Grey Relational Analysis Research Articles

Prediction of Coal Ash Flow Temperature Based on Gray Relational Analysis, Support Vector Regression and Genetic Algorithm.

Coal ash flow temperature significantly influences the operating conditions of entrained flow bed gasification. The relationship between the flow temperature of coal ash and its chemical composition remains uncertain despite being determined by it. To construct a reliable and accurate predictive method, machine learning models were used, and different support vector regression models were built to predict the flow temperature. The prediction results of the proposed gray relational analysis-genetic algorithm-support vector regression model can achieve high accuracy, with a root-mean-square error of 28.37, mean absolute error of 19.48K, and average deviation of 1.58%. Moreover, the prediction results of the proposed model are more accurate and efficient than those calculated by FactSage software, with a mean absolute error of 93.73K. This demonstrates the viability of applying the proposed machine learning model to predict the flow temperature of coal ash and its promising potential application in the area of coal chemical engineering.

The Nearshore Marine of ShenZhen Environmental Risk Assessment Method Based on the DPSIR Framework

The nearshore marine areas are the most intensely concentrated regions for human economic activities and serve as vital carriers for the development of the marine economy. Accurately assessing the environmental risks in these areas is of significant importance for the sustainable development of the marine economy. This study constructs an environmental risk assessment system for the nearshore marine areas of Shenzhen City based on the DPSIR framework and evaluates it using the comprehensive index method. Specifically, the study begins by selecting 27 indicators related to the marine environment to construct the evaluation index system. It employs the grey relational analysis and entropy weight method to weight the indicators and finally assesses the environmental risks of Shenzhen's nearshore marine areas from 2013 to 2020 using the comprehensive index method. The study finds that the environmental risks of Shenzhen's nearshore marine areas from 2013 to 2020 are generally low, with the risk score increasing from 0.3277 to 0.7308 year by year, shifting from a more dangerous to a safe risk level. The research indicates that the marine environment of the nearshore areas in Shenzhen is greatly influenced by local human economic activities and the industrial structure of Shenzhen. The study suggests that the environmental safety level of Shenzhen's nearshore marine areas could be improved by leveraging policy guidance, optimizing the industrial structure, and perfecting tourism management methods.

Mechanical characteristics of optimized alkali-treated Similax Zelanica/glass fiber/nanosilica composites in an epoxy matrix: An experimental investigation and numerical study

The demand for environmentally conscious materials has led the research on natural fiber composites as an alternative to synthetic materials in various industries. This study focuses on the optimization and preparation of alkali-treated Similax Zelanica/glass fiber and nanosilica-reinforced epoxy composites. The weight % of Similax Zelanica/glass and nanosilica was optimized using the grey relational analysis (GRA) multiparameter optimization technique and DOE of L9 orthogonal was used. Mechanical properties including tensile, flexural, impact, rock well hardness, and dynamic mechanical analysis were evaluated. Results indicate that increasing fiber volume fraction up to 30% enhances mechanical properties, with subsequent declines beyond this threshold. Tensile strength peaked at 30% fiber volume (75 MPa), while flexural strength also peaked at 30% substrate (140 MPa). The impact test showed a maximum of 1.84 kJ/m2 at 30% volume fraction. Maximum hardness of 85 RHN is observed for 30% S and 60% E specimens. Weibull distribution plots results, aligned well with the expected distribution pattern, indicating consistent and reliable mechanical behavior. Water absorption rates increase with fiber volume percentage increase, but optimal alkali treatment improves, absorption resistance to some extent. Dynamic mechanical analysis reveals reduced glass transition temperature Tg (97° C) for composite due to their better interaction nature between fiber/silica nanoparticles and matrix. Further characterization reveals thermal stability (374°C), crystalline properties (crystalline index: 56.87%, crystalline size: 21.23 nm), and functional group composition. Numerical analysis using ANSYS validates experimental results, giving confidence in repeatability. Scanning electron microscope analysis confirms good interfacial bonding, crack propagation details, and absence of impurities in the composite. These results like Mechanical properties, thermal stability, crystalline properties, and functional group composition of the composite confirm its suitability for various applications.

