Abstract
In oxygen steelmaking, the charge calculation strongly depends on hot metal temperature prediction. Although a hot metal temperature drop from the blast furnace in a steel plant may be too complex to be accurately modeled in detail, the combined use of sensors and statistical models can improve temperature estimation and result in better cost, quality and productivity, as well as lower emissions. In order to develop a simple but robust method for hot metal temperature forecasting, the suitability of infrared thermometry and time series forecasting has been studied. Simultaneous infrared thermometer measurement and video recording was used for designing the processing of the thermometer signal. The resulting temperature estimations are in good agreement with disposable thermocouple measurements giving an error of 11 °C with 60% reliability (chances of obtaining a successful output). Conversely, the time series approach was based mainly on the AutoRegressive Integrated Moving Average (ARIMA) model in which five additional process variables were introduced as exogenous predictors, as well as using a moving window of past observations for continuous model training. The resulting error was 15 °C with more than 90% reliability. Combining measuring and modeling approaches reduced the error to 13 °C with 100% reliability, thereby providing a hybrid procedure that has long-term stability and is self-adaptive to varying production scenarios.
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)
Reference41 articles.
1. Iron Making and Steelmaking: Theory and Practice;Ghosh,2008
2. Oxygen Steelmaking Processes;Miller,1998
3. Reduction of CO2 Emissions from Integrated Steelmaking by Optimised Scrap Strategies: Application of Process Integration Models on the BF–BOF System
4. Adaptation of process integration models for minimisation of energy use, CO2-emissions and raw material costs for integrated steelmaking;Ryman;Chem. Eng. Trans.,2007
5. Blast Furnace Hot Metal Temperature Prediction through Neural Networks-Based Models
Cited by
12 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献