Data-driven based model for flow prediction of steam system in steel industry

Authors:
Ying Liu;Quanli Liu;Wei Wang;Jun Zhao;Henry Leung
Affiliations:
Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, China;Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, China;Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, China;Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, China;Department of Electrical and Computer Engineering, University of Calgary, Canada
Venue:
Information Sciences: an International Journal
Year:
2012

Citing 7
Cited 1

A practical Bayesian framework for backpropagation networks

Neural Computation
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
2007 Special Issue: Optimization and applications of echo state networks with leaky- integrator neurons

Neural Networks
2007 Special Issue: Decoupled echo state networks with lateral inhibition

Neural Networks
Design of hybrid differential evolution and group method of data handling networks for modeling and prediction

Information Sciences: an International Journal
Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay

Information Sciences: an International Journal
Support Vector Echo-State Machine for Chaotic Time-Series Prediction

IEEE Transactions on Neural Networks

Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry

Information Sciences: an International Journal

Quantified Score

Hi-index	0.07

Visualization

Abstract

The steam system is one of the main energy systems in steel industry, and its operational scheduling plays a crucial role for energy utility and resources saving. For a reasonable resources operation, the accurate prediction of steam flow is required. Considering the large amount of production data in energy system, a data-driven based model is proposed to perform a time series prediction for steam flow, in which a Bayesian echo state network (ESN) is established. This method combines Bayesian theory with ESN to obtain optimal output weight via maximizing the posterior probability density of the weights to avoid over-fitting in the training process of sample data. To pursue optimized hyper-parameters in the proposed Bayesian ESN, the evidence framework based on sample data is further adopted in this work. Experimental results using the real production data from Shanghai Baosteel show the validity and practicality of the proposed data-driven based model in providing scientific decision guidance for the steam system.