Electrical process tomography with known internal structures and resistivities

Heikkinen, Lasse; Vauhkonen, Marko; Savolainen, T.; Leinonen, Kari; Kaipio, Jari P.

doi:10.1080/174159701088027775

Cited by 23 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These internal structures can be, for example, an impeller drive shaft or a mixing paddle in process vessels and an assembly of fuel rods in nuclear reactor. The internal structures inside the object may results in difficulties in the image reconstruction by EIT especially when the high-resistive region is near the conductive internal structure or vice versa [13,14]. The so-called masking effect in the reconstructed image may be significant for the high-contrast case.…”

Section: Problem Definitionmentioning

confidence: 96%

“…The so-called masking effect in the reconstructed image may be significant for the high-contrast case. There are two ways to get around these difficulties; one is to use the internal structure as additional electrodes [13,15,16] and the other is to take it into account as a priori information in the inverse procedure [14]. However, all of the above approaches are for the case where the resistivity distribution inside the object is time-invariant for one classical frame.…”

Section: Problem Definitionmentioning

confidence: 98%

See 1 more Smart Citation

Dynamic electrical impedance imaging of binary-mixture fields with external and internal electrodes

Kim¹,

Kang²,

Kim³

et al. 2004

Inverse Problems in Science and Engineering

View full text Add to dashboard Cite

In the conventional electrical impedance tomography (EIT), the internal impedivity distribution, that is mixture distribution, is reconstructed based on the physical relationship between the known sets of injected currents through the electrodes and induced voltages on the surface of the domain of interest under the assumpution that the domain is stationary during the measurements. This study considers a dynamic electrical impedance imaging to binary-mixture systems with known internal structures to which additional electrodes can be attached. We attempt the utilization of the additional electrodes to enhance the recontruction. Also, we assume the domain is undergoing rapid transient, so the resistivity distribution in the domain of interest changes rapidly within the time taken to acquire a full set of independent measurement data. The dynamic EIT problem is treated as the nonlinear state estimation problem and the unknown state (resistivity) is estimated with the aid of extended Kalman filter in a minimum mean square error sense. Computer simulations for the two-dimensional object with abrupt changing resistivity distribution are provided to illustrate the reconstruction performance of the proposed algorithm.

show abstract

Section: Problem Definitionmentioning

confidence: 96%

Section: Problem Definitionmentioning

confidence: 98%

Dynamic electrical impedance imaging of binary-mixture fields with external and internal electrodes

Kim¹,

Kang²,

Kim³

et al. 2004

Inverse Problems in Science and Engineering

View full text Add to dashboard Cite

show abstract

“…In stationary EIT, it is assumed that the concentration distribution does not change during a set of current injections. The stationary reconstruction problem of EIT is an ill-posed inverse problem and the solution requires spatial prior information about the target [25,26]. In the case of process control, however, the target usually changes between consecutive current injections, and the state estimation approach is more suitable.…”

Section: Observation Modelmentioning

confidence: 99%

Optimal actuator placement for controlling concentration profiles via process tomography

Ruuskanen

Seppänen

Kaipio

2006

Inverse Problems in Science and Engineering

Self Cite

View full text Add to dashboard Cite

In this article, the optimal control of a chemical plant driven by an inverse problem is considered. More specifically, the problem of optimal actuator placement when controlling the concentration of a target substance in rapidly moving fluid is investigated. In this example, the actuators are injectors through which strong concentrate is injected into the fluid in order to obtain a desired concentration profile. The target substance is assumed to obey the stochastic convection-diffusion model and the process is monitored with electrical impedance tomography, the inverse problem of which is notoriously ill-posed even under stationary conditions. The associated reconstruction problem is formulated as a state estimation problem and is intertwined with the determination of the optimal time-varying controls. The model-based linear quadratic Gaussian control strategy is used in the determination of the control inputs. The controller settings affect both the overall controller capabilities per se and the performance of the controller via the embedded state estimation procedure. Although it is obvious that proper actuator setting can have a major impact on the control performance of the system, the optimisation has seldom been carried out over the entire controller chain which includes also the state estimation procedure. When the observation modality exhibits ill-posed characteristics, the state estimates may be rendered useless and the controls far from optimal. The studied case shows that the actuator setting affects the state estimation accuracy and the controller quality significantly.

show abstract

“…The prior information on the spatial structure of the target can be embedded in the regularization matrix R, and the vector u*. For reference about constructing the regularization matrix in different cases, see for example [13,8]. In the case that spatial regularization is required we also need to determine the spatial regularization parameter a.…”

Section: Parameters Of State Estimationmentioning

confidence: 99%

State space models in process tomography — approximation of state noise covariance

Seppänen¹,

Vauhkonen²,

Somersalo³

et al. 2001

Inverse Problems in Engineering

Self Cite

View full text Add to dashboard Cite

In this paper we consider process tomography in the case of time-varying objects. Especially, we concentrate on the case in which the indirect observations from the system are obtained via electrical impedance tomographic (EIT) measurements and in which the time-evolution of the target can be described by a stochastic convection-diffusion model. We use the state estimation approach to obtain the tomographic reconstructions. The state estimation problem is solved with the fixed-lag Kalman smoother algorithm that is a feasible approach for continuous observation with an insignificant delay in the reconstructions. In particular we focus on the covariance structures associated with state space model. The covariance structures determine the temporal and spatial regularization properties of the algorithm. It is shown that the adoption of nontrivial covariance structures in the evolution model yields good estimates for the time-varying object in such a situation in which stationary reconstructions are completely useless.

show abstract

Electrical process tomography with known internal structures and resistivities

Cited by 23 publications

References 25 publications

Dynamic electrical impedance imaging of binary-mixture fields with external and internal electrodes

Dynamic electrical impedance imaging of binary-mixture fields with external and internal electrodes

Optimal actuator placement for controlling concentration profiles via process tomography

State space models in process tomography — approximation of state noise covariance

Contact Info

Product

Resources

About