Performance Of Optimization Method Research Articles

Study on the Mining Effect and Optimal Design of Longwall Full Mining with Paste Partial Filling

Various methods of longwall full mining with partial filling have been extensively researched to satisfy the specific mining needs of pressurized-coal and residual-coal resources. This study introduces three longwall partial-filling-mining techniques: room–pillar filling mining, parallel-strip filling mining, and vertical-strip filling mining. Numerical simulations are employed to evaluate the efficacy of these methods. The findings indicate that vertical-strip filling mining results in minimal surface deformation and a more uniform distribution of displacements. In practical operations, the effectiveness of filling largely depends on the choice of filling technology and materials. The research further includes an optimization analysis of the filling technology, emphasizing the composition of the coal-gangue-paste filling system and the refinement of its components. Additionally, the study aims to explore the optimization analysis of filling materials, specifically focusing on performance-optimization methods. The experimental results illustrate that optimizing the filling materials can enhance the performance of filling paste, improving both early-stage and long-term compressive strength. Moreover, the paper examines the quantitative characterization of paste-filling-mining subsidence at various stages in conjunction with theoretical knowledge. Subsequently, mining-subsidence-control measures are recommended to address the primary deformation factors across different stages. Through an in-depth examination of filling-method designs, enhancements in filling technology, and predictions regarding filling-mining subsidence, this research offers valuable insights for optimizing longwall partial-filling-mining methods.

A methodical approach for the design of thermal energy storage systems in buildings: An eight‐step methodology

AbstractRecent research focuses on optimal design of thermal energy storage (TES) systems for various plants and processes, using advanced optimization techniques. There is a wide range of TES technologies for diverse thermal applications, each with unique technical and economic characteristics. Matching an application with the most suitable TES system remains challenging. This study proposes an eight‐step design methodology guiding the process from describing the thermal process to defining the most appropriate TES based on constraints and requirements. The steps include specifying the thermal process, system design parameters, storage characteristics, integration parameters, key performance indicators, optimization method, tools, and design robustness. Seven already‐designed TES systems are evaluated to assess the methodology's effectiveness, where the design procedures have been adapted to the proposed steps. Case studies involve various applications with both sensible and latent TES systems, demonstrating the applicability of the proposed design procedure. A significant diversity exists among the design cases regarding the design objective, input, design, and output parameters. Nevertheless, the design procedure in each case can be deconstructed into the outlined design steps. The last design step has been excluded from all case studies due to insufficient information regarding the robustness of the design process. The paper demonstrates how a methodical approach can be applied to examine the TES design and the integration. The design steps proposed in this study can serve as a foundation for developing a more systematic approach for designing TES systems in future works, resulting in simplifying the design process.

A CASE STUDY: INTELLIGENT SHADING RETROFIT TO EXISTING HOME-OFFICE USING MULTI-OBJECTIVE OPTIMIZATION

ABSTRACT Improved energy performance and occupant comfort are driving building design decisions due to the increasing demand for sustainable and green buildings. However, despite the variety of technological developments in other fields, the range of solutions to improve building performance is limited. One of the main limitations for an early designer is a performance evaluation method to facilitate the design process. This paper offers a new shading performance optimization process that can help designers evaluate both daylighting and energy performance and generate optimized and flexible designs that can be further improved by implementing user-specific automation. The proposed performance optimization method utilizes parametric design, building simulation models, and Genetic Algorithms. Common shading design systems are explored through parametric design, and daylighting and energy modeling simulations are performed to evaluate shading device performance. Genetic Algorithms are used to identify design options with optimal energy and daylighting performance. A case study is conducted to verify the effectiveness of the overall process. Results are used to analyze the influence of design decisions among different shading designs. Finally, future directions in both shading design and energy optimization are presented.

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

AbstractLake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

Performance optimization of metal-supported solid oxide fuel cells using cathode and full cell impregnation with La0.4Sr0.6Co0.2Fe0.7Nb0.1O3-δ electrode.

