Abstract:
Gasification temperature is the most important operating parameter of entrained flow gasifier. However, reliable methods to long-term measure the gasification temperature of coal gasifier units remain elusive. In order to monitor the operation state of entrained flow gasifier in real time and ensure the safe and stable operation of gasification system, measurable data including gasifier cooling system and reaction system are collected. The outlet temperature of gasifier was predicted by a theoretical calculation model and BP neural based on the genetic algorithm model (GABP). The results were compared to those obtained from industrial measurement. The outlet temperature of gasifier can be obtained by the theoretical calculation of quench system, while the accuracy and stability of the prediction results are unsatisfactory due to the low sensitivity of measurement parameters. GABP neural network model greatly improves the prediction performances. Based on the gasification chamber parameters, the prediction error is large due to the fluctuation of coal water slurry flow rate and a lack of coal property data. Using quench system parameters as the input of GABP neural network greatly improves the prediction accuracy, and the absolute value of the prediction error is less than 15 K. Both the train set and verification set have excellent prediction results, and the average absolute error of GABP model with quench system parameters as input is about 5 K. The GABP model has good performances in the face of complex working conditions. When predictions are carried out under different conditions, the results under steady and variable coal loads are produced with good prediction precision and stability, which meet the requirements of online monitoring of gasifier temperature.