基于模型和神经网络的料浆混合过程优化控制

Rui Bai, Yumei Liu
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引用次数: 0

摘要

原浆混合工艺是烧结氧化铝工业的关键环节。该混合过程的最优控制目标是使原料浆的质量指标控制在目标范围内。原料流量是影响原料料浆质量指标的关键因素。如何获得合适的流量设定点是最优控制中的关键问题。提出了一种由设置层和回路控制层组成的智能最优控制方法。在整定层,采用数学模型和神经网络来获得控制回路的适当设定点。在回路控制层中,原料的实际流量遵循设定层给出的设定值。最后,通过工业实验验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal control of the raw slurry blending process based on the model and neural network
Raw slurry blending process is a key unit in the sintering alumina industry. The optimal control objective of this blending process is to make the quality indices of the raw slurry into their targeted ranges. Flow rates of raw materials are the key factors that affect the quality indices of raw slurry. How to obtain the appropriate set-points of flow rates is the key problem in the optimal control. An intelligent optimal control method, which is comprised of the setting layer and the loop control layer, is proposed. In the setting layer, mathematical model and neural network are adopted to obtain the appropriate set-points of the control loops. In the loop control layer, the actual flow rates of raw materials follow their set-points obtained from the setting layer. At last, the results of industry experiments have proven the effectiveness of the proposed method.
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