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 Anode Effect (AE) prediction method was developed. The method uses the slope of cell resistance, the accumulated slope of cell resistance as main inference facts, and uses the accumulated mass deviation and the intensity of fluctuation of cell resistance as supplementary ones. By data sampling and treating such as sampling and lowpass filtration of cell resistance, calculation of the slope of cell resistance and the accumulated slope of cell resistance,calculation of the intensity of fluctuation of cell resistance and the accumulated mass deviation, the method at first acquires the information for AE prediction, then uses the information for AE predliction with the following steps: firstly, determining AE-predicting reliability by inference with the slope of cell resistance and the accumulated slope of cell resistance at the present analytic period; secondly, adjusting AE-predicting reliability according to the change trend of the intensity of fluctuation of cell resistance and the value of the accumulated mass deviation; finally, treating with the course flags for AE prediction according to the value of AE-predicting reliability. The testing results showed that the chances of success of AE prediction exceeded 90%.
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