Time Series Risk Prediction Based on LSTM and a Variant DTW Algorithm: Application of Bed Inventory Overturn Prevention in a Pant-Leg CFB Boiler

Abstract

The pant-leg design is typical for higher capacity circulating fluidized bed (CFB) boilers because it allows for better secondary air penetration, maintaining good air-coal mixing and efficient combustion. However, the special risk, nominated as bed inventory overturn, remains a big challenge and it hinders the application of pant-leg CFB boilers. For a time series risk, it is critical to do the bed inventory overturn prevention to leave enough time for the adjustment. This paper proposed a new framework combing long short-term memory (LSTM) and dynamic time warping (DTW) methods to do the risk prediction. Pattern matching of data difference discrimination is employed for DTW algorithm, instead of the traditional Euclidean metric. The pattern matching has the merits in reduction of calculation and improvement of the adaptability to variables with different dimensions. After variable processing of the time series data by the variant DTW algorithm, the bed pressure drop prediction model is established based on the LSTM structure in this framework. Compared with some traditional prediction method, the framework in this paper has achieved superior results in the application of bed inventory overturn prevention.