摘要：AbstractThis work investigates economic optimisation of an energy-intensive amine regeneration process in a post-combustion CO2capture plant, subject to a minimum CO2capture ratio over 24 hours. A Dynamic Real-Time Optimisation algorithm is implemented as a single-level Nonlinear Model Predictive Control scheme by utilising the infeasible soft-constraint method to include economic objectives in an industrial tracking NMPC package. A time-varying price of electricity is exploited to enhance cost minimisation by adjusting the regeneration according to the peaks of the price curve. This flexible mode of operation is compared to a fixed mode of operation with constant amine regeneration. Simulation results indicate a cost reduction of 10.9% for a reference accumulated capture ratio of 91%. Robustness of the optimisation to abrupt changes in CO2feed composition and electricity price is also investigated in simulations and results are promising. The NMPC controller uses a reduced, control-oriented model of the capture plant developed from first principle conservation laws with control volumes to discretize the model equations in space.
关键词：Keywordsdynamic real-time optimisationnonlinear model predictive controlmodel-based optimisationcontrolCO2management
