An optimal method for prediction and adjustment on byproduct gas holder in steel industry

Authors:
Xiaoping Zhang;Jun Zhao;Wei Wang;Liqun Cong;Weimin Feng
Affiliations:
Research Center of Information and Control, Dalian University of Technology, China;Research Center of Information and Control, Dalian University of Technology, China;Research Center of Information and Control, Dalian University of Technology, China;Shanghai Baosigh Software Co., Ltd., China;Shanghai Baosigh Software Co., Ltd., China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 12
Cited 1

An optimal gas supply for a power plant using a mixed integer programming model

Automatica (Journal of IFAC)
The nature of statistical learning theory

The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Fast exact leave-one-out cross-validation of sparse least-squares support vector machines

Neural Networks
Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression

Pattern Recognition
A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

Neurocomputing
Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs

Expert Systems with Applications: An International Journal
Evolutionary tuning of multiple SVM parameters

Neurocomputing
SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems

IEEE Transactions on Signal Processing
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

IEEE Transactions on Evolutionary Computation
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines

Artificial Intelligence in Medicine
Financial time series prediction using least squares support vector machines within the evidence framework

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	12.05

Visualization

Abstract

To maintain the balance of byproduct gas holder is an important task in optimal scheduling of byproduct energy in steel industry. However, this is often influenced by many factors and is difficult to obtain a precise mechanism model for analysis. In this paper, an optimal method for prediction and adjustment on byproduct gas holder is proposed. Considering the different operation styles of gasholders, both single and multiple gasholders level prediction models are established by machine learning methodology. And, a hybrid parameter optimization algorithm is developed to optimize the model for high prediction accuracy. Then, based on the predicted gasholder level, the optimal adjustment amount is calculated by a novel reasoning method to sustain the gasholder within safety zone. This method has been verified in the Energy Center of Baosteel, China. The results demonstrate that the proposed approach can precisely predict and adjust gasholders and provide a remarkable guidance for reasonable scheduling of byproduct gases.