Grey Relational Analysis Research Articles

Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method

This paper introduces a multi-objective optimization problem (MPO) for the forming process parameters of disc forgings using grey relational analysis (GRA) and the response surface methodology (RSM). Firstly, an experimental design based on the Box–Behnken design (BBD) principle was established, and simulations were performed in Deform to obtain response data. Secondly, GRA was used to transform the MPO into a grey relational degree (GRD) problem, and the entropic weight method was integrated to ascertain the influence weights of each variable on GRD. Then, a quadratic polynomial prediction model based on the RSM was constructed, and its accuracy was ensured through model validation. Finally, the optimal process parameter combination was determined through the particle swarm optimization algorithm, which included a friction coefficient of 0.3, an initial temperature of 1250 °C, and a downward pressing speed of 7.5 mm/s. The results of the experimental investigation indicate that optimized process parameters significantly reduce the forming load, equivalent stress, and damage value, effectively enhancing the overall quality of forged parts.

Analysis of Antioxidant Properties and Compounds in Different Rosa Taxa Fruits Using UV-Vis and UPLC-TQ-MS Techniques.

This comprehensive analysis of the fruits of Rosa spp. (FR) evaluates their chemical components and antioxidant activity. The study quantified total flavonoids and polyphenols using aluminum trichloride colorimetric assay and Folin-Ciocalteu methods, with the fruit of Rosa. laxa Rtez. var. mollis Yü et Ku. sample exhibiting the highest concentrations of 59.21 mg/g and 81.13 mg/g, respectively. Ultra-High-Performance Liquid Chromatography-Triple Quadrupole Mass Spectrometry (UPLC-TQ-MS) assessed seven primary components, with notable levels of euscaphic acid, ursolic acid, and gallic acid. Antioxidant activities were tested using DPPH and ABTS methods, showing strong activities in samples the fruits of Rosa. persica Mickx ex Juss. and Rosa. laxa Rtez. var. kaschgarica (Rupr.) Y. L. Han. Chemometric analyses, including similarity, cluster, principal component, and grey relational analyses, were used to explore relationships between FR varieties and their antioxidant properties. The study provides a vital basis for future FR quality assessments.

Enhanced machining of Al 10%SiCmicro 1%SiCnano hybrid composite using rotary tool rotary workpiece EDM with bio dielectrics and treated tools

A sustainable approach was proposed to address environmental pollution, carbon footprint and economic efficiency challenges in Electrical Discharge Machining (EDM). This approach involved the use of Bio-dielectric such as biodiesel and Bio fuel (distilled water with 10% ethanol). The EDM process performance was further optimized by experimenting with both electrodes’ rotation (i.e., in same direction, opposite direction, no rotation) and the use of treated tools (no treatment, heat treatment, cryogenic treatment). Biodiesel as a bio-dielectric showed promise by delivering the highest Material Removal Rate (MRR) and the lowest Tool Wear Rate (TWR). Bio-fuel (distilled water with 10% ethanol) resulted in the lowest Surface Roughness (SR) and cleaner machined surface with least carbon deposition. Additionally, electrode rotation improved flushing and enhanced performance parameters, with opposite direction rotation yielding the highest MRR and the lowest SR. However, no rotation of electrodes resulted in the lowest TWR. The use of treated tools, specifically heat-treated and cryogenically treated tools, also improved performance and reduced energy consumption, with cryogenic treatment providing the highest MRR, heat treatment giving least SR, and no treatment providing least TWR. Certain interactions between factors significantly impacted performance parameters. Grey relational analysis revealed that using distilled water with 10% ethanol as a dielectric, employing cryogenically treated copper tools, and having no rotation of both electrodes yielded the best performance parameters.

