Prediction of ash-deformation temperature based on grey-wolf algorithm and support-vector machine

Xiao, Haiping; Chen, Yuhui; Dou, Chaozong; Yu, Ruihang; Cai, Li; Chun, Zhang; Zhang, Kang; Sun, Bin

doi:10.1016/j.fuel.2018.12.064

Cited by 16 publications

(6 citation statements)

References 6 publications

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“…The optimization process of the above models is shown in In addition, the parameters (c, g) of SVM are directly related to the final modeling accuracy, so some optimization algorithms were applied to the SVM model (Xiao et al, 2019;Zhao et al, 2019). Such as Liang et al (2019) used GWO and PSO to optimize SVM, and achieved encouraging results.…”

Section: Selection Of Characteristic Wavelengthsmentioning

confidence: 99%

Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

Wang

Sun

et al. 2022

J Food Process Engineering

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To identify the varieties of red jujube rapidly and nondestructively, hyperspectral imaging (HSI) technology was applied in this article. Hyperspectral data of 480 samples with four different varieties were acquired in the range of 400.68-1001.61 nm.First, Savitzky-Golay and standard normal variable were utilized to process raw spectra. Afterward, a novel method combining competitive adaptive reweighted sampling and iterative retained information variable (CARS-IRIV) was proposed to select feature wavelengths. The support vector machine (SVM) modeling results indicated that CARS-IRIV had better information extraction performance and simplified the model.Finally, to further improve the accuracy, sparrow search algorithm (SSA) was adopted to optimize the parameters (c, g) of SVM. The results showed that SSA-SVM exhibited greater accuracy than other compared models, and the accuracy of training and test sets were 100% and 96.68%, respectively. It confirmed that HSI technology coupled with CARS-IRIV-SSA-SVM can effectively identify varieties of red jujube. Practical ApplicationsThe traditional ways of classifying red jujube varieties are destructive and laborious. Therefore, HSI technology was adopted to overcome the above shortcomings. In this article, the best identification performance was based on the CARS-IRIV-SSA-SVM model with an identification accuracy of 96.68%. This study is helpful for the identification of other agricultural product varieties by HSI technology.

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Section: Selection Of Characteristic Wavelengthsmentioning

confidence: 99%

Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

Wang

Sun

et al. 2022

J Food Process Engineering

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“…Another direction of research is the use of machine learning methods to predict AFTs [11,[20][21][22][23][24][25][26][27][28][29]. The most frequently techniques used were artificial neural networks (ANNs) and support vector machine (SVM).…”

Section: Standard:iso Pn-iso 540:2001 Descriptionmentioning

confidence: 99%

“…The parameters of SVM were optimized by the improved ant colony algorithm. Recently, Xiao et al [26] have used the SVM model optimized by the grey-wolf algorithm. The purpose of the study was to predict IDT and investigate how SO 3 as an input parameter affects prediction performance.…”

Section: Standard:iso Pn-iso 540:2001 Descriptionmentioning

confidence: 99%

An Interpretable Extreme Gradient Boosting Model to Predict Ash Fusion Temperatures

2020

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The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or to be able to predict value of this parameter. In this study a non-linear model predicting the HT value, based on ash oxides content for 360 coal samples from the Upper Silesian Coal Basin, was developed. The proposed model was established using the machine learning method—extreme gradient boosting (XGBoost) regressor. An important feature of models based on the XGBoost algorithm is the ability to determine the impact of individual input parameters on the predicted value using the feature importance (FI) technique. This method allowed the determination of ash oxides having the greatest impact on the projected HT. Then, the partial dependence plots (PDP) technique was used to visualize the effect of individual oxides on the predicted value. The results indicate that proposed model could estimate value of HT with high accuracy. The coefficient of determination (R2) of the prediction has reached satisfactory value of 0.88.

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“…Tambe et al 24 used a computational intelligence model to estimate the coal ash melting temperature and obtained good results. Using the GWOSVM model, Xiao et al 25 predicted the DT of coal ash and indicated that the model has a specific prediction accuracy. Tillman and Duong 26 examined the problem of slagging at Michigan’s Monroe Power Plant.…”

Section: Introductionmentioning

confidence: 99%

Effects of Proximate Analysis on Coal Ash Fusion Temperatures: An Application of Artificial Neural Network

Onifade,

Lawal,

Bada

et al. 2023

ACS Omega

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The temperature at which coal ash melts has a significant impact on the operation of a coal-fired boiler. The coal ash fusion temperature (AFT) is determined by its chemical composition, although the relationship between the two varies. Therefore, it is important to have mathematical models that can reliably predict the coal AFTs when designing coal-based processes based on their coal ash chemistry and proximate analysis. A computational intelligence model based on the interrelationships between coal properties and AFTs was used to predict the AFTs of the coal investigated. A model that integrates the ash, volatile matter, fixed carbon contents, and ash chemistry as input and the AFT [softening temperature, deformation temperature, hemispherical temperature, and flow temperature] as an output provided the best indicators to predict AFTs. The findings from the models indicate (a) a method for determining the AFTs from the coal properties; (b) a reliable technique to calculate the AFTs by varying the proximate analysis; and (c) a better understanding of the impact, significance, and interactions of coal properties regarding the thermal properties of coal ash. This study creates a predictive model that is easy to use, computer-efficient, and highly accurate in predicting coal AFTs based on their ash chemistry and proximate analysis data.

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Prediction of ash-deformation temperature based on grey-wolf algorithm and support-vector machine

Cited by 16 publications

References 6 publications

Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

An Interpretable Extreme Gradient Boosting Model to Predict Ash Fusion Temperatures

Effects of Proximate Analysis on Coal Ash Fusion Temperatures: An Application of Artificial Neural Network

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