Development of a neural network model predictive controller for the fluidized bed biomass gasification process

In this study, an advanced controller for the fluidized bed biomass gasification (FBG) process is proposed. The controller is based on the model predictive control method, utilizing a long short-term memory neural network to accurately predict the controlled variable in the FBG process. Then, employing a gradient-based optimization algorithm, it optimizes the inputs to achieve the desired control objective. The controller specifically aims for temperature regulation in three spatial regions (fluidized bed, freeboard and outlet), using primary air, secondary air, and biomass flow rates as input variables. Open-loop simulations are used to fine-tune the controller's parameters (prediction and control horizon). For unbiased controller testing, a computational fluid dynamics (CFD) model of the FBG process is also developed, which is integrated with the controller in a closed-loop system, simulating real-process feedback for performance evaluation. The proposed controller effectively maintains temperatures (steady state error < 1.5 %) at desired set points (800–900 °C) with a short prediction horizon of 24 time steps, reducing response time to under 5 s, making it suitable for real-time FBG process control.