Spatio-temporal prediction of temperature in fluidized bed biomass gasifier using dynamic recurrent neural network method

Abstract

• Long-short term memory model is proposed to predict temperatures in fluidized bed gasifier. • The proposed model is validated with pilot-scale plant data resolved in space and time. • Multi-step ahead temperature progressions of freeboard, fluidized bed and outlet gas, are predicted. • Gated recurrent unit and long short-term memory models are compared. In this study, a long short-term memory (LSTM) based dynamic recurrent neural network model is proposed for multi-step ahead temperature predictions in a pilot-scale fluidized bed biomass gasifier (FBG). The LSTM model predicts not only the temporal but also the spatial distribution of temperature by considering the temperature of each region of the FBG (fluidized bed, freeboard and outlet gas) as a separate target parameter. The proposed model is validated by comparing simulation data with experimental observations acquired during operation of the FBG. The validation results reveal that the proposed LSTM model is capable of accurately (MAE<6) predicting 1-min-ahead temperature of all the FBG regions. The LSTM model is further challenged for temperature predictions at farther future points (3 min and 5 min ahead) to test the prediction limits of the LSTM model. For 5 min ahead predictions, the proposed LSTM-based prediction model is also compared with other state-of-the-art dynamic neural network methods that include the standard recurrent neural network (S-RNN) and its advanced variant, the gated recurrent unit (GRU). The comparative findings for far future predictions show that LSTM has the highest accuracy, and also exhibit that GRU does not have universally faster convergence than LSTM.