Tow-Layer Data-Driven Model Predictive Control for Coal Blending System of Coking Process

In the coal coking process, coal blending refers to the procedure of mixing different individual coal types from storage bins in specific proportions. The system controls the mass flow rate of each coal type to maintain the blending ratio, which is essential for enhancing production efficiency. Due to the uneven coal quality in storage bins, blockage at feeding ports and mechanism of coal flow monitoring, the coal blending system becomes a time-delayed and uncertain system. Existing control schemes fail to account for the impact of discharge port blockages on the maximum coal flow output. To address this, this paper proposes a two-layer data-driven model predictive control (MPC) framework. Model parameters are updated by using historical data and coal flow setpoints are recalibrated under constraints in the upper layer, while an MPC based on input-mapping method is solved in the lower layer to decrease the effects of the unknown but bounded uncertainties in system parameters. MATLAB simulations validate the effectiveness of the proposed method.