A Study on a Salt Solution Concentration Prediction Method Based on CNN-LSTM-Attention

The rapid and accurate analysis of ion concentrations in mixed salt solutions is a critical aspect of utilizing salt lake chemical resources. To explore an efficient and non-destructive detection method, this study proposes a deep learning model that fuses a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and an Attention mechanism for the prediction of salt solution concentrations using Near-Infrared Spectroscopy (NIRS). First, single-component and two-component mixed salt solution samples of NaCl, KCl, and MgCl<sub>2</sub> were prepared, and their near-infrared spectral data were collected. After applying Savitzky-Golay smoothing and derivative preprocessing to the spectra, a CNN-LSTM-Attention prediction model was constructed. A comparative analysis was conducted against common models such as Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF), and an ablation study was performed to analyze the contribution of each deep learning module. The results show that for single-component salt solutions, the proposed model's performance is comparable to that of the high-performing SVR and RF models. In complex mixed solutions with severe spectral overlap, the CNN-LSTM-Attention model demonstrated significant superiority, with its prediction accuracy surpassing all traditional baseline models across all mixed datasets, achieving a coefficient of determination (R<sup>2</sup>) as high as 0.973. The study concludes that the proposed CNN-LSTM-Attention model can effectively address the challenge of spectral overlap, demonstrating the potential of using deep learning for the quantitative analysis of complex mixture systems via near-infrared spectroscopy.