Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia

This paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. The SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters. The proposed prediction model was trained and tested using historical oil data gathered from different sources. The results suggest that the demand for crude oil in Saudi Arabia will continue to increase during the forecast period (1980–2015). A number of predicting accuracy metrics including Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination ( R 2 ) were used to examine and verify the predicting performance for various models. Analysis of variance (ANOVA) was also applied to reveal the predicting result of the crude oil demand in Saudi Arabia and also to compare the actual test data and predict results between different predicting models. The experimental results show that optimized LR-MARS model performs better than other models in predicting the crude oil demand.

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Hot Metal Temperature Forecasting at Steel Plant Using Multivariate Adaptive Regression Splines

Metals ◽

10.3390/met10010041 ◽

2019 ◽

Vol 10 (1) ◽

pp. 41 ◽

Cited By ~ 3

Author(s):

José Díaz ◽

Francisco Javier Fernández ◽

María Manuela Prieto

Keyword(s):

Temperature Drop ◽

Absolute Error ◽

Multivariate Adaptive Regression Splines ◽

Metal Temperature ◽

Training Dataset ◽

Regression Splines ◽

Hot Metal ◽

Temperature Forecasting ◽

Adaptive Regression ◽

Adaptive Regression Splines

Steelmaking has been experiencing continuous challenges and advances concerning process methods and control models. Integrated steelmaking begins with the hot metal, a crude liquid iron that is produced in the blast furnace (BF). The hot metal is then pre-treated and transferred to the basic lined oxygen furnace (BOF) for refining, experiencing a non-easily predictable temperature drop along the BF–BOF route. Hot metal temperature forecasting at the BOF is critical for the environment, productivity, and cost. An improved multivariate adaptive regression splines (MARS) model is proposed for hot metal temperature forecasting. Selected process variables and past temperature measurements are used as predictors. A moving window approach for the training dataset is chosen to avoid the need for periodic re-tuning of the model. There is no precedent for the application of MARS techniques to BOF steelmaking and a comparable temperature forecasting model of the BF–BOF interface has not been published yet. The model was trained, tested, and validated using a plant process dataset with 12,195 registers, covering one production year. The mean absolute error of predictions is 11.2 °C, which significantly improves those of previous modelling attempts. Moreover, model training and prediction are fast enough for a reliable on-line process control.

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