In this study, precursor solutions of La0.4Sr0.6Co0.2Fe0.7Nb0.1O3-δ (LSCFN) symmetric electrode were prepared, and the applications of cathode impregnation and full cell impregnation in the preparation and performance optimization of four-layer metal-supported solid oxide fuel cells (MSCs) were thoroughly investigated. Test results indicate that the polarization impedance of cathode impregnated MSCs under H2 and CH4 atmospheres at 750 °C is approximately 0.1 Ω cm2 and 0.41 Ω cm2, respectively, with power densities of 1115 mW cm-2 and 700 mW cm-2, respectively. Meanwhile, the polarization impedance of full cell impregnated MSCs under the same conditions is 0.12 Ω cm2 and 0.40 Ω cm2, with power densities of 945 mW cm-2 and 840 mW cm-2, respectively. Remarkably, MSCs full cell impregnated LSCFN exhibit outstanding stability performance under CH4 atmosphere in a 100 h stability test. Research on the application of impregnation method for performance optimization of metal-supported cells is relatively scarce. The results reveal the feasibility of simplifying MSCs preparation steps using full cell impregnation method, further promoting the widespread application of metal-supported overall cells.

An Improved Mask R-CNN Micro-Crack Detection Model for the Surface of Metal Structural Parts.

Micro-crack detection is an essential task in critical equipment health monitoring. Accurate and timely detection of micro-cracks can ensure the healthy and stable service of equipment. Aiming at improving the low accuracy of the conventional target detection model during the task of detecting micro-cracks on the surface of metal structural parts, this paper built a micro-cracks dataset and explored a detection performance optimization method based on Mask R-CNN. Firstly, we improved the original FPN structure, adding a bottom-up feature fusion path to enhance the information utilization rate of the underlying feature layer. Secondly, we added the methods of deformable convolution kernel and attention mechanism to ResNet, which can improve the efficiency of feature extraction. Lastly, we modified the original loss function to optimize the network training effect and model convergence rate. The ablation comparison experiments shows that all the improvement schemes proposed in this paper have improved the performance of the original Mask R-CNN. The integration of all the improvement schemes can produce the most significant performance improvement effects in recognition, classification, and positioning simultaneously, thus proving the rationality and feasibility of the improved scheme in this paper.

Retracted: Research on Performance Optimization Method of UAV Communication Link in Emergency Communication Environment

Retracted: Research on Performance Optimization Method of UAV Communication Link in Emergency Communication Environment

A Review of Recent Modification, Optimization, and Functionalization Methods for Organic Anticorrosive Coatings

Corrosion problems are widespread in nature. As one of the most convenient and efficient methods for metal anticorrosion protection, anticorrosive coatings have received increasing attention. With the continuous advancement of science and technology, more novel functional anticorrosion coatings are being extensively researched. This review provides an overview of recent research progress in anticorrosion coatings and functionalized modified materials. Recent methods for performance optimization can be categorized into three main sections: modification by nanoparticles, modification by carbon-based materials, and specific functionalization (barrier effect, passivation, shielding effect, resistivity, self-repair). Through modification, the anticorrosion performance of coatings is significantly enhanced, with impedance levels improving by up to three orders of magnitude. Furthermore, modification imparts additional outstanding features to the coatings, such as high-temperature resistance, thermal conductivity, self-healing, and hydrophobicity. Finally, the future development trend of anticorrosion coatings is proposed, and several reasonable suggestions are put forward for the challenges faced.

Optomechanical integrated performance analysis and optimization of a greenhouse gas absorption spectrometer

Design of a space-borne opomechanical system with high spatial resolution, spectral resolution and quality imagery demands a tightly integrated performance optimization method to expand the design space and improve the optical performance of the system. In this paper, a Kriging-based method is proposed for optomechanical integrated performance optimization of a greenhouse gas absorption spectrometer (GGAS). The proposed method combines the thermal-structural-optical (TSO) integrated analysis method and the Kriging meta-modeling technique, in which the latter one is used to replace the former analysis process and approximates the I/O functions implied in the underlying TSO simulation model. Structural deformation characteristics and optical performance of GGAS are evaluated firstly. Then, optomechanical structural optimization of GGAS is carried out to achieve lightweight of three sub-benches. Results show that the proposed method is able to reduce the weight of three sub-benches by 11.85 % compared to the common opomechanical design method while satisfying almost the same constraints.

