Hybrid Neural Prediction and Optimized Adjustment for Coke Oven Gas System in Steel Industry

An energy system is the one of most important parts of the steel industry, and its reasonable operation exhibits a critical impact on manufacturing cost, energy security, and natural environment. With respect to the operation optimization problem for coke oven gas, a two-phase data-driven based fore...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2012-03, Vol.23 (3), p.439-450
Main Authors: Jun Zhao, Quanli Liu, Wei Wang, Pedrycz, W., Liqun Cong
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Blast furnaces
Coke oven gas
Computer programs
Computer science
control theory
systems
Connectionism. Neural networks
Data mining
Data processing. List processing. Character string processing
echo state network
Energy
energy balance in steel industry
Energy. Thermal use of fuels
Exact sciences and technology
Gasholders
Gaussian process
Iron and steel industry
Mathematical models
Memory organisation. Data processing
Metals industry
Optimization
Ovens
Predictive models
Production
Rational use of energy: conservation and recovery of energy
Real time
Real time systems
regression prediction
Software
Software engineering
Steel
Studies
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Summary:An energy system is the one of most important parts of the steel industry, and its reasonable operation exhibits a critical impact on manufacturing cost, energy security, and natural environment. With respect to the operation optimization problem for coke oven gas, a two-phase data-driven based forecasting and optimized adjusting method is proposed, where a Gaussian process-based echo states network is established to predict the gas real-time flow and the gasholder level in the prediction phase. Then, using the predicted gas flow and gasholder level, we develop a certain heuristic to quantify the user's optimal gas adjustment. The proposed operation measure has been verified to be effective by experimenting with the real-world on-line energy data sets coming from Shanghai Baosteel Corporation, Ltd., China. At present, the scheduling software developed with the proposed model and ensuing algorithms have been applied to the production practice of Baosteel. The application effects indicate that the software system can largely improve the real-time prediction accuracy of the gas units and provide with the optimized gas balance direction for the energy optimization.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2011.2179309