超细全尾砂絮凝沉降参数优化模型

doi:10.3981/j.issn.1000-7857.2014.17.003

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Abstract: Back-propagation neural network was used to optimize the flocculating sedimentation parameters. To get the best network mode, some learning and training samples were established by the numbered orthogonal blasting tests. In the process of establishing the network mode, the tailings concentration, flocculant consumption and flocculant concentration were used as the input data, the sedimentation speed and limiting concentration were confirmed to be the synthesized output data. Comparison of the influences of hidden layer nodes on model training process and prediction accuracy indicates that the optimal hidden layer node was 9. By entering the refined flocculating sedimentation parameters into the prediction model, optimal samples are searched and the optimal parameters show that the flocculating agent consumption is 4.5 g/t, flocculating concentration is 0.11% and tailings concentration is 15%. Compared with that of the experimental results, the relative error of the prediction results can be controlled at about 5%. The application indicates this mode has relatively high accuracy, providing a new method to optimize the flocculating sedimentation parameters.

Key words： back-propagation neural network unclassified tailings flocculating sedimentation dynamic sand release

Received: 05 March 2014

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	Articles by authors
	WANG Xinmin
	LIU Jixiang
	CHEN Qiusong
	XIAO Chongchun
	WAN Xiaoheng

Cite this article:

WANG Xinmin,LIU Jixiang,CHEN Qiusong, et al. Optimal Flocculating Sedimentation Parameters of Unclassified Tailings[J]. journal1, 2014, 32(17): 23-28.

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http://www.kjdb.org/EN/10.3981/j.issn.1000-7857.2014.17.003 OR http://www.kjdb.org/EN/Y2014/V32/I17/23

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