Cargando…
Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm
In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony–optimized rad...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490594/ https://www.ncbi.nlm.nih.gov/pubmed/37688101 http://dx.doi.org/10.3390/s23177645 |
_version_ | 1785103875897819136 |
---|---|
author | Zhu, Zhiheng Li, Gang Luo, Mingzhang Zhang, Peng Gao, Zhengyang |
author_facet | Zhu, Zhiheng Li, Gang Luo, Mingzhang Zhang, Peng Gao, Zhengyang |
author_sort | Zhu, Zhiheng |
collection | PubMed |
description | In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony–optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss–Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm’s capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm’s generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm. |
format | Online Article Text |
id | pubmed-10490594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104905942023-09-09 Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm Zhu, Zhiheng Li, Gang Luo, Mingzhang Zhang, Peng Gao, Zhengyang Sensors (Basel) Article In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony–optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss–Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm’s capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm’s generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm. MDPI 2023-09-04 /pmc/articles/PMC10490594/ /pubmed/37688101 http://dx.doi.org/10.3390/s23177645 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhu, Zhiheng Li, Gang Luo, Mingzhang Zhang, Peng Gao, Zhengyang Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm |
title | Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm |
title_full | Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm |
title_fullStr | Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm |
title_full_unstemmed | Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm |
title_short | Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm |
title_sort | electrical impedance tomography of industrial two-phase flow based on radial basis function neural network optimized by the artificial bee colony algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490594/ https://www.ncbi.nlm.nih.gov/pubmed/37688101 http://dx.doi.org/10.3390/s23177645 |
work_keys_str_mv | AT zhuzhiheng electricalimpedancetomographyofindustrialtwophaseflowbasedonradialbasisfunctionneuralnetworkoptimizedbytheartificialbeecolonyalgorithm AT ligang electricalimpedancetomographyofindustrialtwophaseflowbasedonradialbasisfunctionneuralnetworkoptimizedbytheartificialbeecolonyalgorithm AT luomingzhang electricalimpedancetomographyofindustrialtwophaseflowbasedonradialbasisfunctionneuralnetworkoptimizedbytheartificialbeecolonyalgorithm AT zhangpeng electricalimpedancetomographyofindustrialtwophaseflowbasedonradialbasisfunctionneuralnetworkoptimizedbytheartificialbeecolonyalgorithm AT gaozhengyang electricalimpedancetomographyofindustrialtwophaseflowbasedonradialbasisfunctionneuralnetworkoptimizedbytheartificialbeecolonyalgorithm |