A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance

Nieto, P.J. Garcı́a; García–Gonzalo, Esperanza; Anton, J.C.; Suárez, Víctor Manuel González; Bayón, Ricardo Mayo; Martín, Fátima

doi:10.1016/j.cam.2017.02.031

Cited by 50 publications

(10 citation statements)

References 30 publications

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“…MARS is used to try to identify and automatically establish the possible explicit regression equation between the regressors and the dependent variables in a stepwise manner; another important advantage of the MARS model is its abilities to provide the part of the contribution of each predictor to the dependent variable, and at the end of the training procedure it provides the final rankings of the regressors individually based on its rank [44]. A wide range of applications of the MARS model can be found in the literature including: estimating heating load in buildings [62,63], predicting centerline segregation in steel cast products [64], predictions of landslide susceptibility [65], estimating fractional snow cover (FSC) from MODIS data [66], and predicting monthly discharge and mean soil temperature [22,62]. Using the MARS model, the space of regressors is divided into several subspaces called knots, each has its own function and splines (segments) which are used to link all these knots, and all the spline are grouped to form a basis function (BF).…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

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The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

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Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

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“…In addition, this type of kernel is robust against adversarial noise and in predictions. However, it has more limitations than neural networks [27,28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The operation x.y is dot product between the two vectors. The application of Sigmoid kernels is similar to that of RBFs and depends on the chosen level of cross-validation [27,28]. It is an appropriate kernel to use particularly for nonlinear classification in two dimensions or when the number of dimensions is high.…”

Section: Literature Reviewmentioning

confidence: 99%

PFW: Polygonal Fuzzy Weighted—An SVM Kernel for the Classification of Overlapping Data Groups

et al. 2020

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Support vector machines are supervised learning models which are capable of classifying data and measuring regression by means of a learning algorithm. If data are linearly separable, a conventional linear kernel is used to classify them. Otherwise, the data are normally first transformed from input space to feature space, and then they are classified. However, carrying out this transformation is not always practical, and the process itself increases the cost of training and prediction. To address these problems, this paper puts forward an SVM kernel, called polygonal fuzzy weighted or PFW, which effectively classifies data without space transformation, even if the groups in question are not linearly separable and have overlapping areas. This kernel is based on Gaussian data distribution, standard deviation, the three-sigma rule and a polygonal fuzzy membership function. A comparison of our PFW, radial basis function (RBF) and conventional linear kernels in identical experimental conditions shows that PFW produces a minimum of 26% higher classification accuracy compared with the linear kernel, and it outperforms the RBF kernel in two-thirds of class labels, by a minimum of 3%. Moreover, Since PFW runs within the original feature space, it involves no additional computational cost.

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“…% X 5 Sulfur content in molten steel wt. % X 6 Secondary cooling water flow rate in zone1 L/min X 7 Secondary cooling water flow rate in zone2 L/min X 8 Secondary cooling water flow rate in zone3 L/min X 9 Secondary cooling intensity L/kg X 10 Pouring temperature • C X 11 Casting speed m/min X 12 Superheat • C X 13 Mold water flow rate L/min X 14 Mold water temperature difference • C Figure 2. Schematic diagrams of sampling (a) and drilling (b) in the steel billets.…”

Section: Experimental Datamentioning

confidence: 99%

“…García et al [10] built a segregation prediction model of slab based on multivariate adaptive regression splines technique, and the results showed good agreement with the actual values. Besides, to predict the centerline segregation in continuous cast steel slabs, a hybrid algorithm was established based on support vector machine in combination with the particle swarm optimization algorithm [11]. Normanton et al [12] developed a quality prediction system of steel casting, which was a combination of several artificial intelligence techniques, such as artificial neural networks, multi-layer perceptron nets, and self-organizing maps and other database methods.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

Zou

Zhang

Liu

et al. 2019

Metals

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Central carbon segregation is a typical internal defect of continuous cast steel billets. Real-time and accurate carbon segregation prediction is of great significance for lean control of the production quality in continuous casting processes. In this paper, a data-driven regularized extreme learning machine (R-ELM) model is proposed for the prediction of carbon segregation index (CSI). To improve model performance, outliers in industrial data were eliminated by means of boxplot tool. Besides, Pearson correlation combined with grey relational analysis (GRA) was conducted to avoid multicollinearity and redundancy in input variables. The new model shows potential to evaluate online quality of steel billets. When predictive errors were within ±0.03 and ±0.025, the prediction accuracy of the R-ELM model was 94% and 89%, respectively, which was higher than that of the multiple linear regression (MLR) model and ELM model. Moreover, the effects of several key continuous casting process parameters on CSI were investigated based on the predictions of the R-ELM model via response surface analysis. The conclusions are consistent with the metallurgical mechanism, and the predictive values of the R-ELM model agree well with experimental values, which further verifies the correctness and generalization ability of the R-ELM model.

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A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance

Cited by 50 publications

References 30 publications

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

PFW: Polygonal Fuzzy Weighted—An SVM Kernel for the Classification of Overlapping Data Groups

Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

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