Quantifying drivers of virtual reality acceptance in tourism planning using a grey system theory-based approach

PurposeThis study examines the role of Virtual Reality (VR) in tourism marketing, analyzing its psychological effects, demographic differences and factors influencing adoption. It seeks to identify and measure the main drivers and obstacles to VR acceptance, improving predictions of adoption trends in tourism planning and experiences across various demographics.Design/methodology/approachThis study employs a quantitative method, blending statistical analysis with Grey System Theory. It analyzes data from VR-experienced participants using ANOVA, regression models and dynamic grey relational analysis to produce actionable insights. Data were collected from July to September 2023 from participants experienced with VR in tourism.FindingsUser engagement, positive word-of-mouth and a desire for immersive experiences drive VR acceptance, especially among frequent travelers and tech enthusiasts. While VR can enhance trip enjoyment, its long-term impact varies. To increase VR adoption in tourism, strategies include targeting frequent travelers and online users, creating accessible and engaging content, leveraging positive feedback and partnering with influencers to develop interactive VR experiences that mitigate travel planning uncertainties and attract specific user groups.Practical implicationsThe study demonstrates how VR can expand access to tourism for those unable to travel, enhance engagement and streamline planning. By leveraging VR in marketing, providers can create immersive, targeted content, reduce uncertainties, attract tech-savvy travelers and boost appeal through influencer partnerships.Originality/valueThis study pioneers the integration of Grey System Theory with traditional statistical methods to elucidate the dynamics of VR adoption in tourism. By uniquely combining ANOVA, regression models and dynamic grey relational analysis, it offers a precise model to interpret complex data, highlighting the nuanced effects of demographic factors on technology acceptance. This approach not only advances the analytical frameworks in tourism marketing but also provides critical insights into the demographic and psychological drivers of VR adoption.

Maximizing efficiency in C45 steel machining: an integrated AI-based approach to coated insert optimization

AbstractMachining involves the subtraction of the material from the sample workpiece to achieve the desired shape or surface. This versatile method is capable of producing a wide range of parts, varying from simple to intricate profiles. Coating materials are increasingly being utilized in tool inserts in the production industry owing to their superior thermal properties and wear resistance. The shielding of hard coatings, with thicknesses of only a few microns, enhances performance and durability. In this study, machining of C45 steel using distinct coated inserts was explored. The experimental trials employed PVD and CVD methods for coated tungsten carbide (WC) tools/inserts and PVD-coated cermet tools/inserts with different machining parameters. Performance metrics, such as the surface finish and reliability of the tool, were considered for the evaluation. The average tool life variation between the PVD-coated cermet and PVD-coated WC was 178.86%, and 30.11% between the PVD-coated cermet and CVD-coated WC at 1 mm DOC. ANOVA was performed using Response Surface Methodology to explore the influence of input parameters on output. The results indicate that the depth of machining and spindle speed significantly influence Ra, whereas spindle speed and type of tool insert have a considerable impact on the life span of the tool. The developed mathematical model for the prediction of tool life and Ra indicates its potential for performance forecasting during C45 steel machining. Grey relation analysis was employed to optimize the process parameters. Optimal results were achieved with a spindle speed of 400 m/min, 0.5 mm depth of cut, and cermet tool inserts. PVD-coated WC inserts performed better. ANFIS was applied for the prediction and optimization of the machining parameters.

Multi-objective optimization and influence degree analysis of the thermally integrated HP-ORC carnot battery based on the orthogonal design method and grey relational analysis

Thermally integrated heat pump (HP)-organic Rankine cycle (ORC) Carnot battery is a promising solution for efficient energy storage and waste heat utilization. Firstly, in order to research the influence degree of the operating parameters on the system performance, single-objective optimization is conducted by the orthogonal design method. Then, multi-objective optimization is conducted by combining the grey relational analysis (GRA) and orthogonal design method to assess the comprehensive performance of the system. The importance order and contribution rates of the operating parameters and the optimal operating combinations are determined. Finally, in order to explore the influence mechanism of the parameters on the system performance, the influence of the parameters on the subsystems and the subsystems on the whole system is quantitatively analyzed. The results show that thermal storage temperature difference, waste heat temperature difference and cold source temperature are the most important parameters affecting the comprehensive performance of the whole system. Under the optimal operating conditions, round-trip efficiency, exergy efficiency, energy storage capacity (ESC), energy storage density (ESD) and levelized cost of storage (LCOS) are 47.05 %, 22.66 %, 1943 kW, 2.90 kWh/m3 and 0.38$/kWh, respectively. Compared to the ORC subsystem, the HP subsystem is more important to round-trip efficiency and exergy efficiency.

