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

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.

[1]  Yoshikazu Nishikawa,et al.  An optimal gas supply for a power plant using a mixed integer programming model , 1991, Autom..

[2]  Christian Igel,et al.  Evolutionary tuning of multiple SVM parameters , 2005, ESANN.

[3]  Herbert Moskowitz,et al.  Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms , 2007, Eur. J. Oper. Res..

[4]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[5]  Chonghun Han,et al.  CONTROL OF GASHOLDER LEVEL BY TREND PREDICTION BASED ON TIME-SERIES ANALYSIS AND PROCESS HEURISTICS , 2003 .

[6]  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..

[7]  Gopal P. Sinha,et al.  A linear programming model for distribution of electrical energy in a steel plant , 1994 .

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  J. Suykens,et al.  Subspace identification of Hammerstein systems using least squares support vector machines , 2005 .

[10]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[11]  Fernando Pérez-Cruz,et al.  SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems , 2004, IEEE Transactions on Signal Processing.

[12]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[13]  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.

[14]  Milorad Bojić,et al.  Energy saving does not yield CO2 emissions reductions: the case of waste fuel use in a steel mill , 2000 .

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

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

[17]  Sabine Van Huffel,et al.  Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines , 2003, Artif. Intell. Medicine.

[18]  Chonghun Han,et al.  A Novel MILP Model for Plantwide Multiperiod Optimization of Byproduct Gas Supply System in the Iron- and Steel-Making Process , 2003 .

[19]  Gavin C. Cawley,et al.  Fast exact leave-one-out cross-validation of sparse least-squares support vector machines , 2004, Neural Networks.

[20]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[21]  R. Poppi,et al.  Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. , 2006, Analytica chimica acta.

[22]  Ernst Worrell,et al.  Energy efficiency and carbon dioxide emissions reduction opportunities in the US iron and steel sector , 2001 .

[23]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[24]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[25]  Keiichi Akimoto,et al.  New Energy Control System at Kawasaki Steel's Mizushima Works , 1989 .

[26]  Rustam M. Vahidov,et al.  Application of machine learning techniques for supply chain demand forecasting , 2008, Eur. J. Oper. Res..

[27]  Theodore B. Trafalis,et al.  Robust classification and regression using support vector machines , 2006, Eur. J. Oper. Res..

[28]  X. C. Guo,et al.  A novel LS-SVMs hyper-parameter selection based on particle swarm optimization , 2008, Neurocomputing.