Multi-response optimization and effect of process parameters during machining of inconel X-750 using grey relational analysis and analysis of variance

Multi-response optimization and effect of process parameters during machining of inconel X-750 using grey relational analysis and analysis of variance

Induced Generalized Intuitionistic Fuzzy OWA Operator on GRA Method for Evaluation of Self-Propelled Artillery System: Ammunition Based Computer Assisted Military Experiment

The Self-Propelled Artillery System selection problem, which is a sub-problem of the Weapon Systems Selection Problems (WSSP), is an extremely important strategic level decision problem and constitutes the main focus of this study. To solve the problem, 3 different weapon systems were evaluated based on 7 criteria. The solution methodology consists of the integration of induced generalized ordered weighted averaging (IG-IFOWA) operator and Grey Relational Analysis (GRA). Such a solution proposal has not been seen in previous WSSP applications. To test the validity and applicability of the proposed methodology, the results of the study were tested on an ammunition-based computer-aided military experiment. The results reveal the effectiveness of the selected weapon system.

Development of high-strength geopolymer mortar based on fly ash-slag: Correlational analysis of microstructural and mechanical properties and environmental assessment

Given the insufficient mechanical performance of existing fly ash-based geopolymer mortars under ambient curing conditions, this study is dedicated to developing a high-strength geopolymer mortar that meets the requirements of modern structural engineering. Based on the rheological performance tests of the fresh geopolymer mortar, the evolution of its rheological characteristics was revealed. Through macroscopic mechanical performance tests, the influence of slag content on the compressive and flexural strength of geopolymer mortar was clarified. Employing microstructural performance testing methods such as Thermogravimetric and Mercury Intrusion Porosimetry, the micro-mechanisms behind the improvement of mechanical properties were revealed. Utilizing the grey relational analysis method, a framework was established to analyze the correlation between the microstructural and macroscopic mechanical properties of geopolymer mortar. Additionally, an assessment system for evaluating environmental and economic benefits was developed using statistical analysis methods. The results show that increasing slag content enhances the yield stress and plastic viscosity of geopolymer mortar. Optimizing slag content boosts the compressive strength of fly ash-based geopolymer mortar, reaching up to 88.13 MPa under ambient curing conditions. An optimal amount of slag also promotes gel formation during polymerization, improving the internal pore structure. Furthermore, the content of reaction products correlates more strongly with macroscopic mechanical properties than pore structure characteristics. Geopolymer mortar also presents significant environmental and economic advantages over traditional cement-based materials, with its carbon and economic intensity indices at only 5.50 % and 10.80 %, respectively, of those in cement-based materials.

Research on the operational capital management performance of real estate enterprises under the background of high-quality developmen

Taking Vanke Real Estate as the research object, this study uses entropy weight method and grey relational analysis to quantitatively evaluate its working capital management performance from three dimensions: profitability, riskiness, and liquidity. The results show that during the period from 2021 to 2023, Vanke ranks behind Poly in terms of comprehensive grey relational degree ranking, indicating inferior working capital management performance compared to Poly. Based on the above analysis results, this study provides insightful suggestions for optimizing the working capital management strategies of real estate companies. It serves as a theoretical reference for improving the working capital management performance of real estate enterprises and promoting their high-quality development.

Prediction of HPC compressive strength based on machine learning

There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research.

Reliability and sensitivity analyses of monopile supported offshore wind turbines based on probability density evolution method with pre-screening of controlling parameters

To reasonably investigate the influence of pile-soil interaction on the dynamic reliability of monopile supported offshore wind turbines (OWTs), a framework with high efficiency and good accuracy is proposed for reliability and sensitivity analyses of monopile supported OWTs. This proposed framework combines the grey relational analysis (GRA), the probability density evolution method (PDEM), and the interval mathematical analysis method (IM). Firstly, the GRA is applied to identify the main controlling parameters influencing the stochastic dynamic response of a monopile supported OWT, considering various factors such as pile elasticity modulus, soil internal friction angle and soil unit weight. Secondly, combining the probability density evolution theory, a complete sample probability set is established to obtain the dynamic reliability of the monopile supported OWT under wind and wave loads. Finally, the effect of each controlling parameter on the dynamic reliability of the monopile supported OWT is systematically analyzed by combining the IM and the change of probability measure. The proposed GRA-PDEM-IM-based framework for reliability analyses of monopile supported OWTs can effectively reduce the number of samples for probability density evolution calculations and provide reasonable and accurate reliability analyses results which can serve as a reference for design and construction of monopile supported OWTs.