Optimization Strategy for Generative Adversarial Networks Design

Generative Adversarial Networks (GANs) are a powerful class of deep learning models that can generate realistic synthetic data. However, designing and optimizing GANs can be a difficult task due to various technical challenges. The article provides a comprehensive analysis of solution methods for GAN performance optimization. The research covers a range of GAN design components, including loss functions, activation functions, batch normalization, weight clipping, gradient penalty, stability problems, performance evaluation, mini-batch discrimination, and other aspects. The article reviews various techniques used to address these challenges and highlights the advancements in the field. The article offers an up-to-date overview of the state-of-the-art methods for structuring, designing, and optimizing GANs, which will be valuable for researchers and practitioners. The implementation of the optimization strategy for the design of standard and deep convolutional GANs (handwritten digits and fingerprints) developed by the authors is discussed in detail, the obtained results confirm the effectiveness of the proposed optimization approach.

Non‐Contact Triboelectric Nanogenerator

AbstractWith the rapid development of science and technology, there is an increasing demand for sustainable energy sources. Although triboelectric nanogenerators (TENGs) can realize self‐powered supply by using various weak mechanical energy, the device's robustness and reliability are seriously affected by frequent, direct, and long‐term mechanical shocks, as well as negative environmental factors during equipment operation. Therefore, researchers have developed non‐contact triboelectric nanogenerators (NC‐TENGs) based on the principle of electrostatic induction. This technology enables electricity generation via relative movement of friction layers even when they are not in direct contact, which consequently leads to enhance device robustness, prolonged service life, and broadened operation scenarios. In this review, the recent progress of NC‐TENG from fundamental theory to practical applications is systematically summarized. The basic structure and working principle of NC‐TENG are first introduced, followed by a discussion of the devices’ structural construction and performance optimization methods. Furthermore, applications of NC‐TENG in automatic force position detection system, non‐contact human‐machine interface, and electrical energy harvesting are reviewed. Finally, a brief prospectus for the future development of NC‐TENG is provided.

Performance enhancement of up-flow anaerobic sludge blanket reactor for psychrophilic temperature during the dry season: Kality wastewater treatment plant

The main purpose of wastewater treatment plant (WWTP) is to reduce organic and inorganic pollutants to meet standards. But WWTPs employing up flow anaerobic sludge blanket (UASB) reactors under psychrophilic temperature are currently removing about 55% chemical oxygen demand (COD) and 70% total dissolved solids (TSS). The research was done to increase the treatment efficiencies of UASB reactor working under psychrophilic conditions through optimization of operational parameters like temperature, organic loading rate (OLR), pH and hydraulic retention time (HRT). Experimentation was carried out in a 0.0486 m3 square-shaped pilot-scale UASB reactor. Experimental design response surface method (RSM) for performance enhancement and optimization of UASB reactor operational parameters through five levels of central composite design (CCD) was used. The optimized operational parameters obtained from CCD-RSM were as follows: temperature of 21.58 °C, OLR of 2.99 kg COD/m3.d, HRT of 4.37hrs and pH of 6.3. Using optimized parameters, tests yielded efficiencies of 92.70%, 99.06%, and 94.50% for COD, TSS, and volatile suspended solid (VSS) respectively. The outlet concentrations of alkalinity, and volatile fatty acids (VFA), were found to be lower than the inlet concentrations. The alkalinity in the system accepts the hydrogen ion released by acids and the system is taken over by methanogensis to maintain the pH. The outlet concentration of sulfate ion was found to be increasing due to inhabitation of sulfur-reducing bacteria by an anaerobic condition of VFA and alkalinity at a pH less than 7.8.This process favors the production of CH4 than H2S gas. In general, there was a high likelihood of improving the performance of UASB reactor operating at psychrophilic temperature by optimizing operational parameters.

