KPCA and LSSVM model-based slag basicity prediction for silicomanganese smelting process

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument

Volumn 31, Issue 3, 2010, Pages 689-693

  Tang, Chunxia ^a   Yang, Chunhua ^a   Gui, Weihua ^a   Zhu, Hongqiu ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Kernel principal component analysis(KPCA); Least square support vector machine(LSVM); Silicomanganese; Slag basicity

Indexed keywords

DE-NOISE; INPUT DATAS; KERNEL PRINCIPAL COMPONENT ANALYSIS; LEAST SQUARE SUPPORT VECTOR MACHINES; MODEL-BASED; ON-LINE MEASUREMENT; PREDICTING METHOD; PREDICTION MODEL; PRINCIPAL COMPONENTS; SILICOMANGANESE; SIMULATION RESULT; SLAG BASICITY; SMELTING PROCESS; TRACKING PERFORMANCE;

COMPUTER SIMULATION; FORECASTING; INPUT OUTPUT PROGRAMS; MATHEMATICAL MODELS; SLAGS; SMELTING; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77952130972     PISSN: 02543087     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Shi J T, Ma Y Q, Jiang R Q. Study on improving Mn and Si recovery for ferromanganese-silicon smelting and its practice [J]. Chinese Journal of FERRO-ALLOYS, 2006, 188(3):14.
2. Hauksdottir A S, Gestssonb A, Vesteinsson A. Current control of a three-phase submerged arc ferro-silicon furnace [J]. Control Engineering Practice, 10(2002):457-463.
3. Singh V, Tathavadkar V, Rao S, et al. Predicting the performance of submerged arc furnace with varied raw material combinations using artificial neural network. [J] Journal of Materials Processing Technology, 183(2007):111-116.
4. Krishnaiah J, Kumar C S, Faruqi M A. Modelling and control of chaotic processes through their bifurcation diagrams generated with the help of recurrentneural network models. Part 2: An industrial study [J]. Journal of Process Control, 16(2006):67-79.
5. Gao Ch H, Jian L, Chen J M, et al. Data-driven modeling and predictive algorithm for complex blast furnace ironmaking process [J]. Acta Automatica Sinica 2009, 35(6): 725-730.
6. Kadlec P, Gabrys B, Strandt S. Data-driven Soft Sensors in the process industry [J]. Computers and Chemical Engineering, 33(2009):795-814.
7. Vapnik V. The nature of statistical learning theory [M]. New York: Springer-Verlag, 1999:150-164.
8. Chang Y Q, Wang F L, Wang X G, et al. Soft sensor modeling based on support vector machines and its applications to fermentation process [J]. Chinese Journal of Scientific Instrument, 2006, 27(3):241-244.
9. Suykens J A K. Nonlinear model and support vector machines [C]. IEEE Instrumentation and Measurement Technology Conf. Budapest, Hungary, 2001:287-294.
10. Yan W W, Shao H H, Wang X F. Soft sensing modeling based on support vector machine and Bayesian mod- el selection [J]. Computers and Chemical Engineering, 28(2004): 1489-1498.
11. Suykens J A K, Vanderwalle J. Least squares support vector machine classifiers [J]. Neural Processing Letters, 1999, 9(3):293-300.
12. Kong L Sh, Yang Ch H, Zhu H Q, et al. LS_SVM prediction model based on RS reconstruction and its industrial application [J]. Chinese Journal of Scientific Instrument, 2009, 30(5):921-925.
13. Zhang Y W. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM [J]. Journal of Chemical Engineering Science, 64(2009):801-811.
14. Teixeira A R, Tomea M, Stadlthanner K, et al. KPCA Denoising and the pre-image problem revisited [J]. Digital Signal Processing, 18(2008):568-580.
15. Liu D P, Ye X R, Chen B Y, et al. Fault diagnosis of medium speed mill based on KPCA and LSSVM [J]. Chinese Journal of Power Engineering, 2009, 29(2): 155-158.