Experimental investigation and optimization of the effects of manufacturing parameters on geometric tolerances in additive manufacturing of AlSi10Mg alloy

While the quality of parts produced by additive manufacturing is generally evaluated by surface roughness, relative density, and mechanical properties, the issue of dimensional accuracy is not examined sufficiently. However, dimensional accuracy is very important for the final use and finishing of a product. Since the dimensional change mainly occurs due to shrinkage resulting from the heat energy applied during the sintering process, the effect of production parameters in the additive manufacturing method is quite large. To minimize shrinkage and increase dimensional accuracy, manufacturing parameters need to be optimized and meticulously examined. This study was aimed at determining the effects of manufacturing parameters on geometric tolerances in the production of parts using the additive manufacturing method. AlSi10Mg powder alloy and selective laser melting (SLM) technology were used in the additive manufacturing of this alloy in part production. Twelve different laser powers and scanning speeds, as well as fixed scanning range and layer thickness parameters, were used in production. In determining geometric tolerances, features such as hole diameter change, deviation from angularity, deviation from perpendicularity, deviation from flatness, and deviation from parallelism were taken into consideration. As a result of the study, deviation values increased in high and low laser power/scanning speed combinations. Minimum deviation amounts were obtained in the range of 250–310 laser power and 785–974 scanning speed, which are the middle values of the parameters used. The optimum values of different output responses have been obtained with different production parameters, but for the final use and quality control approval of the product, it is necessary to determine the input parameters at which all output responses are optimal. In this process, the gray relational analysis optimization method, which is one of the multi-criteria decision-making methods, was preferred. As a result of the optimization, the optimum manufacturing parameters for geometric tolerances were determined as the 290/911 laser power/scanning speed combination.

Research on identification, classification, and policy development for urban shrinkage: A case study of Liaoning Province, China

Under the influence of economic globalization and the transformation of pillar industries, cities are facing phenomena such as slow economic growth and population outflow, which indicate urban shrinkage. However, the theoretical framework for this phenomenon remains incomplete, and measurement standards are inconsistent. Therefore, studying urban shrinkage is of significant importance. This paper analyzes the current status and causes of urban shrinkage in Liaoning Province, China, and proposes an accurate model for identifying shrinkage. It utilizes a two-step diagnostic method that incorporates both one-dimensional and multidimensional approaches to identify shrinking cities and applies systematic clustering methods to categorize urban population changes. By observing and analyzing the population and economic data of 30 cities over 12 years, this study assesses urban shrinkage from the perspectives of population change and multivariate indicators. It identifies five cities with significant shrinkage, categorized into mild, moderate, and severe levels. Based on multiple linear regression and grey relational analysis, the study examines the impact of socio-economic factors on population changes and offers corresponding development suggestions.

Heavy metal removal performance of capacitive deionization technology studied by machine learning

Capacitive deionization (CDI) technology is utilized for efficient treatment of industrial wastewater, characterized by low energy consumption and environmental protection. In order to comprehend the correlation between key experimental parameters and the electrosorption capacity (EC) of heavy metals in CDI technology, this paper employs a genetic algorithm (GA) to optimize a backpropagation artificial neural network (BPANN) for predicting the EC of CDI technology for heavy metal ions, with the characteristics of electrode materials converted into numerical characteristics for further analysis. Compared to the BPANN, the optimized GABPANN model demonstrates superior predictive accuracy. It achieves automatic adjustment of the hidden layer structure, neuron count, and transfer functions. Furthermore, the grey relational analysis indicates that the electrode material and the initial pH value of the solution are pivotal in determining the EC of heavy metal ions. This underscores the efficacy of machine learning (ML) algorithms in forecasting the nonlinear dynamics of CDI systems and elucidates the influence of individual parameters on the efficacy of heavy metal removal.

