Intelligent Prediction of Ammonium Nitrate Production Under Industrial Big Data: A Deep Learning Model Integrating Adaptive Attention with LSTM

The increasing complexity associated with ammonium nitrate production within the global chemical industry highlights the necessity for advanced predictive methods capable of effectively utilizing the extensive datasets inherent to industrial applications. Given the intricate and dynamic nature of these processes, accurate production forecasting is crucial for optimizing operational efficiency, ensuring safety, and meeting production targets. Long Short-Term Memory (LSTM) networks, known for their proficiency in capturing long-term dependencies within time-series data, have become a prominent solution for addressing such prediction challenges. However, traditional LSTM models, which employ static attention mechanisms, are insufficient for capturing the multifaceted dynamics present in ammonium nitrate production data.