#5629. Hybrid Koopman model predictive control of nonlinear systems using multiple EDMD models: An application to a batch pulp digester with feed fluctuation

July 2026publication date
Proposal available till 22-05-2025
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Journal’s subject area:
Applied Mathematics;
Computer Science Applications;
Electrical and Electronic Engineering;
Control and Systems Engineering;
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Abstract:
In the pulping process, feed fluctuations often occur due to the supply of raw materials from various and unconventional sources, such as recycle to meet the increasing market demand for paper, and strict environmental regulations. However, such feed fluctuation can significantly extend the operating range of the process, which may cause differing local dynamics that degrade the performance of a single model-based controller. Motivated by these concerns, in this work, a hybrid Koopman model predictive control (KMPC) framework for a batch pulping process is developed to regulate the Kappa number and the cell wall thickness (CWT) of fibers to produce pulp with desired properties in the presence of feed fluctuations. Specifically, multiple local models are constructed by clustering the time-series operation data from the pulping process and identifying lifted state–space models for each cluster using extended dynamic mode decomposition (EDMD).
Keywords:
Batch pulp digester; Extended dynamic mode decomposition; Koopman-based model predictive control; Kraft pulping; Multiple model predictive control

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