Browse
Subjects
Records with Subject: Optimization
Showing records 1 to 25 of 1543. [First] Page: 1 2 3 4 5 Last
Enhancing Damage Localization in GFRP Composite Plates: A Novel Approach Using Feedback Optimization and Multi-Label Classification
Jiayu Cao, Jianbin Liao, Jin Yan, Hongliang Yu
June 10, 2024 (v1)
Subject: Optimization
Keywords: damage localization, feedback optimization, GFRP, multi-label classification
Damage localization in GFRP (glass-fiber-reinforced polymer) composite plates is a crucial research area in marine engineering. This study introduces a feedback-based damage index (DI) combined with multi-label classification to enhance the accuracy of damage localization and address scenarios involving multiple damages. The research begins with the creation of a modal database for yachts’ GFRP composite plates using finite element modeling (FEM). A method for deriving a feedback-weighted matrix, based on the accuracy of the DI, is then developed. Sensitivity analysis reveals that the feedback DI is 50% more sensitive than the traditional DI, reducing false positives and missed detections. The associated feedback-weighted matrix depends solely on the structural shape, ensuring its transferability. To address the challenge for localizing multiple damages, a multi-label classification approach is proposed. The synergy between the feedback optimization and multi-label classification enabl... [more]
APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy
Mingqiang Gao, Xu Yang
June 10, 2024 (v1)
Subject: Optimization
Keywords: adaptive, complex optimization, particle swarm optimization (PSO), state-based
Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the... [more]
Analysis and Optimization of the Fuel Consumption of an Internal Combustion Vehicle by Minimizing the Parasitic Power in the Cooling System
Pedro H. A. Brayner, José Â. P. da Costa, Alvaro A. V. Ochoa, José J. Urbano, Gustavo N. P. Leite, Paula S. A. Michima
June 7, 2024 (v1)
Subject: Optimization
Keywords: cooling system, Energy Efficiency, fuel consumption, parasitic power
This study aims to enhance energy efficiency by reducing parasitic losses in the engine cooling system through a new drive strategy involving a two-stage water pump and a variable electro-fan. The fuel consumption gain analysis focused on a vehicle with average characteristics typical of 1.0L hatchbacks in the Brazilian market and urban driving conditions. The methodology implemented aims to minimize power absorbed by the forced water circulation and thermal rejection, thereby reducing parasitic losses, particularly during low-speed urban driving, without causing air-side heat exchanger saturation. The results show a potential decrease of up to 80% in power absorbed by the cooling system, leading to an estimated fuel consumption saving of approximately 1.4% during urban driving cycles.
A Hybrid Feature-Selection Method Based on mRMR and Binary Differential Evolution for Gene Selection
Kun Yu, Wei Li, Weidong Xie, Linjie Wang
June 7, 2024 (v1)
Subject: Optimization
Keywords: biomarker, differential evolution, feature selection, microarray data
The selection of critical features from microarray data as biomarkers holds significant importance in disease diagnosis and drug development. It is essential to reduce the number of biomarkers while maintaining their performance to effectively minimize subsequent validation costs. However, the processing of microarray data often encounters the challenge of the “curse of dimensionality”. Existing feature-selection methods face difficulties in effectively reducing feature dimensionality while ensuring classification accuracy, algorithm efficiency, and optimal search space exploration. This paper proposes a hybrid feature-selection algorithm based on an enhanced version of the Max Relevance and Min Redundancy (mRMR) method, coupled with differential evolution. The proposed method improves the quantization functions of mRMR to accommodate the continuous nature of microarray data attributes, utilizing them as the initial step in feature selection. Subsequently, an enhanced differential evol... [more]
