Closed-loop control framework for optimal startup of cryogenic air separation units

Current volatile electricity market conditions incentivize the adaptation of the operation, including the startup, of cryogenic air separation units (ASUs) which are large consumers of electricity. Improvement in ASU startups using earlier proposed open-loop control strategies may not be fully realized in the presence of uncertainties and disturbances. This paper assesses the potential benefit of using a proposed closed-loop control framework to address uncertainty and disturbances. A rolling-horizon economic nonlinear model predictive control (ENMPC) approach is considered, for which strategies are proposed to improve computation time. Online parameter estimation is performed using a computationally efficient method that is easy to implement. Through the case studies presented, it is shown that the proposed framework outperforms the use of offline pre-computed optimal inputs in response to the disturbance and uncertainty considered. • Use of economic nonlinear MPC (ENMPC) framework for control of ASU startup. • Disturbance rejection and uncertainty mitigation in ASU startup. • Online parameter estimation via computationally efficient PI-based method. • Performance demonstrated through case studies of industrially relevant scenarios. • ENPMC outperforms use of offline pre-computed optimal inputs on profit recovery.