ISSN: 1003-6326
CN: 43-1239/TG
CODEN: TNMCEW

Vol. 10    No. 6    December 2000

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Adaptive prediction system of sintering
through point based on self-organize artificial neural network①
FENG Qi-ming(冯其明), LI Tao(李 桃), FAN Xiao-hui(范晓慧), JIANG Tao(姜 涛)
(Department of Mineral Engineering, Central South University, Changsha 410083, P.R.China)
Abstract: A soft-sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self-organize the hidden-layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.
Key words: sintering process; burning through point; prediction; artificial neural network; BP algorithm
Superintended by The China Association for Science and Technology (CAST)
Sponsored by The Nonferrous Metals Society of China (NFSOC)
Managed by Central South University (CSU) 湘ICP备09001153号-9