In the aluminum electrolysis production process, the traditional cell control method based on cell voltage and series current can no longer meet the goals of energy conservation, consumption reduction, and digital-intelligent transformation. Therefore, a new digital cell control technology that is centrally dependent on various process parameters has become an urgent demand in the aluminum electrolysis industry. Among them, the real-time online measurement of alumina concentration is one of the key data points for implementing such technology. However, due to the harsh production environment and limitations of current sensor technologies, hardware-based detection of alumina concentration is difficult to achieve. To address this issue, this study proposes a soft-sensing model for alumina concentration based on a long short-term memory (LSTM) neural network optimized by a weighted average algorithm (WAA). The proposed method outperforms BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention models in terms of predictive accuracy. In comparison to LSTM models optimized using the Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Optuna, Tornado Optimization Algorithm (TOC), and Whale Migration Algorithm (WMA), the WAA-enhanced LSTM model consistently achieves significantly better performance. This superiority is evidenced by lower MAE and RMSE values, along with higher R² and accuracy scores. The WAA-LSTM model remains stable throughout the training process and achieves the lowest final loss, further confirming the accuracy and superiority of the proposed approach.

To address the challenge of the real-time monitoring of alumina concentrations during the production process, this paper employs a Deep Belief Network (DBN) within the framework of deep learning to predict alumina concentration. Additionally, the improved Grey Wolf Optimizer (IGWO) is utilized to optimize key parameters of the DBN model, including the number of hidden layer nodes, reverse iteration count, and learning rate. An IGWO-DBN hybrid model is then constructed and compared against DBN models optimized by other techniques, such as the Sparrow Search Algorithm (SSA) and Particle Swarm Optimization (PSO), to evaluate the predictive performance. The comparative analysis reveals that, in terms of predictive accuracy, the IGWO-DBN model outperforms both the SSA-DBN and PSO-DBN models. Specifically, it achieves lower root mean square errors (RMSE) and mean absolute errors (MAE), alongside a higher coefficient of determination (R²). Furthermore, the IGWO-DBN model exhibits a faster convergence rate and a lower final convergence value, indicating superior generalization ability and robustness. Furthermore, the IGWO-DBN model not only demonstrates significant advantages in prediction accuracy for alumina concentration but also substantially reduces model training time through its efficient parameter optimization mechanism. The successful implementation of this model provides robust support for the intelligent and refined management of the aluminum electrolysis industry, aiding enterprises in reducing costs, improving production efficiency, and advancing the green and sustainable development of the industry.