Research on a Safety Dynamic Early Warning Model for Hazardous Chemical Tankers under Multi-Parameter Spatio-Temporal Conditions

Existing early-warning systems for tank trucks transporting hazardous chemicals commonly rely on fixed thresholds and fail to characterize the dynamic coupling among multi-source spatio-temporal parameters, which leads to false and missed alarms. To address this limitation, a spatio-temporal multi-parameter dynamic safety ear ly-warning model was proposed based on a long short-term memory autoencoder and the multivariate state estimation technique (LSTM-AE-MSET). The model integrates multi-source sensor data with spatio-temporal attributes, including temperature, pressure, liquid level, inclination, and vibration, and incorporates road spatial information acquired via the global navigation satellite system (GNSS), the inertial measurement unit (IMU), and map application programming interfaces (API). The LSTM-AE extracts temporal features, captures long-term dependencies among variables, and maps high-dimensional inputs into a low-dimensional latent space; an MSET memory matrix is con-structed based on historical healthy-state data, and the early-warning thresholds are dynamically adjusted through state estimation and residual analysis. The Tennessee Eastman Process (TEP) dataset is used to evaluate the model. The dataset comprises 41 process measurement variables, 12 manipulated variables, and 21 fault modes, covering typical anomalies such as step disturbances, random variations, and valve stiction. After being trained on the TEP dataset, the model is transferred to a tank-truck operating scenario to identify anomalies such as abrupt temperature changes, pressure drift, and liquid-level oscillations under different spatio-temporal conditions. The results show that the model achieves an average coefficient of determination (R²) of 0.621 8 and a mean squared error of 4.301 4, with medians of 0.998 5 and 0.000 1, respectively, indicating excellent fitting performance under most normal operating conditions. Compared with the standalone MSET method, the proposed model increases the anomaly-detection recall from 57% to 88% and the F₁-score from 60% to 74%, improves the overall accuracy by 2 percentage points, and reduces the missed-alarm rate by 72%. The proposed method enhances the identification of abnormal op erating states in hazardous-chemical tank trucks and provides technical support for real-time dynamic early warning under complex operating conditions.