Data-Driven Modeling of PFR Kiln

Modeling the different dynamics in parallel flow regenerative kilns for the production of quicklime is one of the steps towards obtaining the digital twins of the kilns. This paper shows a data-driven modeling approach based on the dynamic mode decomposition algorithm leveraged with a maximum likelihood method that gives linear and discrete-time models of the system. This allows capturing the transient dynamics of the process for the design of subsequent observers of the unknown dynamics, for the implementation of anomaly detection algorithms and eventual control and optimization of the manufacturing process.