XGBoost Analysis based on Consumer Behavior

With the rapid development of the Internet and e-commerce, a large amount of consumer data has become available, which allows us to better understand consumer preferences and purchasing trends. XGBoost (Extreme Gradient Boosting) is a powerful machine learning algorithm that performs well when dealing with large data sets and complex features. This article first introduces the basic principles of the XGBoost algorithm, and then discusses in detail how to apply it to consumer behavior analysis. This paper uses real consumer data set, including multi-dimensional information such as customer identification, product identification and customer purchasing behavior. By building the XGBoost model, we are able to identify important features, predict consumer purchase intentions, and provide personalized recommendations. In addition, the performance evaluation and optimization methods of the model are discussed to ensure its accuracy and practicability. Finally, we summarize the main findings of this study, highlighting the potential applications of XGBoost analytics based on consumer behavior in marketing and business decisions. By digging deeper into consumer behavior data, businesses can better meet customer needs, improve sales efficiency, and achieve sustainable competitive advantage, and this research provides strong support for the use of machine learning techniques to optimize market strategies.

Progress of Prussian Blue and Its Analogues as Cathode Materials for Potassium Ion Batteries

AbstractEnvironmental pollution and the energy crisis have promoted the development of clean energy as well as new‐generation energy storage systems. Potassium ion batteries (PIBs) have emerged as a possible alternative to lithium‐ion batteries due to their abundant reserves, low cost, and impressive electrochemical performance. However, the search for suitable cathode materials has become particularly crucial. Recently, Prussian blue (PB) has been investigated as a potential cathode material for PIBs, which has an open three‐dimensional framework to accommodate a large volume of potassium ions and adjustable composition for different applications. In this review, Prussian blue and its analogues (PBAs) and their application in PIBs were summarized detailly. We presented the composition, structure, potassium ion storage mechanism, preparation process of PBAs, and then focus on the performance optimization methods of the PBAs, including transition metal doping and conductive material adding into PBAs. Finally, the challenges as well as the outlook on the future development of PBAs were proposed for further application in this battery system.

Study on Optimum Selection Method of Additives for Slick-Water Fracturing Fluid

In order to clarify the research idea of fluid system optimization in slick-water fracturing, a scientific and effective single-agent optimization method was selected to study the performance optimization method and performance evaluation method of drag reducer, clay stabilizer and cleanup additive in slick-water fracturing fluid. The specific experimental steps and application examples were investigated. To select the drag reducer, the drag reduction rate rate of the drag reducer at different concentrations should be measured first, the optimal concentration of a single agent should be selected, and then the stability test of temperature, flow rate and shear time should be performed on the optimal concentration of a single agent, and finally the combination formula with high drag reduction rate rate and stable performance can be selected. To optimize the clay stabilizer, the optimal concentration of single agent should be obtained by measuring the anti-swelling rate of different single agent and different concentration, and then the anti-swelling rate of each compound formula should be determined according to the optimal concentration of single agent, and finally the anti-swelling performance of the composite combination should be evaluated according to the core flow experiment. The optimal concentration of a single agent should first be optimized according to the surface tension of the cleanup additive, and then the combined formula should be optimized according to the surface tension of the combined combination, and the cleanup additive ability should be evaluated by core spontaneous imbibition or measuring the increase rate of cleanup additive.

Optimizing Performance in Federated Person Re-Identification through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems

In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.

