Gaussian Process Regression-Based Control of Solids Circulation Rate in Dual Fluidized Bed Gasification

In dual fluidized bed (DFB) gasification, the solids circulation rate is critical as it determines the amount of char and heat transported between the interconnected reactors. In DFB plants, multiple control inputs are typically available to control the solids circulation rate, resulting in an over-actuated system. We propose a modeling and control method based on Gaussian process regression, a technique that provides a measure of confidence in the model prediction. The availability of redundant control inputs is resolved by explicitly incorporating the prediction confidence information into the control algorithm to drive the process in regions of low model uncertainty. To address plant-model mismatches, a disturbance model is employed, and an extended Kalman filter is used to estimate both system and disturbance states, enabling offset-free tracking of constant references. Modeling and closed-loop simulation results for both a 100 kW and a 1 MW DFB gasification plant demonstrate the applicability of the method to different plants. Experimental results are presented for the 100 kW plant, demonstrating the successful control of the circulation rate by the proposed algorithm.