A chaos‐based iterated multistep predictor for blast furnace ironmaking process

Abstract

in Wiley InterScience (www.interscience.wiley.com).The prediction and control of the inner thermal state of a blast furnace, represented as silicon content in blast furnace hot metal, pose a great challenge because of complex chemical reactions and transfer phenomena taking place in blast furnace ironmaking process. In this article, a chaos-based iterated multistep predictor is designed for predicting the silicon content in blast furnace hot metal collected from a pint-sized blast furnace. The reasonable agreement between the predicted values and the observed values indicates that the established high dimensional chaotic predictor can predict the evolvement of silicon series well, which conversely render the strong indication of existing deterministic mechanism ruling the dynamics of complex blast furnace ironmaking process, i.e., a high-dimensional chaotic system is suitable for representing the blast furnace system. The results may serve as guidelines for characterizing blast furnace ironmaking process, an extremely complex but fascinating field, with chaos in the future investigation. V V C 2009 American Institute of Chemical Engineers AIChE J, 55: 947-962, 2009

Ensemble Non-Gaussian Local Regression for Industrial Silicon Content Prediction

“…And the data in different operating areas are distributed irregularly. To evaluate the prediction performance, three indices of the root-mean-square error (RMSE), relative RMSE (simply noted as RE), and the hit rate (HR) [19][20][21][22][23][24][25][26] are utilized and defined below, respectively. (11) where y q and ŷ q are the actual value and the predicted value, respectively.…”

Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

“…40) Additionally, the hit rate (HR) index is often adopted in industrial blast furnace ironmaking processes. [21][22][23][24][25][26][27][28] Three indices of RMSE, RE, and HR are defined, respectively. (18) where ŷ q and y q are the predicted value and the actual value, respectively.…”

“…To online predict the silicon content, various data-driven soft sensor modeling approaches, including various neural networks, [7][8][9][10][11][12][13][14] partial least squares, 14,15) fuzzy inference systems, 16) nonlinear time series analysis, [17][18][19][20] subspace identification, 21) support vector regression (SVR) and least squares SVR (LSSVR), [22][23][24] and others [25][26][27][28][29] have been investigated. A recent overview of black-box models for short-term silicon content prediction in blast furnaces can be referred to.…”

Adaptive Least Squares Support Vector Machine Predictor for Blast Furnace Ironmaking Process

“…Today, as a BF system is concerned, data-driven modeling is being broadly investigated and exhibits great potential in describing the complex behavior of the BF process. Candidate data-driven modeling tools include state space, 3) neural networks, 4) genetic programming, 5) partial least squares, 6) fuzzy set, 7) chaos, 8) support vector machine (SVM), [9][10][11] generalized Gaussian regularization network, 12) and generalized autoregressive conditional heteroskedastic model. 13) These data-driven methods are expected to be able to improve the BF model accuracy and also to shed more light on how to optimize the BF process operation in the future.…”