Evaluation of Key Success Factors in the Visual Optimization of the 3D Forming of Soil-Shaping Ability
Fu-Chi Shih, Chi-Jui Tsai, Shu-Hsuan Chang
June 7, 2024 (v1)
Subject: Optimization
Keywords: 3D forming, Delphi technique, forming ability, manufacturing process, optimized design, soil shaping
Improving the quality of the manufacturing process is an important goal of professional technicians. This study systematically explored the key success factors in the product-forming ability and visual optimization of 3D forming in the clay-shaping process through actual manufacturing and implementation. The purpose of this study was to identify the forming technology and technical indicators that can successfully achieve a high degree of completeness and maturity in the manufacturing process, increasing the shaping performance of the end products and enabling the evaluation of optimization. In this study, we evaluated soil shaping, material use, the manufacturing process, and product forming. The key success factors were studied and analyzed via expert interviews. The research and analysis were summarized into 4 dimensions with 48 sub-dimensions. These included: (1) soil material, with 12 sub-dimensions; (2) the design concept, with 12 sub-dimensions; (3) the prototype process, compri... [more]
Research and Optimization of Operating Parameters of a Rotor Classifier for Calcined Petroleum Coke
Jiaxiang Peng, Chenxi Hui, Ziwei Zhao, Ying Fang
June 7, 2024 (v1)
Subject: Optimization
Keywords: calcined petroleum coke, classification performance, operating parameters, orthogonal experiment, rotor classifier
This article explores the impact of operating parameters on the classification efficiency of a rotor classifier. Based on the experimental data of calcined petroleum coke classification, a single-factor experimental analysis is conducted to find the relationship between operating parameters and classification performance. The cut size becomes progressively smaller as the rotor speed and feeding speed increase, and progressively larger as the inlet air volume increases. Newton’s classification efficiency and classification accuracy decreased with the increase in feeding speed. The range analysis of the orthogonal experiment shows that the rotor speed and inlet air volume have significant effects on the classification performance, but the effect of feed speed is relatively weak. In addition, the optimal combination of operating parameters is obtained by optimizing the operating parameters. Newton’s classification efficiency under this combination is estimated, and the estimated value is... [more]
Energy Optimization through Heat and Power Integration on a Chlorobenzenes Production Plant
Nawaf S. Alqahtani, Turki A. Alrefai, Abdulaziz M. Almutlaq, Saeed M. Alzahrani, Ahmed E. Abasaeed
June 7, 2024 (v1)
Subject: Optimization
Keywords: benzene, dichlorobenzene, monochlorobenzene, pinch analysis, process integration
In this research work, an attempt has been made to address the heat and power integration opportunities for the process of the chlorination of benzene. This process produces a mixture of chlorobenzenes. To increase the production of the dichlorobenzene portion, the ratio of chlorine to benzene is typically 2:1. A process simulation model is designed using Aspen Plus for the production of 70,000 tons/year of dichlorobenzene via the reaction of liquid benzene with gaseous chlorine. Energy analysis is performed for the effective utilization of the utilities by networking the heat exchangers. This modification reduced the process heating and cooling requirements by 56.7% and 12.7%, respectively, and a reduction by 35.4% in the operating costs is achieved, while the annualized fixed cost increased by 9.6%; these changes resulted in savings in the total annual costs of about 10.9%.
