Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm

Abstract

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.

Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

“…In particular, electrical tomography is distinguished by its ability to image objects comprehensively, providing a holistic view of the object's internal dynamics [14,15]. Two prominent non-invasive imaging techniques have emerged as leaders in this field: electrical capacitance tomography (ECT) [16][17][18][19][20][21][22][23][24] and electrical impedance tomography (EIT) [25][26][27][28][29][30][31][32][33]. Unlike ECT, EIT captures impedance characteristics and dielectric properties, providing a richer dataset and thus a more detailed insight into the ongoing processes inside the reactor [31].…”

“…Two prominent non-invasive imaging techniques have emerged as leaders in this field: electrical capacitance tomography (ECT) [16][17][18][19][20][21][22][23][24] and electrical impedance tomography (EIT) [25][26][27][28][29][30][31][32][33]. Unlike ECT, EIT captures impedance characteristics and dielectric properties, providing a richer dataset and thus a more detailed insight into the ongoing processes inside the reactor [31]. In addition, EIT's adaptability to complex scenarios, such as those involving multiple bubbles, and its ability to provide enhanced accuracy in image reconstruction position it as a superior choice for comprehensive reactor monitoring.…”

“…In addition, EIT's adaptability to complex scenarios, such as those involving multiple bubbles, and its ability to provide enhanced accuracy in image reconstruction position it as a superior choice for comprehensive reactor monitoring. Its robustness to noise and its ability to accurately determine the size and shape of bubbles, especially in a dynamically changing environment, make EIT a more reliable tool for industrial applications [31].…”

“…Research on industrial reactors was also carried out. Zhu et al present an algorithm that increases the accuracy of image reconstruction in EIT technology, especially in complicated cases with many bubbles, based on the example of such a reactor model [31]. Following this, Kłosowski et al introduced a novel concept for monitoring industrial tank reactors using a hybrid method of electrical capacitance and impedance tomography [15].…”

Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator

“…Under the above-mentioned situation, electrical impedance tomography (EIT), which is a non-invasive and continuous impedance measurement method, has the possibility to detect minor Cu particles. EIT has the capability to reconstruct conductivity particle distribution of a region of interest utilizing material conductivity properties [10] based on the electrical properties of the higher σ Cu than σ Al and σ Water . However, EIT has a drawback to detect the minor Cu particles due to detection on the outside of the sensitivity sensing area of EIT [11], which produces a weaker signal to detect the minor Cu particles, resulting in blurry images.…”