Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

Xin, Zicheng; Zhang, Jiangshan; Jin, Yu; Zheng, Jin; Liu, Qing

doi:10.1007/s12613-021-2409-9

Cited by 15 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The basic design parameters of the truss structure are shown in Table 3. Then, after solving, the principal component analysis (PCA) method [41] is used to extract the design variables in the SFE model according to their contribution levels, and the extraction results are shown in Figure 6.…”

Section: Figure 4 Schematic Of the Database Constructionmentioning

confidence: 99%

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Minqing,

Hongxi,

Weihuan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In the process of daily product development and design, optimizing the shape and external dimensions of the vehicle chassis truss structure while considering both weight and reliability indicators often involves interdisciplinary collaboration and inefficient communication, leading to repetitive mechanical labor and low efficiency. This is widely regarded as a difficult and challenging task. In order to improve the design quality of the chassis truss structure and enhance work efficiency, this paper proposes a novel lightweight multi-objective optimization design system for vehicle chassis trusses based on the ANFIS-SHAMODE-IWOA model. Firstly, an adaptive spiral search strategy is introduced based on the multi-objective hybrid metaheuristic algorithm (SHAMODE) which is based on successful history. Then, a new SHAMODE-IWOA algorithm is proposed. In order to estimate the reliability level of the chassis truss structure under different design parameter combinations, a new ANFIS-SHAMODE-IWOA model is constructed by using the proposed SHAMODE-IWOA algorithm to learn the ANFIS model. Finally, in order to obtain the optimal design parameter combination, multi-objective optimization based on minimum design quality and optimal reliability measurement functions is studied using the SHAMODE-IWOA algorithm. Experimental results show that SHAMODE-IWOA has leading global optimization capability on CEC2017 test set benchmark functions. Compared with other intelligent models, the ANFIS-SHAMODE-IWOA model has better performance in reliability coefficient estimation. At the same time, the SHAMODE-IWOA algorithm obtains a more optimal design parameter combination for the chassis truss, with improvements of 9.5%, 12.5%, and 15% in Mass, Bending, and Torsion indicators, respectively. Finally, the proposed ANFIS-SHAMODE-IWOA multi-objective optimization design system, as a novel intelligent model, can be used to evaluate the reliability of chassis truss structures, improve development and design efficiency, and obtain the best design parameter combination, which is beneficial to improving the level of green intelligent manufacturing design.

show abstract

Section: Figure 4 Schematic Of the Database Constructionmentioning

confidence: 99%

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Minqing,

Hongxi,

Weihuan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…R 2 , mean absolute error (MAE), root mean squared error (RMSE), and hit ratio are selected as the evaluation indexes for the test set. [39,40] The model aims to obtain more petite MAE and RMSE, a larger hit ratio, and an R 2 closer to 1. The hit ratio calculation formula is shown in Equation ( 3).…”

Section: Evaluation and Analysismentioning

confidence: 99%

A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace

Lu,

Zhu,

Jiang

et al. 2024

steel research int.

View full text Add to dashboard Cite

Developing the prediction model of the end‐point carbon content of the electric arc furnace (EAF) is an effective way to reduce the adjustment frequency of liquid steel composition and shorten the smelting time. Previous data‐driven models lack effective handling of the missing values in EAF production data. This may be the main reason why model accuracy is difficult to improve. This article proposes a novel modeling method based on the CatBoost algorithm with two‐stage optimization. In the preprocessing session, empirical and empirical‐cumulative‐distribution‐based outlier detection (ECOD) methods are utilized to extract input features and reject outliers. The end‐point carbon content prediction model is built based on CatBoost. The generative adversarial imputation nets (GAIN) method is used in the first optimization stage to handle the missing values. In the second optimization stage, recursive feature elimination (RFE) is used to select the final features, and whale optimization algorithm (WOA) is used to optimize the parameters of the CatBoost model. After verification with actual production data, the two‐stage optimized CatBoost model demonstrates excellent performance compared with other methods, with an R2 of 0.903, mean absolute error of 0.021, root mean squared error of 0.043, and 90.34% hit ratio within ±0.05% error range.

show abstract

“…1) The tapping weight information of molten steel affects the alloying process of converter. Equation (1) represents alloy burden in converter process [4] X…”

Section: Introductionmentioning

confidence: 99%

“…1) The tapping weight information of molten steel affects the alloying process of converter. Equation (1) represents alloy burden in converter process [ 4 ]

\sum_{n = 1}^{k} m_{n} \left(\omega\right)_{n i} \left(\eta\right)_{i} = \left(\right. \left(\omega\right)_{i a} - \left(\omega\right)_{i b} \left.\right) \cdot M

$$s .

…”

Section: Introductionmentioning

confidence: 99%

Prediction of Converter Tapping Weight Based on Principal Component Analysis–Whale Optimization Algorithm–Backpropagation Algorithm Neural Network Model

Xue,

Xu,

Bao

et al. 2024

steel research int.

View full text Add to dashboard Cite

Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis–whale optimization algorithm–backpropagation algorithm (PCA–WOA–BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted; the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately.

show abstract

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

Cited by 15 publications

References 24 publications

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace

Prediction of Converter Tapping Weight Based on Principal Component Analysis–Whale Optimization Algorithm–Backpropagation Algorithm Neural Network Model

Contact Info

Product

Resources

About