Optimization of Giant Magnetoimpedance Effect of Amorphous Microwires by Postprocessing
Valentina Zhukova, Paula Corte-Leon, Ahmed Talaat, Mihail Ipatov, Alfonso García-Gomez, Alvaro González, Juan Maria Blanco, Arcady Zhukov
June 7, 2024 (v1)
Subject: Optimization
Keywords: hysteresis loops, induced magnetic anisotropy, internal stresses, magnetic anisotropy, magnetic microwires, magnetoimpedance effect
Magnetic microwires with amorphous structures can present a unique combination of excellent magnetic softness and giant magnetoimpedance (GMI) effects together with reduced dimensions and good mechanical properties. Such unique properties make them suitable for various technological applications. The high GMI effect, observed in as-prepared Co-rich microwires, can be further optimized by postprocessing. However, unexpected magnetic hardening and a transformation of the linear hysteresis loop into a rectangular loop with a coercivity on the order of 90 A/m were observed in several Co-rich microwires upon conventional annealing. Several routes to improve magnetic softness and GMI effect in Fe- and Co-rich magnetic microwires are provided. We observed that stress annealing could remarkably improve the magnetic softness and GMI ratio of Co-rich microwires. Thus, almost unhysteretic loops with a coercivity of 2 A/m and a magnetic anisotropy field of about 70 A/m are achieved in Co-rich micr... [more]
Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning
Md. Samin Safayat Islam, Puja Ghosh, Md. Omer Faruque, Md. Rashidul Islam, Md. Alamgir Hossain, Md. Shafiul Alam, Md. Rafiqul Islam Sheikh
June 7, 2024 (v1)
Subject: Optimization
Keywords: Bayesian optimization, Gaussian process regression, Machine Learning, PV power forecasting, solar radiation intensity
The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper-parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compar... [more]
Optimization of Installation Position for Complex Space Curve Weldments in Robotic Friction Stir Welding Based on Dynamic Dual Particle Swarm Optimization
Guanchen Zong, Cunfeng Kang, Shujun Chen, Xiaoqing Jiang
June 7, 2024 (v1)
Subject: Optimization
Keywords: Cartesian stiffness ellipsoid, friction stir welding, robot stability index, robot stiffness, vibration stability
Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algo... [more]
Maximizing Corrosion Resistance of HA+Ce Coated Mg Implants Using Random Forest and Whale Optimization Algorithm
Zeinab Rajabi, Faramarz Afshar Taromi, Saeed Pourmahdian, Hossein Eivaz Mohammadloo
June 6, 2024 (v1)
Subject: Optimization
Keywords: cerium, coatings, corrosion resistance, hydroxyapatite, magnesium implants, random forest, whale optimization algorithm (WOA)
In this paper, a hybrid three-stage methodology based on in vitro experiments, simulations, and metaheuristic optimization is presented to enhance the corrosion resistance of hydroxyapatite (HA)-coated magnesium implants in biomedical applications. In the first stage, we add cerium (Ce) to HA and present a new coating (named HA+Ce) to improve the resistance of the coating to corrosion. Then, various HA+Ce compounds with different factors (e.g., concentration, pH, immersion time, and temperature) are generated and their propensity for corrosion is examined in a physiological environment using EIS and DC polarization tests in a simulated body fluid solution. Eventually, a comprehensive dataset comprising 1024 HA+Ce coating samples is collected. In the second stage, machine learning using random forest (RF) is used to learn the relation between the input factors of the coating and its corrosion resistance. In the third stage, a metaheuristic algorithm based on the whale optimization algor... [more]
Integrating Improved Coati Optimization Algorithm and Bidirectional Long Short-Term Memory Network for Advanced Fault Warning in Industrial Systems
Kaishi Ji, Azadeh Dogani, Nan Jin, Xuesong Zhang
June 6, 2024 (v1)
Subject: Optimization
Keywords: bidirectional long short-term memory, fault warning, improved coati optimization algorithm, industrial data analysis, predictive maintenance
