An identification algorithm of switched Box-Jenkins systems in the presence of bounded disturbances

An identification algorithm of switched Box-Jenkins systems in the presence of bounded disturbances: An approach for approximating complex biological wastewater treatment models

Moradvandi, A. (TU Delft Sanitary Engineering; TU Delft Delft Center for Systems and Control)
Abraham, E. (TU Delft Water Resources)
Goudjil, Abdelhak (IPSA Institute of Polytechnic Science and Aeronautics)
De Schutter, B.H.K. (TU Delft Delft Center for Systems and Control)
Lindeboom, R.E.F. (TU Delft Laboratory Water Management)

Delft Center for Systems and Control

2024

This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for approximating complex dynamical models of biological wastewater treatment processes. We discuss the adaptation of these processes to the SBJ framework, showing the model's generality and flexibility as a class of switched systems that can offer accurate predictions for complex and nonlinear dynamics. This approach of modeling enables real-time data reconciliation from experiments and allows the design of model-based control strategies. Through the extension of the Outer Bounding Ellipsoids (OBEs) algorithm, the paper introduces an online two-stage parameter identification algorithm that effectively handles bounded disturbances for SBJ models. Using the OBE method relaxes the stochastic assumptions on disturbances, which may not be satisfied in practice, particularly for biological and environmental fluctuations. The proposed decomposed OBE algorithm separately identifies the switching patterns and parameters of linear submodels, conducting parameter identification in two distinct phases for input/output and disturbance/output submodels. The efficacy of this approach is shown via simulation results validated against both ADM1 and PBM models, demonstrating the proposed algorithm's capability to accurately predict outputs from different biological wastewater treatment models.

Bioprocess modeling
Data-driven modeling
Switched systems
System identification
Wastewater treatment modeling

http://resolver.tudelft.nl/uuid:ac5ef55c-d7c8-4fbc-a034-135527bab5ab

https://doi.org/10.1016/j.jwpe.2024.105202

2214-7144

Journal of Water Process Engineering, 60

Institutional Repository

journal article

© 2024 A. Moradvandi, E. Abraham, Abdelhak Goudjil, B.H.K. De Schutter, R.E.F. Lindeboom