A novel SVD-UKFNN algorithm for predicting current efficiency of aluminum electrolysis.

The prediction of current efficiency in the aluminum electrolysis production process (AEPP) is critical for improving industrial production efficiency and product quality. However, the inherent dynamic nonlinearity and multivariable complexity of AEPP hinder the development of accurate current efficiency prediction models. To address these challenges, a novel singular value decomposition unscented Kalman filtering neural network (NSVD-UKFNN) is proposed to improve the prediction accuracy of current efficiency in the AEPP. First, a dynamic prediction model is constructed within the framework of the unscented Kalman filtering neural network (UKFNN), employing artificial neural network (ANN) to capture the complex characteristics of the system. Second, singular value decomposition (SVD) is integrated with the UKFNN to compute the square root of the prior matrix, thereby improving the model's numerical stability. Finally, the prediction variance of state variables is redefined as a cost function and optimized using the gradient descent method to reduce error accumulation during the computation process, enhancing the prediction robustness of proposed method. The experimental results show that the proposed NSVD-UKFNN reduces the mean absolute error (MAE) by 2.08 times and the sum of squared errors (SSE) by approximately 22.23 times compared to the baseline model.