In today’s industrial landscape, the imperative of fault warning for equipment and systems underscores its critical significance in research. The deployment of fault warning systems not only facilitates the early detection and identification of potential equipment failures, minimizing downtime and maintenance costs, but also bolsters equipment reliability and safety. However, the intricacies and non-linearity inherent in industrial data often pose challenges to traditional fault warning methods, resulting in diminished performance, especially with complex datasets. To address this challenge, we introduce a pioneering fault warning approach that integrates an enhanced Coati Optimization Algorithm (ICOA) with a Bidirectional Long Short-Term Memory (Bi-LSTM) network. Our strategy involves a triple approach incorporating chaos mapping, Gaussian walk, and random walk to mitigate the randomness of the initial solution in the conventional Coati Optimization Algorithm (COA). We augment its sea... [more]
Application of Deep Learning Algorithm in Optimization Control of Electrostatic Precipitator in Coal-Fired Power Plants
Jianjun Zhu, Chao Feng, Zhongyang Zhao, Haoming Yang, Yujie Liu
June 6, 2024 (v1)
Subject: Optimization
Keywords: attention mechanism, carbon emissions reduction, concentration prediction, energy saving, long short-term memory, Particle Swarm Optimization, pollution reduction
The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants. The pollution reduction and carbon emissions reduction in coal-fired power plants will be a key task in the future. In this paper, an optimization technique for the operation of an electrostatic precipitator is proposed. Firstly, the voltage-current model is constructed based on the modified dust charging mechanism; the modified parameters are trained through the gradient descent method. Then, the outlet dust concentration prediction model is constructed by coupling the mechanism model with the data model; the data model adopts the long short-term memory network and the attention mechanism. Finally, the particle swarm optimization algorithm is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate predictions of the secondary current and outlet... [more]
Determining Optimal Assembly Condition for Lens Module Production by Combining Genetic Algorithm and C-BLSTM
Hyegeun Min, Yeonbin Son, Yerim Choi
June 6, 2024 (v1)
Subject: Optimization
Keywords: convolutional–bidirectional long short-term memory, Genetic Algorithm, lens module, lens module production, optimal assembly condition, part lens assembly
Mobile camera modules are manufactured by aligning and assembling multiple differently shaped part lenses. Therefore, selecting the part lenses to assemble from candidates (called cavities) and determining the directional angle of each part lens for assembly have been important issues to maximize production yield. Currently, this process is manually conducted by experts at the manufacturing site, and the manual assembly condition optimization carries the risk of reduced production yield and increased failure cost as it largely depends on one’s expertise. Herein, we propose an AI framework that determines the optimal assembly condition including the combination of part lens cavities and the directional angles of part lenses. To achieve this, we combine the genetic algorithm with convolutional bidirectional long-term short-term memory (C-BLSTM). To the best of our knowledge, this is the first study on lens module production finding the optimal combination of part lens cavities and direct... [more]
Synergetic Mechanism of Multiple Industrial Solid Waste-Based Geopolymer Binder for Soil Stabilization: Optimization Using D-Optimal Mixture Design
Xiaoli Wang, Xiancong Wang, Pingfeng Fu, Bolan Lei, Jinjin Shi, Miao Xu
June 6, 2024 (v1)
Subject: Optimization
Keywords: D-optimal mixture approach, hydration mechanism, industrial solid waste, soil stabilization
In order to improve the comprehensive utilization rate of industrial solid waste and the road quality, a novel low-carbon and environmental friendly soil stabilizer is proposed. In this study, steel slag (SS), carbide slag (CS), blast furnace slag (BFS), fly ash (FA), and desulfurized gypsum (DG) were used as raw materials to develop a multiple industrial solid waste-based soil stabilizer (MSWSS). The optimal mix ratio of the raw materials determined by D-optimal design was as follows: 5% SS, 50% CS, 15% BFS, 15% DG, and 15% FA. The 7-day unconfined compressive strength (UCS) of MSWSS-stabilized soil was 1.7 MPa, which was 36% higher than stabilization with ordinary portland cement (OPC) and met the construction requirements of highways. After 7 days of curing, the UCS of MSWSS-stabilized soil was significantly higher than that in the OPC group. X-ray powder diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) analysis indicated that the prominent... [more]
Optimization of Anti-Skid and Noise Reduction Performance of Cement Concrete Pavement with Different Grooved and Dragged Textures
Biyu Yang, Songli Yang, Zhoujing Ye, Xiaohua Zhou, Linbing Wang
