氧化铝焙烧炉内温度智能优化控制

Intelligent Optimized Control on Temperature of Alumina Roaster Inside

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。

In order to improve the quality of alumina products, a temperature intelligent optimization control meth- od of alumina roaster inside is proposed. Firstly, we used mechanism analysis and gray relational analysis method to determine the key parameters affecting of temperature of alumina roaster inside. An ELM network prediction model of temperature was established, and an improved PSO was used to optimize the parameters of the prediction model; after these work, we used the genetic algorithm to obtain the global optimal value of temperature and the corresponding pro- duction conditions. The simulation results show that the model can predict calcinations temperature accurately, at the same time, the setting values are in the range of expected indicators.