Optimal control of carbon capture system in coal‐fired power plant based on fusion of CNN‐LSTM and EMPC for MOGWO optimization

Coal‐fired power plants remain a major source of global CO 2 emissions, posing urgent challenges for climate mitigation. This study proposes an AI‐enabled optimal control strategy for solvent‐based post‐combustion CO 2 capture (PCC), aiming to reduce energy consumption and enhance environmental performance. A deep learning model combining convolutional neural networks and long short‐term memory (CNN‐LSTM) is developed to predict multi‐step CO 2 capture rates. This model is integrated with a multi‐objective grey wolf optimizer (MOGWO)‐based economic model predictive controller (EMPC), which dynamically adjusts key operating variables. Simulation results demonstrate that the proposed control scheme achieves a 12.4% reduction in capture cost and a 10.6% decrease in energy consumption, while maintaining a high CO 2 capture efficiency of 95.3%. These improvements not only optimize process economics while significantly lowering the environmental impact of carbon capture systems. These improvements contribute directly to carbon mitigation, offering a promising pathway for cleaner and more sustainable operation of coal‐based power generation under dynamic industrial conditions.