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铁道科学与工程学报

JOURNAL OF RAILWAY SCIENCE AND ENGINEERING

Vol. 17    No. 5    October 2007

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Prediction of Al(OH)3 fluidized roasting temperature based on wavelet neural network
LI Jie(李 劼)1, LIU Dai-fei(刘代飞)1, DAI Xue-ru(戴学儒)2, ZOU Zhong(邹 忠)1, DING Feng-qi(丁凤其)1

1. School of Metallurgical Science and Engineering, Central South University, Changsha 410083, China; 2. Changsha Engineering and Research Institute of Nonferrous Metallurgy, Changsha 410011, China

Abstract:The recycle fluidization roasting in alumina production was studied and a temperature forecast model was established based on wavelet neural network that had a momentum item and an adjustable learning rate. By analyzing the roasting process, coal gas flux, aluminium hydroxide feeding and oxygen content were ascertained as the main parameters for the forecast model. The order and delay time of each parameter in the model were deduced by F test method. With 400 groups of sample data (sampled with the period of 1.5 min) for its training, a wavelet neural network model was acquired that had a structure of {7-21-1} i.e., seven nodes in the input layer, twenty-one nodes in the hidden layer and one node in the output layer. Testing on the prediction accuracy of the model shows that as the absolute error ±5.0 ℃ is adopted, the single-step prediction accuracy can achieve 90% and within 6 steps the multi-step forecast result of model for temperature is receivable.

 

Key words: wavelet neural networks; aluminum hydroxide; fluidized roasting; roasting temperature; modeling; prediction

ISSN 1672-7029
CN 43-1423/U

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