Design, construction and evaluation of an electrical capacitance tomography system to monitor grains flow passing through pipeline

Document Type : Research Paper

Authors

Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

Abstract

 
In determining the mass flow rate of bulk materials such as grains that pass through closed channels such as pipes, the online measurement of the volume ratio of solid materials and their distribution in the pipe cross-section is of particular importance. Considering the features of conventional methods such as be creating obstacles in the passage of materials (permeability) and low accuracy, in this research the non-contact tomography method of electrical capacitance was investigated for monitoring the flow of solid materials. In this method, to measure the amount of material and its density, it uses the measurement of the dielectric properties of the material inside the pipe. The developed electro-capacitance tomography system has 8 main electrodes and 16 secondary electrodes, anti-noise guards and a transmitter and receiver circuit, which was installed on a non-conductive pipe with a diameter of 20 cm. The main problem in the performance of the electro-capacitance tomography is the noise and lack of optimal image reconstruction with the conventional LBP algorithm. In this study, the performance of Tikhonov algorithm was compared with the conventional LBP algorithm. In this research, by using different guards, the noise level of the system was reduced so that the signal-to-noise ratio reached 56.09 dB. The results of the comparison of two algorithms showed that the Tikhonov algorithm has a good behavior in reconstruction of a tomogram of the wheat mass next to the pipe walls compared to the LBP algorithm, and except for the condition that the pipe is completely full, in other filling patterns of the pipe, has a better performance.

Keywords

Main Subjects

Design, construction and evaluation of an electrical capacitance tomography system to monitor grains flow passing through pipeline

Extended Abstract

Introduction

On-line monitoring of the distribution and density of solids that are transferred from the pipe are required in various processes. Conventional methods of mass flow measurement have limitations. Therefore, in this study, electrical capacitance tomography was used to determine the distribution of grain materials such as wheat in the pipe. In this research, the aim is to improve the performance of the capacitance tomography system by using a suitable arrangement for the electrodes and guards around it and using the Tikhonov reconstruction algorithm instead of the conventional LBP algorithm.

Material and Methods

In order to evaluate the capability of the electrical capacitance tomography system for determining the distribution of grains in the pipe section and measuring the mass flow rate of passing materials, a system based on electrical capacitance tomography was designed and manufactured. This system has three parts. The first part includes capacitive sensors. Sensors are actually a set of electrodes that are arranged around the pipe. The second part is a circuit for stimulating the sensors and measuring the response from the sensors. The third part includes the image reconstruction algorithm and the extraction of information on the distribution of particles in the pipe cross-section.

Algorithms of image reconstruction in electrical capacitance tomography have two stages of solving the direct problem and the inverse problem. The direct problem is to find the capacitance between the electrodes considering any form of known permittivity distribution. In this research, in addition to the conventional LBP algorithm, the performance of another algorithm called the Tikhonov regularization method is investigated. In this research parameters such as signal-to-noise ratio (SNR), which indicates the quality of the signal and sensitivity of the system are investigated. Root mean square error (RMSE) parameter, absolute average percentage of the error (MAPE) and standard deviation (SD) for both image reconstruction algorithms are compared and discussed.

Results and discussion

The dynamic range of boundary potentials is equal to 1740 millivolts. High signal-to-noise ratio indicates resistance of signal to noise and better signal quality. According to the obtained data, the signal-to-noise ratio is equal to 56.09 dB and the voltage variation parameter (VC) is equal to 424 millivolts. Two conventional LBP and Tikhonov algorithms were used to reconstruct the pipe section. The performance of the system was investigated in eight different positions of filling the pipe section by wheat. In situations P2 to P8, the RMSE values for the Tikhonov reconstruction algorithm are in the range of 0.04 to 0.01, which is lower than the mean squared error values for the LBP algorithm. The lowest value of RMSE belongs to the Tikhonov algorithm and for the position where the object is in the middle, and its value is equal to 0.0304. As grains of wheat was approaching the wall, this error increases and reaches to 0.037.

The results showed that with the increase of pipe filling and material density, the standard deviation decreases, and in other words, the accuracy of the system increases at higher densities, and as it was clear from the tomograms, the SD values in the Tikhonov algorithm are less than the algorithm is LBP.

Conclusion

The comparison results of the two algorithms showed that the Tikhonov algorithm has a more favorable behavior in making a tomogram of the wheat mass next to the pipe walls compared to the LBP algorithm. Both algorithms did not perform well in identifying the empty space between the wheat clumps, so it is suggested that other algorithms be used to detect the empty space between the wheat clumps. In general, considering the appropriate speed of Tikhonov's algorithm, this algorithm can be a suitable alternative to the conventional LBP algorithm.

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Iranian Journal of Biosystems Engineering
Volume 54, Issue 1
March 2023
Pages 37-52

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History

  • Receive Date: 05 April 2023
  • Revise Date: 24 June 2023
  • Accept Date: 06 July 2023

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