Sensor network and soft sensor design for stable nonlinear dynamic systems

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Title: Sensor network and soft sensor design for stable nonlinear dynamic systems
Author: Singh, Abhay Kumar
Abstract: 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 .
URI: http : / /hdl .handle .net /1969 .1 /4326
Date: 2006-10-30

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Sensor network and soft sensor design for stable nonlinear dynamic systems. Available electronically from http : / /hdl .handle .net /1969 .1 /4326 .

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