Reliability assessment of key equipment for coal gasification using artificial intelligence technology

To address the gap in quantitatively modeling dynamic failure mechanisms for Gasifier lock bucket valve system reliability, this study proposes an innovative method: using backpropagation (BP) neural network to optimize the prior data of dynamic Bayesian network (DBN). Firstly, based on the empirical formula for the number of hidden layer neurons, the original DBN model of the system is adapted to a structurally adaptive BP neural network to calibrate its prior parameters，and the correspondence between the prior distribution of DBN and the input-output functions of the BP network is established. Subsequently, utilizing the core characteristics of BP network, iterative optimization of DBN prior data is achieved through continuous learning of the operating performance of the lock bucket valve system. Next, the optimized DBN model is subjected to dynamic system reliability evaluation using bidirectional inference analysis. The results show that in the positive prediction, the reliability of the system after 300 hours of operation without considering maintenance is only 0.047, which can be improved to 0.302 after incorporating maintenance factors. The reliability of the optimized system is lower than before optimization, and the gap gradually widens over time. Reverse reasoning clearly identifies the weak links in the system as high-pressure coal powder flushing, adhesion between ball seats, internal deformation and wear. Targeted preventive measures can improve the reliability of the system and extend its service life.