximize the access capacity of renewable sources. The dimensionality reduction clustering is carried out on the wind-light-load mass high-dimensional scenes by the principal component Gaussian mixture clustering algorithm, and the typical scene set of wind-power-loads is obtained. Hereafter, a joint configuration model for multiple flexible resources of distribution network for massive scenarios is constructed, and using the second-order cone relaxation technique to convert the non-convex constraints in the model to convex processing. Last, a Portugal 54 distribution system is employed to verify the proposed method.

Simulation and comprehensive 4E study of a high-efficient CCHP arrangement utilizing geothermal energy and eco-friendly heat recovery

In recent years, geothermal energy has received significant attention as it provides a sustainable and renewable energy resource for various applications. The primary objective of this study is to manage the relative irreversibility and enhance productivity through efficient utilization of geothermal energy. The present paper establishes a high-efficiency and environmentally sustainable heat integration model in a novel configuration to be a geothermal energy source having the ability to produce power, heat, and cooling. The proposed framework consists of a Kalina cycle, two organic Rankine cycles, an ammonia Rankine cycle, a single-flash power unit, and a combined cooling and power unit. The simulation of the proposed system is implemented within the Aspen HYSYS software, with an examination of its feasibility across energy, exergy, economic, and environmental considerations. The research findings indicate that the system can generate 23250.8 kW of power, 582.1 kg/s of hot water, and 2.812 kg/s of chilled water. Therefore, the total exergy destruction rate is 14104 kW, with the Kalina cycle accounting for the majority share at 40.47 % Moreover, the energy and energy efficiencies of the suggested structure are 61.81 % and 72.52 %, respectively. The environmental analysis indicates that the entire process lacks carbon dioxide emissions. Furthermore, the economic analysis reveals that the total unit exergy cost and levelized cost of energy are determined to be 5.13 $/GJ and 0.511 $/kWh, respectively. Also, the proposed system exhibits a favorable net present value of approximately 32.16 M$. Hence, the project demonstrates profitability.

Optimal Energy Configuration of Integrated Energy Community Considering Carbon Emission

An integrated energy community with a distributed utilization of renewable energy and complementary electricity–gas–cold–heat integrated energy will play an important role in energy conservation and emission reduction. In addition, compared with traditional thermoelectric power equipment, solid oxide fuel cells have many advantages, such as a high energy utilization rate, good waste heat quality, and low carbon emissions. Therefore, the SOFC-based multi-energy and energy storage sharing operation model of an integrated energy community with an electricity–gas–cooling–heat integrated energy system is constructed, and a bi-objective optimal configuration model considering the carbon emission index is established. Considering the economic objective of the smallest annual total operating cost as the most important objective in optimizing the planning model, the ε-constraint method is used to transform the environmental objective function with the smallest annual total carbon emission into a constraint condition under the decision making of an economic single objective function, and then the planning model is linearized and solved by using the Big-M method and the McCormick relaxation method. By calculating and analyzing the energy allocation results in five scenarios, the effectiveness and rationality of the model built in this article are verified. At the same time, the calculation example analysis results show that as the ε value decreases, the energy configuration of the integrated energy community will shift from natural gas to clean energy. From this perspective, the energy equipment configuration and operating costs will increase. However, the heat storage system and power storage system sharing can effectively reduce the energy allocation capacity and costs.

Optimal participation and cost allocation of shared energy storage considering customer directrix load demand response

In recent years, with the increase in the proportion of new energy connected to the grid, the main goal of energy storage on the load side and energy storage users is to maximize the overall interests. Based on the poor utilization ratio and high use cost of energy storage configured on the user side, the controllability of adjustable load and the rationality of energy storage configuration are two key points that need to be considered for social welfare maximization (SWM). Hence, this paper puts forward an implementation method of large-scale demand response (DR) based on the customer directrix load (CDL), in order to give full play to the DR of the load side and improve the utilization rate of shared energy storage (SES). First, the CDL proposed in this paper gives the full-time DR regulation target, which can better guide load shaping and solve the problem of flexible load controllability. Then, an SES configuration model based on SWM is built to further shape the load and improve the utilization rate and economy of energy storage. In addition, in the market with incomplete free competition, a recent market clearing strategy based on Vickery-Clark-groves (VCG) is proposed, which encourages market members to declare true information to achieve the global optimal effect and realize the sustainability of the proposed model. The simulation results show that with the participation of SES, the similarity of users based on CDL has increased by 8.59 % and 0.99 % respectively, and the social welfare has reached 41,297.71 yuan/day. The results also show that the reasonable cost allocation through the VCG mechanism shortens the investment return period of SES by 33.33 %, which promotes the investment enthusiasm of SES.

Size effect configuration modeling for the high-performance hybrid Fe-Si directional alloyed soft magnetic material (DASMM)

This work presents a novel hybrid gas/water atomized Fe-Si directional alloyed soft magnetic materials (DASMM), they have a combination of low core loss (Pcv), constant permeability (μ) and high compatibility. The particle size has a big influence upon the Fe-Si DASMP magnetic properties, the study result revealed that the coarse powder DASMM merely have a steady permeability of 60 in the frequency range between 10 and 100 kHz; while the fine particle (≥200 meshes) DASMM could maintain its constant permeability of 40 in the whole range between 10 and 500 kHz. The Pcv of Fe-Si DASMM rapidly decreased as the consisting powder particle size decrease, dramatically decreased from 277.36 W/kg for the 100 mesh to 62.00 W/kg for the 300 mesh Fe-Si DASMM. While the resistivity (Rs) sharply increased, it increased 9 times from 5.93 mΩ·cm for the 100 mesh to 54.33 mΩ·cm for the 300 mesh Fe-Si DASMM.

Strategic contexts, strategic orientations and organisational technology adoption: A configurational approach

AbstractDespite the broad application of the technology–organisation–environment (TOE) framework to explain firms' adoption of technology, prior research tends to over‐emphasise the independent effects of TOE elements while neglecting decision makers' strategic orientations when making organisational technology adoption decisions. This over‐simplistic interpretation of the TOE framework has culminated in inconsistent findings within extant literature. Considering the interdependencies among the three TOE elements in shaping organisational technology adoption and also decision‐makers' inclination to weigh the three TOE elements differently based on their strategic orientations, this study views organisational technology adoption from a systems standpoint based on a configurational approach. Particularly, we differentiate between two types of strategic orientations, namely functional orientation, which accentuates the technology‐induced efficiency gains and symbolic orientation, which stresses the image or reputation afforded through technology adoption. We advance a configurational model for organisational technology adoption that: (1) conceptualises organisational technology adoption as an outcome arising from distinct configurations of TOE elements, and; (2) extends the TOE framework by incorporating strategic orientation as an inevitable aspect of decision‐making for organisational technology adoption. To validate our proposed model, we conducted a field survey of 183 firms to collect data on their considerations underpinning organisational technology adoption before employing fuzzy‐set Qualitative Comparative Analysis to derive configurations of TOE elements responsible for driving such adoption. Analytical results reveal that the TOE configurations vary across three types of organisations (namely performance enhancer, image builder and strategic transformer). The theoretical and practical implications of our study are also discussed.