This paper presents different views on electrode modelling, which include electrode electrochemistry models for modelling the effects of electrode-electrolyte interface, electric field electrode models for modelling electrode geometry, and electrode models ,for modelling ,the effects of ,electrode common ,mode ,voltage and double ,layer capacitance. Taking the full electrode models into consideration in electrical impedance,tomography (EIT) will greatly help the optimised approach to a good solution and further understanding ofthe measurement principle. Key words: Electrical impedance tomography (EIT), Electrochemistry, Electrode modelling doi:10.1631/jzus.2005.A1386 ,,,,,Document code: A ,,,, CLC number:TB126

This paper reviews governing theorems in electrical impedance sensing for analysing the relationships of boundary voltages obtained from different sensing strategies. It reports that both the boundary voltage values and the associated sensitivity matrix of an alternative sensing strategy can be derived from a set of full independent measurements and sensitivity matrix obtained from other sensing strategy. A new sensing method for regional imaging with limited measurements is reported. It also proves that the sensitivity coefficient back-projection algorithm does not always work for all sensing strategies, unless the diagonal elements of the transformed matrix, A ^T A, have significant values and can be approximate to a diagonal matrix. Imaging capabilities of few sensing strategies were verified with static set-ups, which suggest the adjacent electrode pair sensing strategy displays better performance compared with the diametrically opposite protocol, with both the back-projection and multi-step image reconstruction methods. An application of electrical impedance tomography for sensing gas in water two-phase flows is demonstrated. This article is part of the themed issue ‘Supersensing through industrial process tomography’.

This paper reviews the integration of machine learning techniques with sensor-based technologies for multiphase flow measurement in industrial applications. Accurate measurement of multiphase flows is essential for process optimization and safety but presents challenges due to complex phase distributions and varying velocities. The review first discusses traditional sensors used in multiphase flow measurements, including differential pressure, microwave, electrical tomography, and radioactive source-based sensors. It highlights the challenges associated with these sensors. The article then explores various machine learning algorithms applied to multiphase flow data analysis, covering both traditional methods such as multilayer perceptrons and support vector machine networks, and advanced deep learning approaches such as convolutional and recurrent neural networks. The focus is on how sensor-based machine learning can enhance the accuracy of multiphase flow predictions and reduce computational demands. The review compares different sensor-based machine learning methods, illustrating their effectiveness in improving prediction accuracy. This review is relevant to industrial sectors that rely on accurate multiphase flow measurements and highlights the potential of machine learning in augmenting conventional measurement techniques.

Multi-component and multiphase fluid flows exist in many industrial processes. The phase distribution and interfaces in an aqueous-based process or multiphase flow carries significant information about the processes. Due to the correlation of the multiphase and independency of each phase, the correct measuring of multiphase flow, in the terms of concentration, disperse interface, local velocity and mass flow rate, are of extremely challenged tasks. In 1990s, electrical capacitance tomography was originally developed for oil-water two-phase flow, then electrical resistance tomography was introduced from medical research to process engineering. Significant progress has been made during the past decade and now the technology has been proved as a powerful tool for mapping the concentration and velocity distributions of the second phase in two-phase flows, where electrical impedance differences between the two-phase fluids exist. Previous and current work on electrical resistance tomography by the author and his colleagues are reviewed. Developments on sensor, measurement, image reconstruction and application are also briefly introduced. At the end of the paper, existing challenges and prospect of new dimensions in process tomography are discussed.