A New Performance Optimization Method for Linear Motor Feeding System

The linear motor feeding system is a typical electromechanical coupling system. Conventional characteristic analyses of electromechanical coupling often overlook the influence of flexible deformation in critical components of the linear motor feeding system. Moreover, when employing genetic algorithms to optimize servo system PID control parameters, slow convergence, nonconvergence, or premature convergence problems may arise. To address these issues, this paper proposes a new performance optimization method for a linear motor feeding system. The method uses a combination of “multi-body theory + finite element” to accurately account for the flexible deformation of critical components of the feeding system, establishes a rigid–flexible electromechanical coupling model of the linear motor feeding system, and optimizes the PID parameters of the established model with an improved adaptive genetic algorithm. Simulation results demonstrate that, when utilizing an adaptive genetic algorithm to optimize the rigid–flexible electromechanical coupling model and a control system model that disregards flexible body deformation, the system achieves stability in 0.02 s and 0.027 s with overshoots of 13% and 27%, respectively. These outcomes confirm the accuracy and importance of considering flexible body deformation in the optimization performance of a linear motor feeding system. At the same time, the time required to reach the steady state of the rigid–flexible electromechanical coupling model optimized by the adaptive genetic algorithm is shortened from 0.035 s to 0.02 s. The sinusoidal signal response curve of the optimized system does not exhibit any peak overshoot compared with that of the nonoptimized system, and the response speed is also faster. These results demonstrate the effectiveness of the rigid–flexible electromechanical coupling model optimized by the nonlinear adaptive genetic algorithm. The displacement response curves of the linear motor feeding system under different workbench loads are obtained through experiments and compared with those obtained from simulations to verify the established model and the correctness of the proposed method.

Multi-objective optimization of PEMFC performance based on grey correlation analysis and response surface method

This paper aims to combine grey correlation analysis and response surface method to propose a fast and effective performance optimization method for PEMFC. First, based on orthogonal test data, grey correlation analysis method is used to select four variables that have significant influence on PEMFC’s comprehensive performance from eight common parameters. Secondly, based on grey correlation analysis, the multi-objective optimization problem is transformed into a single objective optimization problem about correlation degree, and applying the response surface method to build the key parameters and the correlation between the second order prediction model. Therefore, the current density, system efficiency and oxygen distribution uniformity on cathode catalyst layer of PEMFC were optimized as a whole. Finally, the optimal parameter combination was obtained by optimizing the prediction model. The simulation results show that the optimized operating conditions are significantly improved in the three performance indexes compared with the basic model, which confirms the feasibility of this method in solving the multi-objective optimization problem, and can provide some reference for the optimal design of hydrogen fuel cells.

Performance Analysis and Optimal Design of a Novel Schöenflies-Motion Asymmetric Parallel Mechanism

Abstract Since previous studies of parallel mechanisms (PMs) have tended to favor symmetrical overall configuration to obtain relatively stable kinematic and dynamic performance and to satisfy isotropic requirements. The analysis of kinematic and dynamic performance of asymmetric mechanisms has been an issue of interest. In this paper, an asymmetric SCARA-type PM with four-degrees-of-freedom (DOF) is proposed. First, the orientation characteristic set is calculated to obtain the DOF of the PM. Then, the inverse kinematics and the velocity and acceleration of each branch chain of the mechanism are analyzed. The dynamic model of the mechanism is established according to the principle of virtual work. The workspace of the mechanism is drawn according to the constraints that have been given to the mechanism's kinematic pairs. The singularity, dexterity, motion/force transfer performance, and maximum acceleration performance of the mechanism are also analyzed. On this basis, the kinematic and dynamic performance evaluation indexes of the mechanism are studied. Finally, the workspace and acceleration performance of the mechanism are optimized based on the differential evolution (DE) algorithm to obtain the structural parameters when the mechanism achieves optimal performance. The asymmetric PM proposed in this paper, as well as the algorithm of performance index and optimization method used, can provide some reference value for configuration design and optimization analysis.

Research on Comprehensive Performance Optimization Method of Explosives and Propellants Oriented to the Whole Process

Explosives and propellants are common basic energy for weapons and equipment to achieve delivery, damage, and control, and are an important manifestation of national defense power. However, due to the wide variety of products and complex performance, the traditional serial design optimization mode can no longer meet the requirements of product diversification and rapid development. In this paper, the design process of explosives is deeply studied, and a method for optimizing the comprehensive performance of explosives and propellants oriented to the whole process is proposed. The method uses a comprehensive performance optimization engine to numerically model different design links in a unified parameter space, connects different links horizontally, and adopts a multi-objective optimization algorithm to comprehensively consider the optimization objectives of different links, realizing the automatic execution of the optimization process according to the design process. In order to verify the effectiveness of the method, this paper optimizes the formula of explosives and propellants based on two different types of explosives, gun propellant and rocket propellant. Experimental results show that this method improves the design efficiency and improves product quality under the premise of ensuring safety and manufacturability.