Reduced order modeling for compressible cake filtration processes using proper orthogonal decomposition

Abstract

This work aims to increase the computational efficiency of a complex mathematical cake-filtration model with strong nonlinearities representing cake compression. To this end, we employ a hybrid data driven approach using the technique of proper orthogonal decomposition. Hereby, a few sample simulations from the initial system of partial differential equations are used as the foundation to find optimal, globally defined basis functions, which in return offer the possibility to build a reduced-order model. In summary, the dimension of the reduced order model is diminished by ≈98% compared to the full order model, which translates to a net decrease of ≈90 % computational time needed to solve a benchmark optimization problem. This significant numerical speed-up offers the possibility to use the reduced order model in further advanced process control and optimization methods.