Assessing Land Use Ecological-Social-Production Functions and Interrelationships from the Perspective of Multifunctional Landscape in a Transitional Zone between Qinghai-Tibet Plateau and Loess Plateau

Investigating the evolution and drivers of multifunctional land use is essential for sustainable land management and regional biological conservation. This research focuses on the Hehuang Valley, where we developed an “ecological-social-production” evaluation system for assessing land use multifunctionality from the perspective of multifunctional landscape. Leveraging Geographic Information System technologies, we conducted a quantitative analysis of spatiotemporal variations in multifunctional land use across the valley in recently twenty years. Correlation coefficients were employed to identify trade-offs and synergies among various land use functions. Additionally, geographical detector and grey relational analysis models were utilized to pinpoint the factors influencing spatiotemporal changes in land use functions during the specified period. The results showed that: (1) During the period, the overall multifunctionality of land use in the Hehuang Valley exhibited an increasing trend. The economic production function of the land showed the highest growth, while the ecological and social functions showed lower growth. (2) In most areas of the Hehuang Valley, there was a positive correlation between social and economic production functions and a negative correlation between social and ecological functions, as well as between economic production and ecological functions. (3) Natural conditions were the main factors of spatial variation of land use comprehensive functions, but human factors, including land use intensity and the rate of farmland conversion to non-agricultural uses, were the primary drivers of temporal changes in multifunctional land use. The findings provide valuable references and scientific support for policymakers in optimizing land use and multifunctional landscape conservation.

A combined method for the evaluation of contributing factors to maritime dangerous goods transport accidents

This paper evaluates the contributing factors to maritime dangerous goods (DG) transport accidents by integrating the Entropy Weight (EW) and Grey Relational Analysis (GRA) methods. For this purpose, investigation reports of maritime DG transport accidents that occurred worldwide between 2000 and 2023 are derived from the International Maritime Organization’s Integrated Shipping Information System (IMO GISIS) database’s Marine Casualties and Incidents (MCI) module. Eleven main ship operations and thirteen primary causes were selected by analysing accident investigation reports. The weights of main ship operations are calculated utilizing the EW method. The correlational degrees of the primary causes are then calculated using the GRA method. Most maritime DG transport accidents occur during unberthing, bunkering, and pilotage operations. The most common contributing factors of maritime DG transport accidents are collisions and occupational accidents. Specifically, maritime DG transport accidents are most likely to be caused by collisions during sailing, passage, maneuvering, and bunkering operations, as well as occupational accidents during cargo loading, anchoring, berthing, and mooring operations. The results of this paper can support stakeholders in developing the needed policies to guarantee the safety of maritime DG transport.

Enhanced Hybrid Congestion Mitigation Strategy for ‘6LoWPAN-RPL based patient-centric IoHT’

The Internet of Healthcare Things (IoHT) is rapidly evolving, providing new opportunities to enhance healthcare delivery. However, the resource limitation of connected medical sensing devices leads to congestion, resulting in reduced network performance, delayed data transmission, and loss of critical medical information, which can have significant consequences in healthcare. To address this issue, this paper proposes an Enhanced Hybrid Congestion Mitigation Strategy (EHCMS) for IPv6 over low-power wireless personal area networks (6LoWPAN) and routing protocol for low-Power and lossy networks (RPL) based patient-centric IoHT (PC-IoHT). The EHCMS combines several techniques, including traffic management, network topology optimization, and load balancing, to enhance network performance and reduce congestion. The proposed framework is a hybrid strategy that utilizes resource- and traffic-control mechanisms to alleviate congestion in the 6LoWPAN-RPL-based patient-centric IoHT network. For the resource-control-based approach, a congestion-aware composite objective function is designed using a few congestion-specific routing metrics and formulated as a multi-attribute decision-making problem solved using Grey relational analysis (GRA). In addition, a non-linear multi-criteria optimization problem-based transmission rate adaptation mechanism is contrived as a traffic-control scheme for congestion mitigation. The effectiveness of the proposed EHCMS is evaluated using simulations on the Cooja simulator in the Contiki-3.0 OS and compared with existing congestion-alleviating strategies. The results demonstrate that the proposed framework can significantly reduce congestion in the IoHT network, perform better than existing works, and improve the quality of service. This research paper contributes to the field of IoHT by proposing an effective congestion mitigation strategy that enhances the reliability and performance of the PC-IoHT network, ultimately improving the quality of healthcare delivery.