June 6, 2024 (v1)
Subject: Optimization
Keywords: cement concrete pavement, dragging, grooving, skid resistance, texture, tire/pavement noise
Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise. This study addressed the critical need to optimize pavement textures to balance improved skid resistance with noise reduction. Tests were conducted to assess the influence of surface texture on skid resistance and noise, exploring the relationship between texture attributes and their performance in these areas. The investigation examined the effects of texture representation methods, mean profile depth, and the high-speed sideway force coefficient (SFC) on noise intensity and pavement skid resistance. The findings revealed that transverse grooves significantly improved the SFC, enhancing skid resistance. In contrast, longitudinal burlap drag, thro... [more]
Bi-Level Inverse Robust Optimization Dispatch of Wind Power and Pumped Storage Hydropower Complementary Systems
Xiuyan Jing, Liantao Ji, Huan Xie
June 5, 2024 (v1)
Subject: Optimization
Keywords: economic dispatch, pumped storage hydropower, wind power
This paper presents a bi-level inverse robust economic dispatch optimization model consisting of wind turbines and pumped storage hydropower (PSH). The inner level model aims to minimize the total generation cost, while the outer level introduces the optimal inverse robust index (OIRI) for wind power output based on the ideal perturbation constraints of the objective function. The OIRI represents the maximum distance by which decision variables in the non-dominated frontier can be perturbed. Compared to traditional methods for quantifying the worst-case sensitivity region using polygons and ellipses, the OIRI can more accurately quantify parameter uncertainty. We integrate the grid multi-objective bacterial colony chemotaxis algorithm and the bisection method to solve the proposed model. The former is adopted to solve the inner level problem, while the latter is used to calculate the OIRI. The proposed approach establishes the relationship between the maximum forecast deviation and the... [more]
Capacity Optimization Configuration for a Park-Level Hybrid Energy Storage System Based on an Improved Cuckoo Algorithm
Zhangchenlong Huang, Lei Bei, Ben Wang, Linlin Xu
June 5, 2024 (v1)
Subject: Optimization
Keywords: analytic hierarchy process, cuckoo algorithm, hybrid energy storage, multi-objective optimization
To promote the development of green industries in the industrial park, a microgrid system consisting of wind power, photovoltaic, and hybrid energy storage (WT-PV-HES) was constructed. It effectively promotes the local consumption of wind and solar energy while reducing the burden on the grid infrastructure. In this study, the analytic hierarchy process (AHP) was used to decompose the multi-objective function into a single-objective function. The economic and environmental benefits of the system were taken as the objective function. Furthermore, the cuckoo search algorithm (CS) was used to solve the specific capacity of each distributed power source. Different scenarios were applied to study the specific capacity of microgrid systems. The results show that the equivalent annual cost of the WT-PV-HES microgrid system is reduced by 7.3 percent and 62.23 percent, respectively. The carbon disposal cost is reduced by 1.71 and 2.38 times, respectively. The carbon treatment cost is more sensi... [more]
Performance and Formula Optimization of Graphene-Modified Tungsten Carbide Coating to Improve Adaptability to High-Speed Fluid Flow in Wellbore
Minsheng Wang, Lingchao Xuan, Lei Wang, Jiangshuai Wang
June 5, 2024 (v1)
Subject: Optimization
Keywords: coating, graphene, Optimization, PDC drill bit, tungsten carbide
In order to improve the erosion resistance of steel PDC (Polycrystalline Diamond Compact) bit under high-speed fluid flow conditions underground, it is necessary to develop a high-performance erosion-resistant coating. In this paper, laser cladding was used to prepare the new coating by modifying tungsten carbide with graphene. And the effects of tungsten carbide content and graphene content on the coating performance have been thoroughly studied and analyzed to obtain the optimal covering layer. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding tungsten carbide, the hardness has significantly improved. However, when the tungsten carbide content further increases more than 30%, the increase in hardness is limited. In addition, when the content of graphene is more than 0.3%, the branched structure becomes thicker. In detail, this is a phenomenon where the segregation of Cr, Si, and W becomes very obvious aga... [more]
