Dynamic Risk Assessment of the Coal Slurry Preparation System Based on LSTM-RNN Model

As the core technology of clean and efficient utilization of coal, coal gasification technology plays an important role in reducing environmental pollution, improving coal utilization, and achieving sustainable energy development. In order to ensure the safe, stable, and long-term operation of coal gasification plant, aiming to address the strong subjectivity of dynamic Bayesian network (DBN) prior data in dynamic risk assessment, this study takes the coal slurry preparation system—the main piece of equipment in the initial stage of the coal gasification process—as the research object and uses a long short-term memory (LSTM) model combined with a back propagation (BP) neural network model to optimize DBN prior data. To further validate the superiority of the model, a gated recurrent unit (GRU) model was introduced for comparative verification. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination are used to evaluate the generalization ability of the LSTM model. The results show that the LSTM model’s predictions are more accurate and stable. Bidirectional inference is performed on the DBN of the optimized coal slurry preparation system to achieve dynamic reliability analysis. Thanks to the forward reasoning of DBN in the coal slurry preparation system, quantitative analysis of the system’s reliability effects is conducted to clearly demonstrate the trend of system reliability over time, providing data support for stable operation and subsequent upgrades. By conducting reverse reasoning, key events and weak links before and after system optimization can be identified, and targeted improvement measures can be proposed accordingly.