Optimization of process parameters in fabricating AA6082/Nip surface composite using friction stir processing technique

Abstract In this present study, an attempt was made to improve the Ultimate Tensile Strength (UTS) and Microhardness (MH) of friction stir-processed AA 6082 with Nip surface composites without affecting the ductility of the Base Metal (BM). Nip micron particle with an average size of 149 μm (99.99 purity) was utilized as reinforcement and AA6082 as a matrix for fabricating the surface composites. Process parameters namely rotational speed, transverse speed, and volume fraction with three levels considered as input parameters for this study. Moreover, ultimate tensile strength, microhardness, and wear rate were determined as process output. For this study, experiments were conducted as per Taguchi’s L9 orthogonal array (OA) by repeating each trial three times and the average values were used for further analysis. ANOVA was performed to ensure the influential process parameter and the results exhibited that the volume fraction of Nip exhibited the most influential parameter among the three process parameters. Additionally, Grey relational analysis (GRA) was used to grade the rank of the experimental samples using the Grey Relation Coefficient (GRC). Based on this method, the optimal process parameters (Rotational Speed = 1250 rpm, Transverse Speed = 40 mm min−1, Volume Fraction = 18%) were chosen and the optimal sample (Rank 1) attained a UTS of 247 MPa and MH of 100 HV. The results revealed that the fabricated composite (AA6082/Nip) demonstrated an improvement in UTS (26.02%), MH (56.25%), and wear rate (57.38%) as compared to BM (AA6082). Eventually, the surfaces of the optimized samples were also analyzed by x-ray diffraction (XRD), energy dispersive x-ray analysis (EDAX), and field emission-scanning electron microscopy (FE-SEM) methods to confirm the composition of surface composite and the uniform distribution of particles.

Enhanced FMEA Methodology for Evaluating Mobile Learning Platforms Using Grey Relational Analysis and Fuzzy AHP

This study addresses a significant problem: it is difficult to choose a suitable mobile learning platform effectively since many learning platforms are readily available for users. For this purpose, the study proposes an efficient way to rank and choose the most suitable mobile learning platform by integrating risk analysis and multi-criteria decision-making methods. The selection of a suitable mobile learning platform is challenging due to the vast collection of available platforms. Traditional decision-making approaches often struggle to manage the inherent uncertainty and subjectivity in platform evaluation. To address this, we propose an enhanced methodology that combines grey relational analysis (GRA) and fuzzy analytic hierarchy process (FAHP), leveraging their complementary strengths to provide a robust and adaptive solution. The study employs ISO/IEC 9126 software quality standards to locate the most suitable mobile learning platform. FMEA is based on three risk factors: occurrence, severity, and detection. The fuzzy analytical hierarchy process (FAHP) is used to determine the relative weight of each risk factor to identify the grey risk priority number that can be calculated for each criterion. Mobile learning platforms are then ranked based on their grey risk priority number. The method was applied to five widely used mobile learning platforms with three decision-makers. In addition, the multi-criteria decision-making software was developed to aid users, educators, and administrators in their decision-making processes. The integrated FMEA-GRA-FAHP technique, using ISO/IEC 9126 standards, provides an effective way of locating the most suitable mobile learning platform and ranking them according to their reliability. This research is believed to be the only study applying an integrated FMEA-GRA-FAHP approach to evaluate the risks and quality of mobile learning platforms. The unique approach overcomes certain limitations of the standalone methods such as FMEA and FAHP, making it a valuable tool for identifying the suitability of mobile learning platforms. In addition, the study underscores the importance of inclusivity and equity in ensuring high-quality education and creating an environment conducive to lifelong learning for all.

