| Transport-reaction processes represent a broad class of distributed parameter systems, which permeate both traditional and emerging process industries. These processes exhibit highly nonlinear behavior and are distinctly characterized by spatial variations, owing to the underlying phenomena such as diffusion, convection, and phase-dispersion, and are therefore naturally modeled by Partial Differential Equations (PDEs). Unlike the spatially homogeneous processes, the control problem for these distributed parameter systems requires the regulation of spatially distributed variables, such as temperature and concentration profiles, using spatially distributed control actuators and measurement sensors.;One of the central problems at the interface of process control and operations of these processes is the development of systematic methods for the diagnosis and handling of faults. The motivation for studying this problem stems in part from the vulnerability of automated industrial processes to faults, such as malfunctions in control actuators, measurement sensors and process equipment, as well as the increased emphasis placed on safety, reliability and profitability in the operation of industrial processes. It is well known that faults can lead to serious degradation in the system performance and may even lead to a complete breakdown of process operation, if not handled properly in the control system design. Compared with the extensive body of work on this problem for lumped parameter processes, results for spatially distributed processes remain limited. Major bottlenecks in the design of fault-tolerant control systems for distributed processes include the infinite-dimensional nature of these processes, as well as the complex nonlinearities and uncertainties that characterize their behavior.;The work in this dissertation presents a methodology for the development of an integrated model-based framework for the detection, isolation and management of faults in transport-reaction processes modeled by highly dissipative systems of PDEs. The framework brings together tools from infinite-dimensional systems, model reduction, nonlinear and robust control, as well as hybrid system theory. The central idea in this work is to exploit the underlying structural characteristics of various classes of distributed processes to design robust fault diagnosis algorithms and controller reconfiguration strategies on the basis of appropriate low-order models that are suitable for practical implementation and are amenable to rigorous closed-loop analysis. Using singular perturbations analysis, precise criteria were developed to implement the finite-dimensional framework on the infinite-dimensional system. Techniques for handling practical implementation issues such as plant-model mismatch, control constraints, and measurement sampling constraints in the design of the fault-tolerant control architecture are also developed. Finally, case studies involving applications to simulated models of representative transport-reaction processes are presented to illustrate the developed methods. |