A Comprehensive Approach to Biodiesel Blend Selection Using GRA-TOPSIS: A Case Study of Waste Cooking Oils in Egypt

The transition to sustainable energy sources is critical for addressing global environmental challenges. In 2017, Egypt produced about 500,000 tons of waste cooking oil from various sources including food industries, restaurants and hotels. Sadly, 90% of households choose to dispose of their used cooking oil by pouring it down the drain or into their village’s sewers instead of using proper disposal methods. The process involves converting waste cooking oil (WCO) into biodiesel.This study introduces a multi-criteria decision-making approach to identify the optimal biodiesel blend from waste cooking oils in Egypt. By leveraging the grey relational analysis (GRA) combined with the technique for order preference by similarity to the ideal solution (TOPSIS), we evaluate eight biodiesel blends (diesel, B5, B10, B20, B30, B50, B75, B100) against various performance metrics, including carbon monoxide, carbon dioxide, nitrogen oxides, hydrocarbons, particulate matter, engine power, fuel consumption, engine noise, and exhaust gas temperature. The experimental analysis used a single-cylinder, constant-speed, direct-injection eight cylinder diesel engine under varying load conditions. Our methodology involved feature engineering and model building to enhance predictive accuracy. The results demonstrated significant improvements in monitoring accuracy, with diesel, B5, and B20 emerging as the top-performing blends. Notably, the B5 blend showed the best overall performance, balancing efficiency and emissions. This study highlights the potential of integrating advanced AI-driven decision-making frameworks into biodiesel blend selection, promoting cleaner energy solutions and optimizing engine performance. Our findings underscore the substantial benefits of waste cooking oils for biodiesel production, contributing to environmental sustainability and energy efficiency.

Parametric Analysis and Optimization on Fiber Laser Micro-Milling on AZ31 Magnesium Alloy

One of the proficient machining technologies to create micro-features in a variety of materials is the laser micro-milling process. This procedure involves irradiating a highly concentrated laser beam on the sample and performing laser cutting at various cutting patterns to change the surface conditions and enhance the characteristics of that surface tribology. In this work, an effort has been made to mill magnesium alloy (AZ31) material using a laser. The pulse frequency, laser beam power, cutting speed, number of passes, and transverse feed are among the different process variables taken into account for the current research effort. Measured results include depth of cut and surface roughness. To ensure the validity of the established empirical models, this paper also presents experimental data. Several surface plots have been used to examine the test data. To get the lowest values of surface roughness and depth of cut, multiperformance optimization is also utilized. The influence of process factors on response has also been examined using optical microscopic images. Gray relational analysis approach is also applied to obtain the multiobjective optimization parametric combination and based on the calculated results of gray relational grade, it is revealed that improvement in the value of gray relational grade at predictive setting is 0.054 compared to the initial parametric combination.

Comprehensive Evaluation of the Development Level of China’s Characteristic Towns under the Perspective of an Urban–Rural Integration Development Strategy

With the advancement of urbanization and the continuous deepening of reforms in urban–rural systems, China’s urbanization process has entered a new era of integrated urban–rural integration. Currently, as a global “new green revolution” gains momentum, numerous countries are deeply integrating the concept of sustainable development into new urban planning. Against this backdrop, urban planners worldwide are committed to building green, livable, and smart cities that can meet the needs of the present generation without compromising the ability of future generations to meet their needs, thus achieving the vision of harmonious coexistence between humanity and nature. Characteristic towns, leveraging their resource advantages, play a significant role in achieving sustainable regional economic development. They serve as valuable references for China’s urban transformation and upgrading, as well as for promoting rural urbanization, and are crucial avenues for advancing China’s urban–rural integration development strategy. The evaluation of the development level of characteristic towns is a necessary step in their progress and a strong guarantee for promoting their construction and development. Therefore, effectively evaluating the social benefits of characteristic towns is paramount. This study constructs an evaluation model based on the grey rough set theory and Technique for Order Preference by Similarity to Ideal Solution of TOPSIS. Firstly, an evaluation index system for the development level of characteristic towns is established. Then, the grey relational analysis method and rough set theory are used to reduce the index attributes, while the conditional information entropy theory is introduced to determine the weights of the reduced indicators. Finally, the TOPSIS model is applied to evaluate the development level of characteristic towns. Through empirical research, eight characteristic towns in Zhejiang Province, China, were assessed and ranked, verifying the effectiveness and feasibility of the proposed model.

