基于综合指标评估模型的铝电解槽状态智能预测

Intelligent prediction of aluminum electrolysis cell state based on comprehensive index evaluation model

在电解铝生产中,准确判断电解槽的运行状态是实现过程优化的前提条件。目前生产中槽状态及其变化趋势的解析主要靠人工经验,为了提高槽状态评判与预测的准确性,提出了一种基于综合指标评估模型的铝电解槽状态智能预测方法。首先,从全局的角度建立关于能量平衡、物料平衡、稳定性的槽状态综合指标模型;其次,为了准确分类槽状态,采用模糊C均值聚类算法构建槽状态评估模型,根据综合指标大小将槽状态分为优、良、差三类;最后,建立基于模糊神经网络的槽状态预测模型,实现24h后的状态预测。采用实际生产数据对模型进行验证,结果表明,该方法能够准确判断当前槽状态并预测未来槽状态,对稳定的电解铝生产、实现节能降耗有一定指导意义。

In electrolytic aluminum process,the analysis of electrolytic cell s operation state is precondition to its optimization and control.Currently,cell state evaluation and prediction is generally based on manual experience.To improve the evaluation accuracy,an intelligent prediction method of cell state based on comprehensive index evaluation model is proposed.First,from a global perspective,a comprehensive index is defined,which reflects energy balance,material balance and process stability.Then,a cell state evaluation model based on fuzzy c-means clustering algorithm is established to classify the cell states into three categories,which are good,normal and poor.Finally,the cell state prediction model based on fuzzy neural network is designed then the cell state can be predicted 24 hours in advance .The proposed model is validated by using the actual process data.The results show that the method can accurately evaluate the current cell state and predict the future cell state,which is of great significant to the process stability and energy saving.