The penetration of renewable electricity promises an economic advantage for flexible operation of energy-intense processes. One way to achieve flexible operation is economic model predictive control (eNMPC), where an economic dynamic optimization problem is directly solved at controller level taking into account a process model and operational constraints. We apply eNMPC in silico to an air separation process with an integrated liquefier and liquid-assist operation. We use a mechanistic dynamic model as both controller model and plant surrogate. We conduct a closed-loop case study over a time horizon of 2 days with historical electricity prices and input disturbances. We solve the dynamic optimization problems in DyOS. Compared to the optimal steady-state operation, the eNMPC operating strategy gives a significant improvement of 14%. We further show that the eNMPC enables economic improvements similar to an idealized quasistationary scheduling. While the eNMPC provides control profiles qualitatively similar to those obtained from deterministic global optimization of quasistationary scheduling, the eNMPC satisfies the product purity constraints all the time whereas the quasistationary scheduling sometimes fails to do so. The eNMPC applies local optimization methods and achieves profiles similar to the scheduling solved using deterministic global optimization methods over the complete closed-loop simulation time horizon.