Abstract
This paper develops a two-level control structure for commercial-scale coal-fired power plant integrated with post-combustion carbon capture (CFPP-PCC). In high-level scheduler, an economic objective function with the consideration of grid demand, price conditions and system constraints is developed. Deep belief network is utilized to develop the steady-state model for CFPP-PCC process. Intelligent scheduling of electricity generation and capture level is then implemented using Bayesian optimization. Given the optimal setpoints, model predictive controller (MPC) is applied to achieve flexible operation in the low-level supervisory control. The simulation results show that the proposed control structure specifies an economically attractive operation profile for the CFPP-PCC process.
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