Abstract
Blast furnace system is a typical example of complex industrial system. The silicon ([Si]) content in blast furnace system is an important index to reflect the temperature of furnace. Therefore, it is significant to carry out an accurate predictive control of furnace temperature. In this paper a composite model combining Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LSSVM) is established to predict the furnace temperature. At the very beginning, in order to avoid redundancy and excessive noise pollution, PCA method is applied to reduce the dimensionality of original input variables. Secondly, the dimension-reduced variables are introduced to predict the silicon content by applying the LSSVM model. Finally, the result is compared with direct multivariable LSSVM prediction. The simulation results show that the new algorithm has positive significance as it achieves more obvious prediction hit rate (more than 80%) than direct multivariable LSSVM (with rate lower than 75%).
Highlights
Blast furnace temperature is an important index during operating
The control of the silicon content ([Si]) of molten iron is closely related to the furnace condition, stability, production efficiency, energy consumption and the quality of molten iron ([S]) in the process of blast furnace smelting
Analyzing the existing prediction model, we found that some models only used single silicon content of ([Si]) sequences without many key state and control variables, this is not reality
Summary
Blast furnace temperature is an important index during operating. keeping it stationary in a reasonable range is essential. Many domestic and foreign research teams have established many models such as Bayesian network model[3], chaotic prediction model[4], neural network model based on genetic algorithm[5], fuzzy data generation rule control model[6,7], partial least squares model[8], mathematical model of multifluid theory[9], support vector machine and intelligent algorithm cross model[10], wavelet analysis model etc These models obtain satisfactory results in different aspects; due to the complexity of the blast furnace system, it is difficult to achieve closed-loop predictive control in this field.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Online and Biomedical Engineering (iJOE)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