Multi-Criteria Model for Identifying and Ranking Risky Types of Maritime Accidents Using Integrated Ordinal Priority Approach and Grey Relational Analysis Approach

Accidents in marine operations are occurring consistently despite government safety initiatives and efforts to lower the number of accidents and the ensuing human casualties. Since each accident type has a different frequency and casualty rate, identifying risky accident types is important to determine the priority for taking necessary risk reduction actions. Usually, a risk is calculated using two criteria, i.e., the frequency and fatality of an accident. However, the accident statistics show that for the last 5 years from 2018 to 2022, the injury rate is more than three times the death rate in maritime accidents in Korean waters. Considering the importance of injury, unlike other previous studies, we perform a risk analysis with three criteria, i.e., frequency, death, and injury to complement the conventional risk calculation methods, which can help decision-makers allocate the limited resources to the riskiest types of accidents in order of priority. In doing so, we employed an integrated ordinal priority approach (OPA) and grey relational analysis (GRA) method to assign proper weight to each criterion and rank eight accident types. We categorized the accidents types into three different groups where safety accidents and collisions were ranked as the most dangerous types. The combined OPA and GRA technique has been effectively applied to other risky industries, as well as the maritime industry. Additionally, the proposed method is suitable for multi-criteria models when each criterion has a different importance. Finally, the method can be integrated into the framework of the risk ranking process to enhance the analysis results.

Investigation of dry sliding wear characteristics of Al 7075 reinforced with SiC-graphite hybrid composites: multi-parameter optimization using grey relational analysis

Investigation of dry sliding wear characteristics of Al 7075 reinforced with SiC-graphite hybrid composites: multi-parameter optimization using grey relational analysis

A new failure analysis and validation scheme for shale gas pilot processes

Shale gas pilot processes are usually composed of skid-mounted assemblies of individual equipment, with weak process matching, unknown unit equipment wear, and high failure rates. We propose a solution using the grey relational analysis model, analytical hierarchy process model, and reliability analysis model to achieve failure analysis, prediction, and improvement validation, respectively. The probability and degree of influence relationships between the factors affecting the occurrence of failures are discussed. The practical application of the study area shows that: when the threshold value is set to 0.8833, it can match the grey correlation conclusion well, connect the logic of hierarchical analysis, and cluster the engineering problems; this study area needs to focus on preventing the problems of erosion damages and puncture leakage. Although the short life of the nozzle has a large impact on the overall process, but the frequency of occurrence is not high, the next step needs to continue to collect data, using other mathematical models to mine the fault data internal information.

Gray Relation Analysis of Human-caused Accidents in the Oil Industry

Gray Relation Analysis of Human-caused Accidents in the Oil Industry

Water and fertilizer decision model for synergistic optimization of yield, quality and efficiency of cherry tomato based on combination evaluation

The scientific nutrient supply under deficit irrigation can ensure the yield and quality of crops, and balance water and fertilizer benefits. The model of the combination of water and fertilizer was constructed based on the analysis of the effects of irrigation, K2O and MgO application on yield, quality and water and fertilizer use efficiency of cherry tomato over two growing seasons. Deficit irrigation contributed to quality accumulation, while full irrigation resulted in higher yields. The optimal irrigation water use efficiency (IWUE) was attained with a moderate level of potassium and magnesium. An evaluation system of cherry tomato was established containing totally 6 indicators from 3 categories of yield, quality and efficiency based on correlation analysis, and yield obtained the highest weight of 0.402 and 0.373 in the two study seasons, respectively. The overall differential combination model was adopted to comprehensively evaluated combination of water and fertilizer factors by integrating three single models of TOPSIS, Membership function analysis and Grey relational analysis, and the correlation coefficient of any two single model was greater than 0.614. The response of comprehensive score for cherry tomato to each single factor of irrigation, K2O and MgO followed a downward-opening parabola. The analytical model revealed that the coupling of potassium and magnesium has a mutually reinforcing effect when they were both at medium level, while an inhibitory effect was observed at higher or lower level. The model verification showed that 7 out of the total 10 indicators reached more than 80 % of maximum value of the corresponding single indicator under the condition of the water and fertilizer with highest comprehensive score. Considering yield, quality and efficiency, the optimal combination of irrigation, K2O, and MgO was 80.6 ∼ 83.2 % Evaporation (Ep), 161.40 ∼ 171.27 kg/ha, and 19.59 ∼ 20.17 kg/ha in 2021S. The corresponding combination for 2021F was 75.4 ∼ 77.9 % Ep, 133.41 ∼ 140.45 kg/ha, and 13.99 ∼ 14.80 kg/ha. This strategy can maintain yield, improve quality, save irrigation amount, and increase fertilizer efficiency, which guides the production of deficient irrigation in northwest China.