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

• Published in: Steel research international Vol. 95; no. 3
• Main Authors: Xue, Chao, Xu, Weili, Bao, Yanping, Zhao, Lihua, Liu, Xin, Zheng, Ruixuan
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.03.2024
• Subjects: Algorithms; Back propagation; Back propagation networks; backpropagation algorithm neural networks; Baking; Blowing; data analyses; Empirical analysis; industrial applications; Neural networks; Optimization; Optimization algorithms; Parameters; Prediction models; Principal components analysis; Raw materials; Regression coefficients; Regression models; Steel converters; tapping weights; Temperature measurement; whale optimization algorithms
• ISSN: 1611-3683; 1869-344X
• DOI: 10.1002/srin.202300410

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. By using the collected molten iron and scrap information of the converter through the adjusted principal component analysis–whale optimization algorithm–backpropagation algorithm tapping weight prediction model, a very accurate prediction value of the converter tapping weight can be obtained in the early stage of converter blowing.