Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process

Accurate prediction of carbon efficiency is a prerequisite for achieving energy saving and consumption reduction in an iron ore sintering process, and is the key to guaranteeing the quality and yield of sintered ore. This paper proposes an original real-time dynamic prediction model for carbon effic...

Full description

Saved in:
Bibliographic Details
Published in:Journal of process control Vol. 111; pp. 97 - 105
Main Authors: Hu, Jie, Wu, Min, Chen, Luefeng, Cao, Weihua, Pedrycz, Witold
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.03.2022
Subjects:
Broad learning
Carbon efficiency prediction
Iron ore sintering process
Real-time dynamic prediction model
Working condition identification
Broad learning
Iron ore sintering process
Real-time dynamic prediction model
Working condition identification
Carbon efficiency prediction
ISSN:0959-1524, 1873-2771
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate prediction of carbon efficiency is a prerequisite for achieving energy saving and consumption reduction in an iron ore sintering process, and is the key to guaranteeing the quality and yield of sintered ore. This paper proposes an original real-time dynamic prediction model for carbon efficiency prediction in the process. A Savitzky–Golay filter is used to eliminate noise of the actual production data collected from a cooperative sintering plant, and the correlation between carbon efficiency and process parameters is determined by mutual information. A modified version of maximum entropy clustering algorithm is presented for identifying working conditions to accurately discriminate between anomalies and normal working conditions. Then, the real-time dynamic prediction model of carbon efficiency based on broad learning is established by taking into account the process characteristics and using the prediction error information under normal working conditions. The proposed model is demonstrated to be valid by carrying out some experiments with actual production data. The experimental comparative analysis show that this model has good generalization capabilities and high real-time prediction accuracy, and is superior to other advanced methods in dynamic prediction of carbon efficiency. •An improved maximum entropy clustering is presented to identify working conditions.•A real-time dynamic prediction model of CCR based on broad learning is established.•A suitable modeling framework is proposed to reflect the sintering process dynamics.
ISSN:0959-1524
1873-2771
DOI:10.1016/j.jprocont.2022.02.002