Evaluation and comparison for higher temperature performance index of emulsified asphalt mastic using coal gangue powder

In order to investigate the rheological behavior and the influence of solid waste coal gangue powder on the high-temperature performance of cold recycled Emulsified Asphalt Mastics (EAM), Dynamic Shear Rheometer (DSR) and Multiple Stress Creep Recovery (MSCR) tests were conducted using three different fillers and four powder-to-binder ratios of EAM. Evaluation indexes such as rutting factor (G*/sinδ), improved rutting factor (G*/1−1/sinδ∙tanδ), Unrecoverable Creep Compliance (Jnr), Cumulative Strain (CS) and Creep Recovery Ratio (R) were used to analyze the influence of filler type and dosage on high temperature of EAM, and the effectiveness of these indexes to evaluate high temperature is considered. Moreover, atomic force microscopy experiments were carried out to study on interaction mechanism between the fillers and emulsified asphalt. Finally, grey relational analysis method was used for quantitative assessment of these indexes for EAM. The results indicate that all three fillers effectively improve the high-temperature performance, with a larger improvement observed as the filler dosage increases, among which the tunneling coal gangue powder is the most significant. Under a stress level of 0.1 KPa, the coal gangue filler mastic appears the best deformation recovery ability, whereas under a stress level of 3.2 KPa, the cement filler mastics appears the best deformation recovery ability. The additional fillers significantly influence the nano-scale surface morphology of the residues, and the tunneling coal gangue powder appears a stronger adsorption capacity for free polar components compared to the cement and limestone mineral powder. It is suggested that the Jnr and CS can be used as evaluation indexes of high temperature evaluation index of EAM, instead of G*/1−1/sinδ∙tanδ or G*/sinδ or R.

Revealing dynamic goals for university’s sustainable development with a coupling exploration of SDGs

The sustainability of universities is important to realize global SDGs. However, there is a lack of research on the internal dynamic relationship of the SDGs in universities. This study aims to deep tap the dynamic mechanism and scientific core connotation of the relationship among the environmental, economic, and social dimensions in the sustainable development of the top 100 universities ranked by the Times Higher Education Impact Rankings, in order to point out the specific action direction in line with their characteristics. This study adopts coupling model, Sustainable Development Triangle model, and Grey Relational Analysis to learn the development system and the main development dynamic goals of universities on five continents. The results show that the development of the top sustainable universities in the five continents is very uneven with three echelons of development in the world. Although the sustainability quality in the world has improved year by year, the sub-quality of sustainability of the top universities on each continent is obviously different. The sustainable coupling degrees of the top universities of the five continents are increasing, but the overall coupling strength is not high. The development of coupling is at the level of weak coordination. SDG12 is the main dynamic goal for the top universities in Asia and America, SDG6 is for Europe and Oceania, and SDG3 is for Africa.

Exploring critical quality indicators and developing a non-destructive detection method using near-infrared spectroscopy for sea bass (Lateolabrax japonicus) quality evaluation

In this study, chemometrics were employed to explore the relationship between sensory evaluation and physicochemical indicators of sea bass (Lateolabrax japonicus). Through principal component analysis, cluster analysis, and Pearson correlation analysis, three pivotal indicators were identified: protein content, b* value, and condition factor. Leveraging the grey relational analysis, weights were assigned to these three core quality indicators, resulting in a comprehensive sea bass quality evaluation model: Y = 0.911 × protein (g/100 g) + 0.742 × b* + 0.747 × condition factor. Moreover, near-infrared spectroscopy combined with chemometrics were employed to evaluate the quality of sea bass. The different origins of sea bass were accurately distinguished using orthogonal partial least squares discriminant analysis. The partial least squares regression model was constructed for predicting the critical quality indicator, protein content, with R2P of 0.926. This study offers new insights for developing rapid, economical, and reliable methods for assessing aquatic product quality.

