Browsing by Author "Kristensen, Isabella"

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    State estimation and model-based fault detection in a submerged arc furnace
    (Stellenbosch : Stellenbosch University, 2023-12) Kristensen, Isabella; Louw, Tobias Muller; Bradshaw, Steven Martin; Stellenbosch University. Faculty of Engineering. Dept. of Chemical Engineering. Process Engineering.
    ENGLISH ABSTRACT: Model-based state estimators use noisy plant measurements and a process model to calculate accurate and timely estimates of the state variables for process monitoring, model-based fault detection, and model predictive control. The aim of this project was to perform model-based fault detection using state estimation in a complex chemical unit operation and compare the model-based fault detection to a datadriven technique under plant-model mismatch. A system observability analysis and fault detectability analysis was first conducted. The performance of the various nonlinear state estimation techniques, namely the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the particle filter (PF), and the moving horizon estimator (MHE), was then assessed, enabling the selection of appropriate state estimation techniques for model-based fault detection. Model-based fault detection was employed using the residuals generated from the state estimators followed by residual evaluation using PCA. The modelbased fault detection was compared to data-driven fault detection using PCA on the measurements and the effect of plant-model mismatch on the performance of model-based fault detection was investigated. A submerged arc furnace (SAF) for platinum group metal smelting was used as a case study to apply these techniques. The state observability analysis found the SAF system to be locally observable and the measured states to have a higher degree of observability than the unmeasured states. Upon implementation of the state estimation algorithms, the least observable states corresponded to states estimates with the largest estimation error. The fault detectability analysis identified all faults investigated to be structurally detectable. Upon implementation of model-based fault detection, it was concluded that the more structurally detectable a fault is, the better the fault detection performance. The investigation into state estimation in the SAF showed that the EKF, UKF, and PF display good estimation accuracy and fast computation times. The PF showed superior estimation accuracy under low process noise conditions and was selected for model-based fault detection. The EKF, being the most popular algorithm in literature and displaying fairly good estimation accuracy, was selected as the second method. The computational requirements of the MHE proved to be its greatest limitation. Investigations were carried out into reducing the computational load of the method using alternative singular perturbation SAF model with larger integration steps which halved the computational requirements. However, the computation times remained inappropriate for application in model-based fault detection. Lastly, this study found that the model-based fault detection using the PF residuals outperformed the model-based fault detection using the EKF residuals and the data-driven PCA method for detection of faulty conditions within the SAF process. Due to the sensitivity of the PF residuals resulting from the nature of the algorithm, this method showed exceptionally poor robustness to plant-model mismatch. The investigation then demonstrated that residual evaluation of the PF and EKF residuals in a reduceddimensional space using PCA improved the classification performance of the method when plant-model mismatch was present. However, when no modelling error is present, the classification of PF and EKF residuals showed the best performance in the original dimension space.