Economic Nonlinear Model Predictive Control for Flexible Operation of Air Separation Units

The integration of renewables into the portfolio of energy sources implies that dynamic operation of energy intensive processes, such as air separation, may give an economic advantage. Dynamic process operation can be achieved by applying economic nonlinear model predictive control (eNMPC). In this work, we present an in-silico case study of dynamic operation of an air separation process under fluctuating electricity prices. Using a day-ahead electricity price profile, an offline dynamic optimization (DO) problem is solved, which is used as an initial guess to start a fast update method deployed by the eNMPC. The eNMPC uses the same objective and constraints as the offline DO. However, the prediction horizon is shorter and current states and disturbances are taken into account. A first-principle air separation process model implemented in Modelica is used in all optimization problems. All optimization problems are solved using the DO framework DyOS. The process is flexibly operated by the application of the eNMPC, which leads to near-optimal economic process behavior during operation. This work demonstrates the contribution of model based process control to the integration of renewable energy sources in the supply chain of the process industry.