An Empirical Study on Industry Convergence in an Underdeveloped Area: Shanwei, Guangdong, China

The present paper studies the convergence of industry in Shanwei, a coastal city in Guandong province, China. The data originated from the Shanwei Statistical Yearbook 1990-2021. The Herfindahl-Hirschman Index is used to evaluate industry convergence. The Grey relational analysis assesses the contributions of the three industries’ added values to local GDP. The regression model verifies if the industry convergence is conducive to economic growth. The results first indicate that Shanwei City’s economy shows a significant increase in the proportions of the manufacturing and service sectors and a sharp decline in the ratio of the agricultural sector. Second, the service sector contributes the most to GDP, followed by the agricultural sector, with the manufacturing sector contributing the least. Third, industrial convergence is driving regional economic growth. This study’s theoretical contribution outlines the relationship between Shanwei City’s industrial convergence and regional economic development. The proposed policy recommendations are valuable for adjusting its industrial framework and promoting high-quality development.

Experimental Analysis of EDM Parameters on D2 Die Steel Using Nano-aluminum Composite Electrodes

This paper presents an experimental study on the electrical discharge machining (EDM) of AISI D2 die steel using an Al-Ni composite electrode. The investigation focuses on the influence of input parameters like current, pulse duration, and pulse interval, on key output parameters such as material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). EDM oil was employed as the dielectric fluid. Grey relational analysis (GRA) was utilized for designing and conducting the experiments using the Taguchi L9 method. The ANN model showed excellent predictive accuracy with a perfect correlation coefficient (R) of 1.00, indicating strong capability in forecasting MRR based on machining parameters. GRA further confirmed that higher current settings and longer pulse-off times effectively reduce tool wear, suggesting that the ANN model accurately reflects the conditions that minimize TWR. The ANN model achieved strong predictive accuracy for SR, with high correlation coefficients, although with slightly higher mean squared error (MSE) in testing.

Factors influencing the wetting performance of asphalt emulsion on RAP surface

The wetting performance of asphalt emulsion on reclaimed asphalt pavement (RAP) strongly affects the physical bonding ability of asphalt emulsion-RAP interface. For a more comprehensive understanding on the wetting performance of asphalt emulsion on RAP, many factors affecting the wetting performance of asphalt emulsion on RAP are studied, including the asphalt and emulsifier dosage in asphalt emulsion, RAP asphalt aged degree, and some additives (such as wetting agent, hydrated lime and silane coupling agent). The contact angle of asphalt emulsion on RAP asphalt surface and the other wetting parameters calculated by the surface wetting theory were used to comprehensively analyze the wetting performance. The results show that all of the tested factors have influence on the wetting performance of asphalt emulsions. The grey relational analysis indicates that the wetting agent, hydrated lime and silane coupling agent have significant effects on the wetting performance of asphalt emulsion on RAP. A suitable dosage of wetting agent, hydrated lime modified with silane coupling agent (HL-SCA) can decrease the contact angle and improve the wetting speed of asphalt emulsion on RAP. Meanwhile, the addition of both wetting agent and hydrated lime modified with silane coupling agent can greatly decrease the volume of air voids and improve the indirect tensile strength of cold recycled mixture with asphalt emulsion. Therefore, the wetting agent and the HL-SCA are recommended to improve the wetting performance of asphalt emulsion on RAP and the mechanical performance of cold recycled mixture with asphalt emulsion.

Multi-objective optimization of micro crystalline cellulose and montmorillonite filled poly lactic acid bio–composite and its characterizations

