Jump to Main Content
Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization
- Wu, Xiao, Shen, Jiong, Wang, Meihong, Lee, Kwang Y.
- Energy 2020 v.196 pp. 117070
- algorithms, carbon dioxide, flue gas, neural networks, temperature
- This paper develops an intelligent predictive controller (IPC) for a large-scale solvent-based post-combustion CO₂ capture (PCC) process. An artificial neural network (NN) model is trained to represent the dynamics of the PCC process based on an in-depth behavior investigation of the process under different operating conditions. The resulting NN model can portray the PCC characteristics very well in terms of dynamic trend, response time and steady-state gain. An intelligent predictive controller is thus developed based on the NN model to track the desired CO₂ capture level and maintain the given re-boiler temperature, in which the particle swarm optimization (PSO) algorithm is applied to find the best future control sequence for the PCC process. A warm start scheme is proposed in the IPC to improve the quality of initial swarm in the PSO. Dynamic simulations to change CO₂ capture level set-point and flue gas flow rate are carried out on the PCC process. The results show that the IPC can adjust CO₂ capture level fast and significantly reduce the fluctuations in re-boiler temperature. It is concluded that the proposed IPC is helpful for flexible operation of the solvent-based PCC process.