Prediction of the Temperature of Liquid Aluminum and the Dissolved Hydrogen Content in Liquid Aluminum with a Machine Learning Approach

Kim, Moon-Jo; Yun, Jong Pil; Yang, Ji-Ba-Reum; Choi, Sangchun; Kim, DongEung

doi:10.3390/met10030330

Cited by 11 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lowest MSE value was also obtained for this model (MSE = 85.36). The linear regression model can be a proper model to predict if the accuracy is sufficient, which is known as one of the simplest machine learning models [50]. As their comparison shows, the best results were obtained for the GPR model.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland

et al. 2022

View full text Add to dashboard Cite

Air pollution has a major impact on human health, especially in cities, and elevated concentrations of PMx are responsible for a large number of premature deaths each year. Therefore, the amount of PM10 in the air is monitored and forecasts are made to predict the air quality. In Poland, mainly deterministic models are used to predict air pollution. Accordingly, research efforts are being made to develop other models to forecast the ambient PM10 levels. The aim of the study was to compare the machine learning models for predicting PM10 levels in the air in the city of Lublin. The following machine learning models were used: Linear regression (LR), K-Nearest Neighbors Regression (KNNR), Support Vector Machine (SVM), Regression Trees (RT), Gaussian Process Regression Models (GPR), Artificial Neural Network (ANN) and Long Short-Term Memory network (LSTM). The collected data for three consecutive years (January 2017 to December 2019) were used to develop the models. In total, 19 parameters, covering meteorological variables and concentrations of several chemical species, were explored as potential predictors of PM10. The data used to build the models did not take into account the seasons. The algorithms achieved the following R2 values: 0.8 for LR, 0.79 for KNNR, 0.82 for SVM, 0.77 for RT, 0.89, 0.90 for ANN and 0.81 for LSTM. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. In this research, the ANN model performed slightly better than other models. Then, an ANN was used to train a network with five output neurons to predict the approximate level of PM10 at different time points (PM level at a given time, after 1 h, after 6 h, after 12 h and after 24 h). The results showed that the developed and tuned ANN model is appropriate (R = 0.89). The model created in this way can be used to determine the risk of exceeding the PM10 alert level and to inform about the air quality in the region.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The regression trees that we will use in the current study are binary, and every step in prediction included examining the value of one predictor parameter. The lowest leaf size will be 4, 12, and 36 for fine trees, medium trees, and coarse trees, respectively (Kim et al, 2020).…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Application of Soft Computing in Predicting Groundwater Quality Parameters

Hanoon

Ammar

Ahmed

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

Evaluating the quality of groundwater in a specific aquifer could be a costly and time-consuming procedure. An attempt was made in this research to predict various parameters of water quality called Fe, Cl, SO4, pH and total hardness (as CaCO3) by measuring properties of total dissolved solids (TDSs) and electrical conductivity (EC). This was reached by establishing relations between groundwater quality parameters, TDS and EC, using various machine learning (ML) models, such as linear regression (LR), tree regression (TR), Gaussian process regression (GPR), support vector machine (SVM), and ensembles of regression trees (ER). Data for these variables were gathered from five unrelated groundwater quality studies. The findings showed that the TR, GPR, and ER models have satisfactory performance compared to that of LR and SVM with respect to different assessment criteria. The ER model attained higher accuracy in terms of R2 in TDS 0.92, Fe 0.89, Cl 0.86, CaCO3 0.87, SO4 0.87, and pH 0.86, while the GPR model attained an EC 0.98 compared to all developed models. Moreover, comparisons among the different developed models were performed using accuracy improvement (AI), improvement in RMSE (PRMSE), and improvement in PMAE to determine a higher accuracy model for predicting target properties. Generally, the comparison of several data-driven regression methods indicated that the boosted ensemble of the regression tree model offered better accuracy in predicting water quality parameters. Sensitivity analysis of each parameter illustrates that CaCO3 is most influential in determining TDS and EC. These results could have a significant impact on the future of groundwater quality assessments.

show abstract

“…There are a large number of aluminum products in the manufacturing industry [2][3][4][5][6][7][8], and efficient production process control can reduce production costs and improve product quality. In the process of manufacturing casting products, the temperature control of molten aluminum is a key factor [3,[8][9][10][11]. In this sense, it is of great significance to control the temperature of molten aluminum to improve the performance of castings.…”

Section: Introductionmentioning

confidence: 99%

A Liquid Metal Temperature Detection System Based on Multi-Node Sapphire Fiber Sensor

Wang

Qian

Sun

et al. 2023

Sensors

View full text Add to dashboard Cite

In order to accurately detect the temperature of molten aluminum and overcome the adverse influence of high temperature and corrosiveness on the sensing results, a temperature detection system based on a multi-node sapphire fiber sensor was proposed and developed. Through the structural parameter design of the fiber sensor, the scheme of utilizing the 0.7 mm diameter fiber and 0.5 mm groove was formulated. Simulation and analysis were carried out to determine the ultrasonic response distribution of the signal passing through the whole fiber sensor. The results indicate that the system is capable of distinguishing test signals from various positions and temperatures. Following the completion of the static calibration, the temperature of the molten aluminum was observed in real-time, and the data of the temperature measurements conducted at the two groove locations were compared. According to the obtained results, the test accuracy was greater than 1 degree Celsius and the temperature test stability was good, laying a solid foundation for the potential development of temperature measurement devices.

show abstract

Prediction of the Temperature of Liquid Aluminum and the Dissolved Hydrogen Content in Liquid Aluminum with a Machine Learning Approach

Cited by 11 publications

References 44 publications

Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland

Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland

Application of Soft Computing in Predicting Groundwater Quality Parameters

A Liquid Metal Temperature Detection System Based on Multi-Node Sapphire Fiber Sensor

Contact Info

Product

Resources

About