Kinetic Intersections as a Source of Information on Rate Coefficients

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Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview

Disturbance Observer has been one of the most widely used robust control tools since it was proposed in 1983. This paper introduces the origins of Disturbance Observer and presents a survey of the major results on Disturbance Observer-based robust control in the last thirty-five years. Furthermore, it explains the analysis and synthesis techniques of Disturbance Observer-based robust control for linear and nonlinear systems by using a unified framework. In the last section, this paper presents concluding remarks on Disturbance Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure

Inverse Dynamics and Control for Nuclear Power Plants

A new nonlinear control technique was developed by reformulating one of the “inverse Problems” techniques in mathematics, namely the reconstruction problem. The theory identifies an important concept called inverse dynamics which is always a known property for systems already developed or designed. Accordingly, the paradigm is called “reconstructive inverse dynamics” (RID) control. The standard state-space representation of dynamic systems constitutes a sufficient foundation to derive an algebraic RID control law that provides solutions in one step computation. The existence of an inverse solution is guaranteed for a limited dynamic space. Outside the guaranteed range, existence depends on the nature of the system under consideration. Derivations include adaptive features to minimize the effects of modeling errors and measurement degradation on control performance. A comparative study is included to illustrate the relationship between the RID control and optimal control strategies. A set of performance factors were used to investigate the robustness against various uncertainties and the suitability for digital implementation in large scale-systems. All of the illustrations are based on computer simulations using nonlinear models. The simulation results indicate a significant improvement in robust control strategies. The control strategy can be implemented on-line by exploiting its algebraic design property. Three applications to nuclear reactor systems are presented. The objective is to investigate the merit of the RID control technique to improve nuclear reactor operations and increase plant availability. The first two applications include xenon induced power oscillations and feed water control in conventional light water reactors. The third application consists of an automatic control system design for the startup of the Experimental Breeder Reactor-II (EBR-II). The nonlinear dynamic models used in this analysis were previously validated against available plant data. The simulation results show that the RID technique has the potential to improve reactor control strategies significantly. Some of the observations include accurate xenon control, and rapid feed water maneuvers in pressurized water reactors, and successful automated startup of the EBR-II. The scope of the inverse dynamics approach is extended to incorporate artificial intelligence methods within a systematic strategy design procedure. Since the RID control law includes the dynamics of the system, its implementation may influence plant component and measurement design. The inverse dynamics concept is further studied in conjunction with artificial neural networks and expert systems to develop practical control tools

Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems

Modelling of faults for chemical batch reactor using artificial neural network and fuzzy logic [TP1-1185].

Setiap proses kimia cenderung untuk mengalami kegagalan. Situasi ini memaksa industri dan penyelidik mencari teknik bersesuaian bagi mengesan kegagalan secepat yang mungkin. Every chemical processes prones to failure. This situation enforces the researchers and industrial to find the appropriate techniques to detect a process failure as early as possible