Blast Furnace Dynamics Using Multiple Autoregressive Models with Exogenous Inputs

Abstract

Autoregressive models with exogenous inputs are useful tools for analyzing systems with unknown dynamics, but are limited by the assumption that the relations between inputs and output(s) are linear. For complex systems with nonlinear or abruptly changing dynamics it is possible to modify the technique by allowing for multiple local models and designing a strategy for switching between them. A method by which this can be realized is developed in the paper. The technique is applied on a complex problem in the metallurgical industry, i.e., the prediction of hot metal silicon content in the blast furnace. A set of local models is developed for different parts of a training set, using a statistical criterion for model selection. The resulting local models are then applied to predict future values of the silicon content. It is demonstrated that the method is capable to develop models, among which a proper choice can be made for prediction. The potential of multi-step predictions is also studied. Finally, some conclusions concerning the method and the results are drawn.

Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction

“…To explore and utilize useful information hidden in the data, several empirical soft-sensing approaches were applied to online predict the silicon content. Existing popular data-driven methods include artificial neural networks [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ], multivariate regression [ 14 , 15 ], time series analysis [ 16 , 17 , 18 , 19 ], fuzzy systems [ 20 ], subspace identification [ 21 ], support vector regression (SVR) and least squares SVR (LSSVR) [ 22 , 23 , 24 , 25 ], and multi-scale and multiple models [ 26 , 27 , 28 , 29 , 30 ]. These data-driven empirical models for short-term silicon content prediction can be constructed in a quick way [ 31 , 32 , 33 ].…”

“…Additionally, it is difficult to update the global models quickly when the process dynamics are changing [ 41 ]. Nurkkala et al [ 30 ] proposed multiple autoregressive vector models to describe complex systems. To construct the local models automatically, several just-in-time learning methods were utilized for nonlinear process modeling problems [ 42 , 43 , 44 ].…”

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

“…40) Additionally, the blast furnace dynamics may change, gradually or abruptly, and a fixed soft sensor model validated on earlier data may not perform well on future data. 29) Here, JLSSVR is considered as a JITL-based local modeling method. To better illustrate the effect of the proposed method, the adaptive weighted relevant sample selection strategy is applied to acquire the relevant data set for each query sample.…”

“…However, it is not enough to describe all the process characteristics only using a single model, [34][35][36][37][38][39][40] especially for some complicated regions with insufficient information. To improve the prediction performance, Nurkkala et al 29) presented multiple autoregressive vector models to describe complex systems. On the other hand, although moving-window-based recursive soft sensors can gradually be adapted to new operational conditions, how to choose a suitable moving-window size for complex blast furnace ironmaking processes is difficult.…”

“…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.…”