1,429 research outputs found
Bayesian estimation and reconstruction of marine surface contaminant dispersion
Discharge of hazardous substances into the marine environment poses a
substantial risk to both public health and the ecosystem. In such incidents, it
is imperative to accurately estimate the release strength of the source and
reconstruct the spatio-temporal dispersion of the substances based on the
collected measurements. In this study, we propose an integrated estimation
framework to tackle this challenge, which can be used in conjunction with a
sensor network or a mobile sensor for environment monitoring. We employ the
fundamental convection-diffusion partial differential equation (PDE) to
represent the general dispersion of a physical quantity in a non-uniform flow
field. The PDE model is spatially discretised into a linear state-space model
using the dynamic transient finite-element method (FEM) so that the
characterisation of time-varying dispersion can be cast into the problem of
inferring the model states from sensor measurements. We also consider imperfect
sensing phenomena, including miss-detection and signal quantisation, which are
frequently encountered when using a sensor network. This complicated sensor
process introduces nonlinearity into the Bayesian estimation process. A
Rao-Blackwellised particle filter (RBPF) is designed to provide an effective
solution by exploiting the linear structure of the state-space model, whereas
the nonlinearity of the measurement model can be handled by Monte Carlo
approximation with particles. The proposed framework is validated using a
simulated oil spill incident in the Baltic sea with real ocean flow data. The
results show the efficacy of the developed spatio-temporal dispersion model and
estimation schemes in the presence of imperfect measurements. Moreover, the
parameter selection process is discussed, along with some comparison studies to
illustrate the advantages of the proposed algorithm over existing methods
Kalman Filtering and its Application to On-Line State Estimation of a Once-Through Boiler
This thesis contributes to non-linear continuous-discrete Kalman filtering of multiplex systems through the development of two main ideas, namely, integration of the unscented transforms with linearly implicit methods and incorporation of simulation errors in the state estimation problem. The newly developed techniques are then applied to the technically relevant problem of state estimation on the main components of a utility boiler. State estimators in industrial systems are used as soft-sensors in monitoring and control applications as the most cost effective and practical alternative to telemetering all variables of interest. One such example is in utility boilers where reliable and real-time data characterising its behaviour is used to detect faults and optimise performance. With respect to the state-of-the-art, state estimators display limitations in real-time applications to large-scale systems. This motivates theoretical developments in state estimation as a first part in this thesis. These developments are aimed at producing more practical and efficient algorithms in non-linear continuous discrete Kalman filtering for stiff large-scale industrial systems. This is achieved using two novel ideas. The first is to exploit the similarities between the extended and unscented Kalman filter in order to estimate the Jacobian required for linearly implicit schemes, thereby tightly coupling state propagation and continuous-time simulation. The second is to account for numerical integration error by appending a stochastic local error model to the system's stochastic differential equation. This allows for coarser integration time steps in systems that are otherwise only suited to relatively small step sizes, making the filter more computationally efficient without lowering its potential to construct accurate estimates. The second part of this thesis uses these algorithms to demonstrate the feasibility of on-line state estimation on the main components of a once-through utility power boiler that require in excess of a hundred state variables to capture its behaviour with adequate fidelity. Two separate models of the boiler are developed, a MATLAB® and a Flownex® model, comprising the economiser, evaporators, reheaters, superheaters and furnace. The mathematical MATLAB® model is better suited to real-time execution and is used in the filter. The more sophisticated model is based on a commercial thermal-hydraulic simulation environment, Flownex® , and is used to validate the mathematical modelling philosophies and construct filter observation data. After validating the performance of the filter against ground-truth data provided by the Flownex® model, the filter is demonstrated on historical plant data to illustrate its utility
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