In this research study, the synthesis of poly lactic acid (PLA) based bio composite material factors contributions were investigated through the Taguchi-based grey relational analysis (GRA) technique. Effects of micro crystalline cellulose (MCC) and montmorillonite (MTT) nano clay filler, sorbitol (S) plasticizer, and temperature (T) operating factor on the PLA matrix through melt-mixing preparation method. The tensile strength (TS), Young modulus (YM), flexural strength (FS), flexural modulus (FM), hardness, impact strength (IS), water absorption (WA), and density properties of bio composite material were investigated for each experimental setup (orthogonal array, L16). Additionally, neat PLA and optimal sample structural, thermal, and morphological properties were examined through Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (X-RD), thermal gravimetry analysis/differential thermal gravimetry (TGA/DTG), and DSC and SEM analyses. The obtained result for optimal mechanical and physical properties of MCC/MTT/S/PLA bio composite was MCC at level 3 (6%), MTT at level 4 (9%), S at level 2 (10%), and T at level 4 (175 °C). Analysis of variance (ANOVA) shows that MTT has the greatest significant effect on mechanical and physical properties of MCC/MTT/S/PLA bio composite followed by MCC, T, and S. The confirmation test indicates that the improvement of weighted grey relational grade (GRG) from 0.7896 to 0.846 and the FTIR, XRD, thermal gravimetry/differential scanning calorimetry (TG/DSC), and scanning electron microscopy (SEM) results indicates that the good interaction between PLA and fillers, improvement of thermal and morphological properties of optimal (6MCC/9MTT/10S/175 °C) bio composite samples. Therefore, the multi-response characteristics of MCC/MTT/S/PLA bio composite can be highly improved by this technique.

An adaptive photovoltaic power interval prediction based on multi-objective optimization

Photovoltaic (PV) power interval prediction can provide a variation range of prediction results, which is of great significance to promoting the optimization of power grid dispatching and maintaining the stability of the power system. However, the existing PV interval prediction fails to accurately reflect error fluctuations in their construction process due to dependency on data distribution assumptions and unreasonable estimation of prediction error standard deviations (PEStds). To address these issues, we propose an adaptive interval prediction method based on multi-objective optimization. Firstly, K-means clustering is used to segment similar days. Subsequently, Convolutional Neural Network-Gated Recurrent Unit-Attention (CNN-GRU-Attention) is utilized for point prediction. Furthermore, Grey Relation Analysis (GRA) is used to screen dates similar to the target date under the same weather type, enabling a more precise estimation of prediction error fluctuations. Ultimately, the estimated values are multiplied by non-fixed parameters to construct intervals, which avoids the dependency on data distribution assumptions. Among them, the optimal values for non-fixed parameters are obtained through multi-objective optimization considering interval coverage probability, interval width, and deviation. To verify the effectiveness of the proposed model, we conduct comparative experiments on two datasets with different resolutions. The results demonstrate that the proposed model offers more flexible and higher-quality intervals. Not only does this research improve the accuracy of PV power generation interval prediction, but it also helps to promote the development of smart grid technology and improve the adaptive ability of power systems facing dynamic environments and complex data, which has an important impact on the future energy management and power market.

Optimization and characterization of boric acid powder mixed electrical discharge machining of Ti-6Al-4V ELI

The powder mixed electrical discharge machining (PMEDM) process is used for enhancing machining efficiency and improving the surface characteristics of the workpiece compared to normal EDM. The present study evaluates the effect of boric acid powder mixed in deionized water in PMEDM of Ti-6Al-4V ELI (extra low interstitial). The PMEDM process is assessed by evaluating material removal rate (MRR) and surface roughness (SR) at varying powder concentration (PC), current and pulse duration ( Ton). Boric acid PMEDM exhibits advantageous for both MRR and SR, where MRR increases and SR decreases with an increase in PC. To simultaneously achieve higher MRR and lower SR, the grey-fuzzy logic method is employed. Initially, the raw input data is normalized into grey numbers and a single index is obtained through grey relational analysis. Subsequently, triangular membership functions are assigned to the inputs and output. Fuzzy logic rules are then formulated, and grey fuzzy relational grade (GFRG) is calculated for each condition. The optimum condition (GFRG = 0.787) is attained at 15 g/l PC, 9 A current and 106 µs Ton, resulting in a 63% improvement compared to the initial condition. At the optimum condition, MRR increased by 145% and SR decreased by 29% compared to the initial condition. The effect of boric acid in PMEDM can be visualized through the formation of smooth surfaces, exhibiting reduced SR, smaller and shallower craters, and fewer debris and globules. Additionally, the boric acid PMEDM experiments resulted in the formation of a hard TiB phase on Ti-6Al-4V, thus improving its wear resistance.