Electrical Tomography: A Review of Configurations, and Application to Fibre Flow Suspensions Characterisation
Abstract
:Featured Application
Abstract
1. Introduction
2. Pulp Suspensions Imaging Overview
3. Electrical Tomography Principles and Applications
3.1. Electrical Tomography
3.2. Fundamentals of Electrical Tomography
3.2.1. Capacitance Tomography
3.2.2. Resistivity/Impedance Tomography
3.2.3. Image Reconstruction
- Linear methods (single-step and iterative methods): In this approach, images are generated by simply multiplying the measurements by a pre-calculated matrix, and consequently they are quite fast in terms of computational effort. Among the linear methods, Linear back-projection (LBP) is the most widely used (the used matrix is the transpose of an estimated solution of the forward problem [82]). Nevertheless, they are usually non-iterative methods. Images obtained by means of LBP are low-resolution, heavily smoothed ones, because the transpose of the forward problem solution is a poor estimate of the solution to the inverse problem. To circumvent some of these limitations, an iterative back projection algorithm was proposed by Yang et al. [83]. Linear methods have been improved by including approaches based on ridge-regression and eigenanalysis [84].
- Non-linear methods: in this approach, numerical forward solvers that predict the measurements, together with sensitivity maps that make it possible to calculate the measurement residuals, are used to estimate the image. Then, in a second stage, that image is updated via a non-linear iterative scheme, such as the modified Newton Raphson (NR) [85] or adaptive mesh regenerating techniques [86]. However, because NR can introduce artificial errors into the solution, regularisation procedures, such as the Marquard and the Tikhonov procedures, are applied to obtain a better approximation at each iteration [73]. In cases where direct inverse solution algorithms are used, there is a search for the minimised residual vector, making it possible to obtain images with improved accuracy [73,74]. In cases where higher measurement flexibility is desired, non-linear iterative methods are to be recommended; however, the considerable computational effort makes image reconstruction difficult and too slow. Real-time reconstruction of images using non-linear iterative methods is their major drawback, although this may change through a combination of efficient algorithms [87] (Molinari et al. [88] decreased the time needed to reconstruct images using an adaptive mesh grouping method based on a fuzzy genetic algorithm). The increase in computing performance will also help to decrease the time required for image reconstruction. In FEM methods (Finite Element Methods), which are mesh-based reconstruction algorithms, in order to obtain higher-resolution images, mesh optimisation is also of major importance [74].
- Heuristic methods: these methods can be linear or non-linear, and they model empirically the relationship between calibration sets of images and measured ones. Methods making use of multiple linear regressions [89] belong in the linear sub-class. Self-organised maps and artificial neural networks [90] are examples of methods belonging to the non-linear sub-class. Heuristic models also allow the mathematical relations between the measurements and other variables of interest to be obtained [91]. In many cases, image reconstruction in process applications is just an intermediate stage towards the calculus of other variables.
4. Electrical Tomography Applications
4.1. Examples of Applications of Electrical Tomography to Chemical Engineering Processes
4.1.1. Operating Apparatus
4.1.2. Type of Media Conditioning
4.1.3. General Applications of ET
4.2. Applications of ET in the Pulp Suspensions Domain
4.3. Pulp Suspensions Characterisation Using ET: The Authors’ Approach
4.3.1. Overview of the Developed EIT System and of the Experimental Setup
4.3.2. Overview of the Pulp Suspension Flow Imaging
4.3.3. Fibre Flow Velocity Estimation
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Type of Pulp | Concentration (wt/wt) | Flow Velocities (m/s) | Suspension Conductivity (mS·cm−1) |
---|---|---|---|
Eucalypt | 2.35 | 0.5, 1, 1.5, 2, 2.5, 3 | 1.460 |
1.5 | 0.5, 1, 1.5, 2, 2.5, 3 | 1.150 | |
1 | 0.5, 1, 1.5, 2, 2.5, 3 | 1.128 | |
Pine | 2.35 | 0.5, 1, 1.5, 2, 2.5, 3 | 1.471 |
1.5 | 0.5, 1, 1.5, 2, 2.5, 3 | 1.461 | |
1 | 0.5, 1, 1.5, 2, 2.5, 3 | 1.435 |
Concentration (wt/wt) | Flow Velocities (m/s) |
---|---|
3 | 0.25, 0.5 |
2 | 0.25, 0.5, 0.75, 1 |
1 | 0.25, 0.5, 0.75, 1 |
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Faia, P.; Silva, R.; Rasteiro, M.G.; Garcia, F. Electrical Tomography: A Review of Configurations, and Application to Fibre Flow Suspensions Characterisation. Appl. Sci. 2020, 10, 2355. https://doi.org/10.3390/app10072355
Faia P, Silva R, Rasteiro MG, Garcia F. Electrical Tomography: A Review of Configurations, and Application to Fibre Flow Suspensions Characterisation. Applied Sciences. 2020; 10(7):2355. https://doi.org/10.3390/app10072355
Chicago/Turabian StyleFaia, Pedro, Rui Silva, Maria G. Rasteiro, and Fernando Garcia. 2020. "Electrical Tomography: A Review of Configurations, and Application to Fibre Flow Suspensions Characterisation" Applied Sciences 10, no. 7: 2355. https://doi.org/10.3390/app10072355
APA StyleFaia, P., Silva, R., Rasteiro, M. G., & Garcia, F. (2020). Electrical Tomography: A Review of Configurations, and Application to Fibre Flow Suspensions Characterisation. Applied Sciences, 10(7), 2355. https://doi.org/10.3390/app10072355