Version 1
: Received: 21 August 2024 / Approved: 21 August 2024 / Online: 22 August 2024 (09:17:52 CEST)
How to cite:
Zhang, X.; Wang, L.; Tang, S.; Zhao, C.; Yao, J. Online Self-Learning Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development. Preprints2024, 2024081573. https://doi.org/10.20944/preprints202408.1573.v1
Zhang, X.; Wang, L.; Tang, S.; Zhao, C.; Yao, J. Online Self-Learning Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development. Preprints 2024, 2024081573. https://doi.org/10.20944/preprints202408.1573.v1
Zhang, X.; Wang, L.; Tang, S.; Zhao, C.; Yao, J. Online Self-Learning Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development. Preprints2024, 2024081573. https://doi.org/10.20944/preprints202408.1573.v1
APA Style
Zhang, X., Wang, L., Tang, S., Zhao, C., & Yao, J. (2024). Online Self-Learning Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development. Preprints. https://doi.org/10.20944/preprints202408.1573.v1
Chicago/Turabian Style
Zhang, X., Chang Zhao and Jun Yao. 2024 "Online Self-Learning Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development" Preprints. https://doi.org/10.20944/preprints202408.1573.v1
Abstract
With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process, coupled with its reliance on manual labor, results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the Local Outlier Factor (LOF) method to detect any abnormal values, Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience has demonstrated good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface and has significantly enhanced batching efficiency and product quality.
Keywords
intelligent formula of raw materials; rotary hearth furnace; online clustering algorithm; intelligent optimization system
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
