(72b) Material Distribution of Gas–Solid Fluidized Beds Measured By Electrical Capacitance Tomography – a Big-Data Perspective

Ye, M., Dalian Institute of Chemical Physics, Chinese Academy of Sciences
Guo, Q., Dalian Institute of Chemical Physics, Chinese Academy of Sciences
Meng, S., Dalian Institute of Chemical Physics, Chinese Academy of Sciences
Yang, W. Q., School of Electrical and Electronic Engineering
Liu, Z., Dalian Institute of Chemical Physics, Chinese Academy of Sciences

Distribution of Gas-Solid Fluidized Beds Measured by Electrical Capacitance
Tomography - a Big-Data Perspective

fluidized bed
s are used in many industrial processes,
such as coal gasification, power generation, granulation, and polymerization.
To measure hydrodynamic characteristics of gas-solid fluidized beds, numerous
intrusive and non-intrusive experimental techniques have been developed. The
intrusive methods like optical probe, capacitance probe,and pressure measurementare easy to implement but only capable
of providing local information concerning the fluid flow. While the
non-intrusive techniques such as tomographycan be used to visualize the entire flow field without causing any
disturbance to the flow. Compared to other industrial process tomography
techniques, Electrical Capacitance Tomography (ECT) shows advantages in terms
of fast imaging speed, no radiation, robustness, and low cost. Moreover,
considering the non-conductive nature of materials in gas-solid fluidized beds,
ECT is a suitable measuring technique for hydrodynamic investigation of these

the sensing electronics measure variations in capacitance between pairs of
electrodes, which are placed around the periphery of a pipe or vessel under
investigation. These measurements are then used to reconstruct the cross-sectional
permittivity distribution as a presentation of the material distribution inside
the sensing area via a specific image reconstruction algorithm. However, two
major difficulties are associated with the reconstruction process. First, the
number of measured independent capacitance
data is far less than the number of unknown image pixels, and therefore the
problem is severely under-determined. Secondly, due to the ill-posed and
ill-conditioned property of the reconstruction process, the reconstructed
results are sensitive to raw capacitance measurement noise. To address these
problems, different reconstruction techniques, including single-step and
iterative algorithms, have been proposed and have been reviewed by Yang and
Peng2 and Cui et al.

big progress has been made in the image reconstruction with ECT in the last two
decades to improve the image quality, i.e., the accuracy of material
distribution measurements, the correlation coefficient between the true image
and reconstructed image by a certain technique is usually lower than 0.9. This
apparently hiders ECT as an excellent technique for gas-solid fluidized beds
measurements, especially when a direct comparison between ECT measurements and Computational
Fluid Dynamics (CFD) simulations is needed, in which high-accuracy images are

In the
last several years, big-data analysis has gained considerable attention and
momentum in both academia and industry, and also see some applications in the
field of chemical engineering.4
The dynamic evaluation method, which we have proposed recently,5 provides a way for image reconstruction
with ECT using a big-data perspective, and is potential to improve the image
quality greatly.

To meet
different needs, the big-data image reconstruction method is categorized to two
schemes: an on-line scheme and an off-line scheme. The procedure of the on-line
scheme is as follows. First, CFD simulations of a gas-solid fluidized bed,
which is the same as that used in experiments, are performed. Next, a generic
sensitivity matrix is obtained from a probability distribution analysis carried
out on the material distributions extracted from the CFD simulation results.
Finally, material distributions in experiments can be obtained using the
modified Tikhonov regularization method equipped with the generic sensitivity
matrix. While the off-line scheme is due to fingerprint matching the normalized
capacitance between CFD simulations and experiments. As an illustration, Figure
1 shows some typical results obtained by different image reconstruction
algorithms and the second-scheme big-data method. As can be seen, the correlation
coefficients obtained by the existing image reconstruction algorithms are all
lower than 0.9, also some details especially on the boundary between bubble and
emulsion phases are blurred. While the best image can be obtained by the
big-data method with the correlation coefficient as high as 0.95.

Figure 1. Material distribution
reconstructed by different image reconstruction algorithms and the second-scheme
big-data method.

Literature Cited

1. Rasel RK, Zuccarelli CE, Marashdeh QM,
Fan L-S, Teixeira FL. Towards multiphase flow decomposition based on electrical
capacitance tomography sensors. IEEE Sens
. 2017;in press.

2. Yang WQ, Peng LH. Image reconstruction
algorithms for electrical capacitance tomography. Meas Sci Technol. 2003;14:R1-R13.

3. Cui ZQ, Wang Q, Xue Q, Fan WR, Zhang
LL, Cao Z, Sun BY, Wang HX, Yang WQ. A review on image reconstruction
algorithms for electrical capacitance/resistance tomography. Sens Rev. 2016;36:429-445.

4. Qin SJ. Process data analytics in the
era of big data. AlChE J.

5. Guo Q, Meng SH, Wang DH, Zhao YF, Ye M,
Yang WQ, Liu ZM. Investigation of gas-solid bubbling fluidized beds using ECT
with a modified Tikhonov regularization technique. AlChE J. 2017;in press.