Effective Noise Estimation-Based Online Prediction for Byproduct Gas System in Steel Industry

A rapid and accurate prediction of byproduct gas flow in steel industry can help not only to become aware of the operational situations of gas system, but it also provides the energy scheduling workers with sound decision-making mechanisms. In this study, a least square support vector machine (LS-SVM) model based on online hyperparameters optimization is proposed, where the variance of effective noise of the sample is estimated, while a conjugate gradient algorithm is developed to optimize the width of Gaussian kernels and the regularization factor. To assess the quality of the proposed method, we experiment with a test function affected by additive noise and an industrial gas flow data from Shanghai Baosteel Company Ltd. A series of comparative experiments are reported as well. The results demonstrate that the proposed method shows the shortest computing time while ensuring the prediction accuracy. These two features make the approach applicable to real-time prediction of gas flow in steel industry.

[1]  Antonia J. Jones,et al.  A note on the Gamma test analysis of noisy input/output data and noisy time series , 2007 .

[2]  Chi-Man Vong,et al.  Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference , 2006, Eng. Appl. Artif. Intell..

[3]  Davut Hanbay,et al.  Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs , 2009, Expert Syst. Appl..

[4]  Johan A. K. Suykens,et al.  Benchmarking Least Squares Support Vector Machine Classifiers , 2004, Machine Learning.

[5]  K. Johana,et al.  Benchmarking Least Squares Support Vector Machine Classifiers , 2022 .

[6]  A. J. Jones,et al.  A proof of the Gamma test , 2002, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[7]  X. C. Guo,et al.  PSO-Based Hyper-Parameters Selection for LS-SVM Classifiers , 2006, ICONIP.

[8]  Ya-Xiang Yuan,et al.  Optimization Theory and Methods: Nonlinear Programming , 2010 .

[9]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[10]  Amaury Lendasse,et al.  Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation , 2010, Fuzzy Sets Syst..

[11]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[12]  J Zhao,et al.  A Two-Stage Online Prediction Method for a Blast Furnace Gas System and Its Application , 2011, IEEE Transactions on Control Systems Technology.

[13]  Yi Zhao,et al.  Fulfillment of Retailer Demand by Using the MDL-Optimal Neural Network Prediction and Decision Policy , 2009, IEEE Transactions on Industrial Informatics.

[14]  Wei Wang,et al.  An optimal method for prediction and adjustment on byproduct gas holder in steel industry , 2011, Expert Syst. Appl..

[15]  Johan A. K. Suykens,et al.  The differogram: Non-parametric noise variance estimation and its use for model selection , 2005, Neurocomputing.

[16]  Andrew Kusiak,et al.  Constraint-Based Control of Boiler Efficiency: A Data-Mining Approach , 2007, IEEE Transactions on Industrial Informatics.

[17]  H. S. Kim,et al.  Nonlinear dynamics , delay times , and embedding windows , 1999 .

[18]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[19]  Samuel H. Huang,et al.  Robust and Efficient Rule Extraction Through Data Summarization and Its Application in Welding Fault Diagnosis , 2008, IEEE Transactions on Industrial Informatics.

[20]  L. Cao.  Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[21]  Antonia J. Jones,et al.  New tools in non-linear modelling and prediction , 2004, Comput. Manag. Sci..

[22]  H. Pomares,et al.  A heuristic method for parameter selection in LS-SVM: Application to time series prediction , 2011 .

[23]  Amaury Lendasse,et al.  Methodology for long-term prediction of time series , 2007, Neurocomputing.

[24]  Ginés Rubio,et al.  Efficient Optimization of the Parameters of LS-SVM for Regression versus Cross-Validation Error , 2009, ICANN.

[25]  A. N. Sharkovskiĭ.  Dynamic systems and turbulence , 1989 .

[26]  Michel Verleysen,et al.  Residual variance estimation in machine learning , 2009, Neurocomputing.

[27]  Janusz Kolbusz,et al.  Network Traffic Model for Industrial Environment , 2005, IEEE Transactions on Industrial Informatics.

[28]  I. Higashi.  Energy Balance of Steel Mills and the Utilization of By-product Gases , 1982 .

[29]  Mei Yang,et al.  Genetic Algorithm-Based Support Vector Classification Method for Multi-spectral Remote Sensing Image , 2010, LSMS/ICSEE.

[30]  Kwoh Chee Keong,et al.  Fast leave-one-out evaluation and improvement on inference for LS-SVMs , 2004, ICPR 2004.

[31]  Johan A. K. Suykens,et al.  Automatic relevance determination for Least Squares Support Vector Machines classifiers , 2001, ESANN.

[32]  Senjian An,et al.  Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression , 2007, Pattern Recognit..