Prediction of syngas compositions in shell coal gasification process via dynamic soft-sensing method

Syngas compositions is very important parameter in Shell coal gasification process, and the real-time measurement used on-line instrument is still unrealistic. Because Shell coal gasification process has large pure lag and capacity lag, traditional static soft-sensing methods are difficult to achieve good effects. And therefore, a novel dynamic soft-sensing method based on impulses response template (IRT) for Shell coal gasification process, is developed in this paper. Actual operating data of Shell gasifier is used as training samples of dynamic soft-sensing model. Research results show that the proposed method provides better prediction reliability and accuracy than traditional static soft-sensing method, and supposed to have extensive application prospects in coal gasification processes.