开发了以槽电阻斜率和累积斜率为主要判据、以物料平衡估算值以及电阻针振强度为辅助判据的阳极效应智能预报方法 .该方法的步骤是 :首先获取用于阳极效应预报的信息 ,包括槽电阻采样、槽电阻低通滤波、槽电阻斜率与累积斜率计算、槽电阻针振强度计算以及物料平衡估算 (即估算一定时间内累计加入电解槽内的氧化铝量与该时间内理论消耗量之差 ) ;然后据此进行阳极效应预报 ,即利用当前解析周期内的电阻斜率与累积斜率推理确定当前预报阳极效应的可信度 ,依据近期槽电阻针振强度的变化趋势以及物料累计偏差的取值情况对可信度进行调整 ,依据可信度值对阳极效应预报的进程标志进行处理 .测试结果表明 ,该方法的阳极效应预报成功率达 90 %以上 . An intelligent method for the prediction of anodic effects is developed based on the criterion of slope and slope of tank resistance, the material balance estimate and the strength of resistance needles as auxiliary criteria.The steps of this method are as follows: Firstly, the information for anodic effect prediction , Including the slot resistance sampling, the low resistance of the slot filter, the slot slope resistance and cumulative slope calculation, slot resistance pin strength calculation and material balance estimation (that is estimated within a certain amount of time accumulated in the electrolytic tank and the amount of time the theory of aluminum Then the anode effect forecast is made based on this, that is, the reliability of the current forecast anode effect is determined by the resistance slope and the cumulative slope reasoning in the current analysis cycle. Based on the trend of the resistance intensity of the slot resistance and the material accumulation The reliability of the method is adjusted according to the value of the deviation, and the process of the anodic effect prediction is processed according to the credibility value.The test results show that the success rate of the anode effect prediction is over 90%.