Path Optimization of Aircraft-Gear-Tooth-Surface Detection Based on Improved Genetic Algorithm
Xiaomeng Chu, Zhiji Zhou
June 5, 2024 (v1)
Subject: Optimization
Keywords: detection path, face gear, intelligent algorithm, path optimization
Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the sho... [more]
Improving Ammonia Emission Model of Urea Fertilizer Fluidized Bed Granulation System Using Particle Swarm Optimization for Sustainable Fertilizer Manufacturing Practice
Norhidayah Mohamad, Nor Azlina Ab. Aziz, Anith Khairunnisa Ghazali, Mohd Rizal Salleh
June 5, 2024 (v1)
Subject: Optimization
Keywords: ammonia emission, granulation, Particle Swarm Optimization, urea fertilizer
Granulation is an important class of production processes in food, chemical and pharmaceutical manufacturing industries. In urea fertilizer manufacturing, fluidized beds are often used for the granulation system. However, the granulation processes release ammonia to the environment. Ammonia gas can contribute to eutrophication, which is an oversupply of nitrogen and acidification to the ecosystems. Eutrophication may cause major disruptions of aquatic ecosystems. It is estimated that global ammonia emissions from urea fertilizer processes are approximately at 10 to 12 Tg N/year, which represents 23% of overall ammonia released globally. Therefore, accurate modeling of the ammonia emission by the urea fertilizer fluidized bed granulation system is important. It allows for the system to be operated efficiently and within sustainable condition. This research attempts to optimize the model of the system using the particle swarm optimization (PSO) algorithm. The model takes pressure (Mpa),... [more]
Machine Learning Algorithms That Emulate Controllers Based on Particle Swarm Optimization—An Application to a Photobioreactor for Algal Growth
Viorel Mînzu, Iulian Arama, Eugen Rusu
June 5, 2024 (v1)
Subject: Optimization
Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that leads to a large controller execution time. The main motivation of this work is to decrease the computational effort and, consequently, the controller execution time. This paper proposes to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO and PM); the training data are obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple linear regression and the regression neural networks were considered the predicting models. This paper first proposes algorithms for collecting and... [more]
Optimization of the Assessment Method for Photovoltaic Module Enhancers: A Cost-Efficient Economic Approach Developed through Modified Area and Cost Factor
Sakhr M. Sultan, Tso Chih Ping, Khan Sobayel, Mohammad Z. Abdullah, Kamaruzzaman Sopian
June 5, 2024 (v1)
Subject: Optimization
Keywords: cooler, modified cost and area effectiveness, PV performance, reflector, solar energy
The advancement of photovoltaic module (PV) enhancer technology shows significant promise due to its rapid growth. Nevertheless, there remains a requirement for ongoing research to refine the evaluation techniques for this technology. In a prior investigation, the concept of the area and cost-effectiveness factor, denoted as FCAE, was introduced to analyze the economic impact of enhancing the PV through techniques such as reflectors or coolers. This metric relates the surface area and manufacturing expenses of a PV enhancer to its capacity for improving the PV output power, aiding in the comparison of different enhancer types. However, this assessment approach is costly, requiring a set of PVs without enhancers to be compared with an equal number of modules fitted with enhancers. This paper introduces a modified version of this metric, termed the modified area and cost-effectiveness factor (FMCAE), along with its minimum value (FMCAE,min), with the aim of reducing the assessment expens... [more]
Progress of Optimization in Manufacturing Industries and Energy System
Dapeng Zhang, Qiangda Yang, Yuwen You
June 5, 2024 (v1)
Subject: Optimization
The manufacturing and energy industry are typical complex large systems which cover a long cycle such as design [...]
Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm
Zhengrui Zhang, Fei Wu, Aonan Wu
June 5, 2024 (v1)
Subject: Optimization
Keywords: hard turning, improved algorithm, machining process, multi-objective optimization, NSGA-II algorithm, process parameters
To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate.... [more]
Showing records 1 to 25 of 1543. [First] Page: 1 2 3 4 5 Last
[Show All Subjects]