Variable-horizon economic MPC for cyclic industrial air dryers using hybrid models and state estimation

Abstract

This paper introduces a variable horizon economic model predictive control (EMPC) framework for a twin bed industrial desiccant air drying plant. Hybrid mechanistic and machine learning models are employed to simulate the drying and regeneration processes, providing a realistic representation of system dynamics. A moving horizon state estimation framework, integrated with hybrid models, is utilized to estimate the adsorbed water content in the beds. Based on these estimated values, an algorithm is implemented to estimate the end time of the regeneration process. The EMPC framework uses this end time as the prediction horizon to optimize the manipulated variable trajectories for the drying process. Simulation results show that the proposed EMPC reduces cooling-energy consumption by increasing the average temperature of the inlet wet air by approximately 2 degrees C. At the same time, it improves system performance by increasing the moisture adsorbed in the bed by approximately 6-10%. Under these new operating conditions, the overall energy consumption is estimated to decrease by about 6.5%, thereby enhancing process profitability.