Dissertação (Mestrado em Engenharia Elétrica) - Centro Universitário da Fei, São Bernardo do Campo, 2014

In chemical processes, online measurements of all the process variables and parameters required for process control, monitoring and optimization are seldom available. The use of soft sensors or observers is, therefore, highly significant as they can estimate unmeasured state variables from available process measurements. However, for reliable estimation by a soft sensor, the process measurements have to be placed at locations that allow reconstruction of process variables by the soft sensors. This dissertation presents a new technique for computing an optimal measurement structure for state and parameter estimation of stable nonlinear systems. The methodology can compute locations for individual sensors as well as networks of sensors where a trade-off between process information, sensor cost, and information redundancy is taken into account. The novel features of the approach are (1) that the nonlinear behavior that a process can exhibit over its operating region can be taken into account, (2) that the technique is applicable for systems described by lumped or by distributed parameter models, (3) that the technique reduces to already established methods, if the system is linear and only some of the objectives are examined, (4) that the results obtained from the procedure can be easily interpreted, and (5) that the resulting optimization problem can be decomposed, resulting in a significant reduction of the computational effort required for its solution. The other issue addressed in this dissertation is designing soft sensors for a given measurement structure. In case of high-dimensional systems, the application of conventional soft sensor or observer designs may not always be practical due to the high computational requirements or the resulting observers being too sensitive to measurement noise. To address these issues, this dissertation presents reduced-order observer design techniques for state estimation of high-dimensional chemical processes. The motivation behind these approaches is that subspaces, which are close to being unobservable, cannot be correctly reconstructed in a realistic setting due to measurement noise and inaccuracies in the model. The presented approaches make use of this observation and reconstruct the parts of the system where accurate state estimation is possible.

This thesis proposes a methodology for determining sensitivity of building energy consumption of HVAC systems to sensor error. It is based on a series of simulations of a generic building, the model for which is based on several typical input parameters. There are a total of eight scenarios considered in this simulation. The simulation tool was developed based on Excel. The control parameters examined include room temperature, cold deck temperature, hot deck temperature, pump pressure, and fan pressure. All of the parameters considered are varied in order to analyze the sensitivity of building energy consumption to their variation. In this tool, different operation schedules for equipment, occupancy, and lighting are considered. By changing each control parameter, the sensitivity of energy use to sensor error is simulated, a regression model is generated, and the energy consumption change is expressed as a function of sensor error and outside air percentage. Two applications of this methodology are presented in this thesis. One is a SDVAV system and the other is a DDVAV system. The outside air percentage changes the trend of the sensor error curve. After the sensitivity study is discussed, some recommendations regarding the calibration intervals of the sensors are given.

Recently, wireless sensor networks have attracted a lot of attention. Such environments may consist of many inexpensive nodes, each capable of collecting, storing, and processing environmental information, and communicating with base station nodes through wireless links. In this paper, we survey a fundamental problem in wireless sensor networks, the energy consumption problem, which reflects how well a sensor field is deployed. Therefore, a critical aspect of applications with wireless sensor networks is network lifetime. Furthermore, one of the fundamental issues in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. We formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a given parameter. In this paper, we propose an energy-efficient method based on Genetic Algorithms to deal with the deployment problem of wireless sensor networks such that it provides target-location and surveillance services.

In chemical processes, online measurements of all the process variables and parameters required for process control, monitoring and optimization are seldom available. The use of soft sensors or observers is, therefore, highly significant as they can estimate unmeasured state variables from available process measurements. However, for reliable estimation by a soft sensor, the process measurements have to be placed at locations that allow reconstruction of process variables by the soft sensors. This dissertation presents a new technique for computing an optimal measurement structure for state and parameter estimation of stable nonlinear systems. The methodology can compute locations for individual sensors as well as networks of sensors where a trade-off between process information, sensor cost, and information redundancy is taken into account. The novel features of the approach are (1) that the nonlinear behavior that a process can exhibit over its operating region can be taken into account, (2) that the technique is applicable for systems described by lumped or by distributed parameter models, (3) that the technique reduces to already established methods, if the system is linear and only some of the objectives are examined, (4) that the results obtained from the procedure can be easily interpreted, and (5) that the resulting optimization problem can be decomposed, resulting in a significant reduction of the computational effort required for its solution. The other issue addressed in this dissertation is designing soft sensors for a given measurement structure. In case of high-dimensional systems, the application of conventional soft sensor or observer designs may not always be practical due to the high computational requirements or the resulting observers being too sensitive to measurement noise. To address these issues, this dissertation presents reduced-order observer design techniques for state estimation of high-dimensional chemical processes. The motivation behind these approaches is that subspaces, which are close to being unobservable, cannot be correctly reconstructed in a realistic setting due to measurement noise and inaccuracies in the model. The presented approaches make use of this observation and reconstruct the parts of the system where accurate state estimation is possible.

Zugl.: Regensburg, Univ., Diss., 2004 / Hergestellt on demand

Since its invention in the early seventies, the Shack-Hartmann wavefront sensor has seen a wide variety of applications and has had great success in the fields of Adaptive Optics and Ophthalmology, where interferometry is usually impractical. Its application to optical shop testing has been less visible perhaps because shop environments can be manipulated to sufficiently remove vibration and turbulence to a degree that can support interferometry. However, with the growing need to accurately test aspheric optics, the Shack-Hartmann has an advantage; its dynamic range can be manipulated through the design of the lenslet array, rather than being directly tied to the wavelength of light and therefore lessen the need for expensive null optics.When the Shack-Hartmann is pushed to the limits of dynamic range, several issues must be dealt with. First, to reach the limits of dynamic range, those limits must be well understood. This dissertation presents a graphical approach to designing the Shack-Hartmann sensor that makes the trade-off between sensitivity and dynamic range, and accuracy and resolution intuitively clear. Next, the spots that once landed neatly in the region behind each lenslet, may now wander several lenslets away and the data reduction must be able handle this. This dissertation presents a novel and robust method for sorting these widely wondering spots and is shown to work in measurements of highly aspheric elements. Finally, in the high dynamic range regime, induced aberrations can severely limit the accuracy of the instrument. In this dissertation, these non-linear and measurement-dependent errors are studied in detail and a method of compensation is presented along with experimental results that illustrate the efficacy of the approach.

No description available.

Thesis(M.S.)--Auburn University, 2005. / Abstract. Includes bibliographic references.

Thesis (M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (ℓ. 51-55)