###### Abstract  
To improve the accuracy of calculating the aluminum electrolysis pre-close wind volume, this study focused on optimizing the two main factors that influence its magnitude: the aluminum output speed and the pre-close wind volume coefficient. First, the Unscented Kalman Filter (UKF) algorithm was used to estimate the aluminum output speed, and its application in real production was verified through simulation experiments. The results demonstrate that UKF provides more accurate speed estimates when handling the non-linear dynamic system of aluminum electrolysis. When there was a sudden change in speed, the UKF achieved a relative error of only 0.0373%, significantly lower than the 2.52% error of the traditional Kalman Filter (KF). At the same time, the UKF exhibited a shorter runtime in the simulation. Additionally, this research introduces a self-correction mechanism for the pre-close wind volume coefficient for the first time. By dynamically adjusting the parameter based on aluminum output deviations and applying the Average Filter (AF) to improve the compensation accuracy, the pre-close wind volume coefficient can be precisely calculated. The combination of these methods significantly enhances the accuracy and robustness of pre-close wind volume calculations, providing solid theoretical foundations and the technical support needed to achieve high-precision aluminum output control. pre-close wind volume; UKF; AF; electrolytic aluminum  
\"pre-close wind volume\" compensates for the overshoot, aiming to match it precisely. The net aluminum output is calculated as the difference between the ladle weight when the valve is activated and the final ladle weight. This is a real-time updated value. When the sum of the pre-close wind volume and net output equals or exceeds the planned output, the PLC closes the valve. However, controlling this process is challenging due to the complexity of accurately calculating the pre-close wind volume. This requires two key parameters: the real-time extraction flow rate at air-off and the pre-close wind volume coefficient. The flow rate, derived from the net aluminum output, is subject to significant variability and measurement noise; thus, filtering must be performed in order to improve accuracy. Additionally, the pre-close wind volume coefficient, traditionally determined empirically, must dynamically adapt to changes in the flow rate to maintain precision. Faster flow rates require larger coefficients, while slower rates necessitate smaller ones. As discussed earlier, the rapid and accurate calculation of aluminum extraction flow rates is indispensable in determining the pre-close wind volume. However, the electronic scale is a hanging scale, and during its operation, the inevitable swinging of the lifted package generates observational noise. Moreover, the aluminum tapping process in the electrolytic aluminum cell is inherently nonlinear [7]. As a result, the aluminum tapping process exhibits strong nonlinearity and randomness, making it difficult for traditional computational methods, such as the standard Kalman Filter (KF), to simultaneously meet the accuracy and computational speed required to predict the aluminum flow rate. The  
Figure 1: Electrolytic aluminum process flowchart. Extended Kalman Filter (EKF), which linearizes nonlinear systems using the Taylor expansion, performs better with mildly nonlinear systems [8; 9]. However, its linearization process introduces errors, especially in highly nonlinear systems. In contrast, the Unscented Kalman Filter (UKF) directly addresses nonlinearity using the Unscented Transformation (UT) [10; 11], avoiding derivative calculations and reducing the number of linearization errors. This enables the UKF to capture nonlinear dynamics more accurately. Additionally, while Particle Filters (PFs) theoretically achieve high precision in estimating nonlinear, non-Gaussian systems, they are computationally expensive, especially when there is a large number of particles [12; 13].