Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: a review
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier b.v.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Iron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing production and improving energy efficiency. This paper provides a systematic review of intelligent prediction and soft-sensing techniques applied to the iron ore sintering process. It details the mechanisms and operational principles of these technologies, with a focus on key indicators such as quality, thermal state, yield, and energy consumption. This paper explores the current state-of-the-art in four prediction methodologies: mechanism analysis-based methods, data feature analysis-based methods, multi-model fusion-based methods, and operating mode recognition-based methods. Finally, the challenges to the current comprehensive production indicator prediction of the sintering process are pointed out, including the difficulty of dealing with the changing operating mode, the incomplete analysis of image features, and the insufficient consideration of the differences in data distribution. In the future, operating mode recognition approaches, deep learning approaches, transfer learning approaches, and computer vision techniques will have a broad prospect in the comprehensive production indicator prediction of the sintering process.
Açıklama
Anahtar Kelimeler
Data-Driven, Indicator Prediction, Intelligent Prediction, Operating Mode, Sintering Process
Kaynak
Computers in industry
WoS Q DeÄŸeri
Q1
Scopus Q DeÄŸeri
Q1
Cilt
165
Sayı
Künye
Du, S., Ma, X., Fan, H., Hu, J., Cao, W., Wu, M., & Pedrycz, W. (2025). Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review. Computers in Industry, 165, 104215.
