(1. 重庆大学 资源与安全学院，煤矿灾害动力学与控制国家重点实验室，重庆 400044；
2. 博眉启明星铝业有限公司，眉山 620010；
3. 贵阳铝镁设计研究院有限公司，贵阳 550000；
4. 四川省四维环保设备有限公司，遂宁 629000)
摘 要: 本文对铝电解槽阳极效应机理和故障参数进行研究，提出了一种基于深度学习的阳极效应预测方法，能适应不同维度、不同数据特征的槽况参数，直接从海量原始数据中挖掘故障特征信息，大幅缩减效应响应时间，具有很好的鲁棒性和抗干扰能力，同时在模型调试优化上，采用Batch normalization算法和梯度检验，提高了模型收敛速度和稳定性。结果表明：该模型效应预测准确率和F1分数分别达到94.65%和0.9317，提前预报时间可达16 min，并通过现场实验验证，达到实际生产要求。
关键字: 铝电解；300 kA；阳极效应预测；深度学习；算法优化
（1. State Key Laboratory of Coal Mine Disaster Dynamics and Control, College of Resource and Safety Engineering, Chongqing University, Chongqing 400044, China;
2. Bomei Qimingxing Aluminum Co., Ltd., Meishan 620010, China;
3. Guiyang Aluminum Magnesium Design & Research Institute Co., Ltd., Guiyang 550000, China;
4. Sichuan Siwei Environmental Protection Equipment Co., Ltd., Suining 629000, China）
Abstract:The anode effect mechanism and fault parameters of aluminium electrolytic cells were studied, and a deep learning-based anode effect prediction method was proposed. It can adapt to the parameters of tank conditions in different dimensions and different data characteristics, and directly mine fault characteristic information from massive raw data. It greatly reduces the response time of the effect, has good robustness and anti-interference ability. At the same time, in the model debugging optimization, the Batch Normalization algorithm and gradient test are used to improve the model convergence speed and stability. The prediction accuracy and F1 score reach 94.65% and 0.9317, respectively. The prediction time can reach 16 min, and it is verified by field experiments to meet the actual production requirements.
Key words: aluminium electrolysis; 300 kA; anode effect prediction; deep learning; optimization
