No abstract

No abstract

No 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

No abstract

No abstract

No abstract

Blast furnace is one of the most complex industrial reactors and remains some unsolved puzzles, such as blast furnace automation, prediction of the inner thermal state, etc. In this work, a sliding‐window smooth support vector regression model is presented to address the issue of predicting the blast furnace inner thermal state, represented by the silicon content in blast furnace hot metal in the context. Different from the traditional numerical prediction models of silicon, the constructed SW‐SSVR model is devoted to predicting the changing trend of silicon and exhibits good performance with high percentage of successful trend prediction, competitive computational speed and timely online service. Additionally, some sharp fluctuation trend in the silicon test data can also be followed well by the SW‐SSVR model, which is always difficult for traditional data–driven based silicon prediction models. All of these indicate that the SW‐SSVR model is a good candidate to predict the change of blast furnace inner thermal state, and may provide a guide for operators to take proper action on operating blast furnace in advance.