Practical issues in distributed parameter estimation: Gradient computation and optimal experiment design

Point, Nicolas; Wouwer, Alain Vande; Remy, M.

doi:10.1016/0967-0661(96)00169-4

Cited by 30 publications

(12 citation statements)

References 12 publications

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“…The sensitivity analysis (Point et al, 1996;Vande Wouwer et al, 2000) allows one to determine the number of elution peaks and the associated concentrations needed to identify the parameters with good accuracy. The identifiability of the kinetic, LDF and ED models are also compared.…”

Section: Sensitivity Analysismentioning

confidence: 99%

A systematic approach to SMB processes model identification from batch experiments

Grosfils

Levrie

Kinnaert

et al. 2007

Chemical Engineering Science

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Section: Sensitivity Analysismentioning

confidence: 99%

A systematic approach to SMB processes model identification from batch experiments

Grosfils

Levrie

Kinnaert

et al. 2007

Chemical Engineering Science

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“…A related optimality criterion was given by Point et al (1996), who investigated maximization of the Gram determinant being a measure of the independence of the sensitivity functions evaluated at sensor locations. The authors argue that such a procedure guarantees that the parameters are identifiable and the correlation between the sensor outputs is minimized.…”

Section: Optimal Sensor Location For Parameter Identificationmentioning

confidence: 99%

Sensor network scheduling for identification of spatially distributed processes

Uciński

2010

2010 Conference on Control and Fault-Tolerant Systems (SysTol)

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The work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A simple node activation strategy is discussed for the design of a sensor network deployed in a spatial domain that is supposed to be used while detecting changes in the underlying parameters which govern the process evolution. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset of them can be selected because of the cost constraints. As a suitable performance measure, the Ds-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. The problem is then formulated as the determination of the density of gauged sites so as to maximize the adopted design criterion, subject to inequality constraints incorporating a maximum allowable sensor density in a given spatial domain. The search for the optimal solution is performed using a simplicial decomposition algorithm. The use of the proposed approach is illustrated by a numerical example involving sensor selection for a two-dimensional diffusion process.

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“…These are linearly dependent if and only if the Gram determinant gz; r vanishes, i.e., [8] The main goal of this study is to determine the experimental conditions that lead to the linear independence of all the sensitivity functions and to ensure that the sensors are located in the spatial region where the sensitivity takes the largest absolute values.…”

Section: Optimum Sensor Locationsmentioning

confidence: 99%

“…In addition to this, the sensitivity functions can provide relevant information for determining optimum experimental conditions such as those reported by Point et al [8]. The following normalized sensitivity functions were adopted:…”

Section: Sensitivity Studiesmentioning

confidence: 99%

“…Hence, it is crucial to assure that the set of experimental data at hand possesses the necessary information for a reliable estimation of the parameters. A possible solution to this problem is provided by the method proposed by Point et al [8] which consists in verifying the linear independence of the sensitivity functions. This methodology requires the computation of a determinant of the so-called sensitivity matrix gz; r, similar to the Gram determinant, in various radial and axial positions of the bed.…”

Section: Introductionmentioning

confidence: 99%

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An analytical and experimental study of heat transfer in fixed bed

Ferreira

Castro

Rodrigues

2002

International Journal of Heat and Mass Transfer

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Heat transfer in a ®xed-bed packed with polymeric adsorbent particles, percolated by a liquid phase¯owing upward at 60°C or downward at 20°C, is experimentally studied in a column 9 cm in diameter and 1 m in length. The transient temperature pro®les were measured axially and radially in the column. Sensitivity function calculations enabled to prove that the thermal parameters could be estimated from the experimental data and also to obtain information about the optimum location of the sensors. Experimental transient temperature responses were compared to those predicted by one-dimensional (1-D) and two-dimensional ( 2-D) pseudo-homogeneous (PH) models. By ®tting the 2-D model solution to the experimental responses, values for the thermal parameter, eective radial and axial conductivities and the wall heat transfer coecient were found. Ó

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Practical issues in distributed parameter estimation: Gradient computation and optimal experiment design

Cited by 30 publications

References 12 publications

A systematic approach to SMB processes model identification from batch experiments

A systematic approach to SMB processes model identification from batch experiments

Sensor network scheduling for identification of spatially distributed processes

An analytical and experimental study of heat transfer in fixed bed

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