氧化铝焙烧炉内温度智能优化控制

发布时间：2018-10-12 19:25

【摘要】：为了降低生产成本、提高氧化铝产品质量,提出了一种氧化铝焙烧炉内温度智能优化控制方法。首先,基于机理分析和灰关联分析法确定影响氧化铝焙烧炉内温度的关键参数,建立炉内温度的ELM网络预测模型,然后采用改进PSO对预测模型的参数进行优化;最后利用遗传算法的全局寻优能力寻找得到不同炉内温度下对应的最优操作参数值。通过实际生产数据进行仿真,仿真结果表明,优化控制方法可以较准确地预测焙烧炉内温度,同时得到预期指标范围内的操作参数值。
[Abstract]:In order to reduce the production cost and improve the quality of alumina products, an intelligent optimization control method for temperature in alumina roaster was proposed. Firstly, based on the mechanism analysis and grey correlation analysis, the key parameters affecting the temperature of alumina roaster are determined, and the ELM network prediction model of the furnace temperature is established. Then, the parameters of the prediction model are optimized by improved PSO. Finally, the global optimization ability of genetic algorithm is used to find the corresponding optimal operating parameters at different furnace temperatures. The simulation results show that the optimal control method can accurately predict the temperature in the calciner and obtain the operating parameters in the range of the expected index.
【作者单位】：广西大学电气工程学院;
【基金】：国家自然科学基金项目(61364007) 广西自然科学基金项目(2014GXNSFAA118391)
【分类号】：TP18;TQ133.1

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