Observer switching strategy for enhanced state estimation in CSTR networks

Accurate state estimation in nonlinear chemical reactors is essential for advanced monitoring and control, yet sensor limitations and model uncertainties pose significant challenges. This paper presents a novel multi-observer switching framework that operates four state estimators in parallel-Extended Luenberger Observer, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-and dynamically selects the most reliable estimate at each sampling instant. The switching mechanism employs a composite cost function combining L2 and L infinity norms of the output estimation error: the L2 component captures sustained deviations while the L infinity component enables rapid response to transient peaks, together providing robust adaptation to changing operating conditions. The framework is validated on continuous stirred-tank reactor networks with up to three reactors in series, under partial global observability where only downstream concentrations and temperatures are measured. Monte Carlo simulations demonstrate that the switching observer achieves superior estimation accuracy compared to individual estimators while maintaining computational efficiency suitable for realtime implementation. Parametric robustness analyses confirm reliable performance under kinetic and thermal uncertainties. The proposed approach offers a scalable solution for state estimation in complex chemical processes, with potential applications in fault detection and model predictive control.