Elongation Prediction of Strip Steel in Annealing Furnace based on KPCA and Optimized LSSVM with ICPSO

DOI

10.2991/aiie-15.2015.92How to use a DOI?

Keywords

kernel principal component analysis; strip elongation; immune clone particle swarm optimization; least squares support vector machine; soft-sensing

Abstract

The elongation prediction of strips in furnace is extremely important in annealing process, which determines the quality and yield of product [1, 2]. Furthermore, the safety of air-knife also depends on the prediction accuracy [3, 4]. Thus, the optimal soft-sensing method is proposed based on kernel principal component analysis (KPCA) and optimized weighted least squares support vector machine (WLSSVM) by immune clone particle swarm optimization (ICPSO). Avoiding the particles are easy to sink into premature convergence and run into local optimization in the iterative process by using ICPSO , which generated by particle swarm optimization (PSO) algorithm, and the ICPSO is also used to optimize the parameters of WLSSVM. Then, the method uses KPCA to denoise the input data set and capture the high-dimensional nonlinear principal components in input data space, and the principal components are input into the ICPSO-WLSSVM model to establish the soft-sensing prediction model. The proposed method is successfully applied in the strip elongation prediction in annealing furnace. The simulations show that the KPCA and ICPSO-WLSSVM model has higher prediction accuracy compared with other algorithms that verified with production data.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - C. Wang
AU  - J.H. Wang
AU  - S.S. Gu
AU  - X.K. Fang
AU  - Y.X. Zhang
PY  - 2015/07
DA  - 2015/07
TI  - Elongation Prediction of Strip Steel in Annealing Furnace based on KPCA and Optimized LSSVM with ICPSO
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 330
EP  - 333
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.92
DO  - 10.2991/aiie-15.2015.92
ID  - Wang2015/07
ER  -