Model predictive control

Control (management)

Engineering

Biology

Kraft process

Pulp (tooth)

Computer science

Offset (computer science)

Mathematics

Kappa number

Process engineering

Artificial intelligence

Kraft paper

Medicine

Programming language

Control theory (sociology)

Biological system

Pulp and paper industry

Pathology

AIChE Journal

This work presents the application of a Koopman operator approach to a batch pulp digester. To manufacture paper products with desired properties, it is essential to consider both macroscopic and microscopic attributes of pulp. However, the complexity of multiscale dynamics of pulping processes hinders proper control system design. Therefore, we utilize extended dynamic mode decomposition (EDMD), which is based on Koopman operator theory, to derive a global linear representation of a pulp digester. Then, we design an offset‐free Koopman‐based model predictive control (KMPC) system to regulate the Kappa number and cell wall thickness (CWT) of fibers at a batch pulp digester while compensating for the influence of plant‐model mismatch and disturbance during operation. The numerical experiments demonstrate that the linear state‐space model, obtained via EDMD, properly predicts the behavior of a batch pulp digester, and the designed offset‐free KMPC system successfully drives the Kappa number and CWT to set‐point values.

Application of offset‐free Koopman‐based model predictive control to a batch pulp digester