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Circular pipelines are mostly used for pneumatic conveyance in industrial processes. For optimum and efficient production in industries that use a pipeline for conveyance, tomographic image of the transport particles is paramount. Sensing mechanism plays a vital role in process tomography. The purpose of this paper is to present a two‐dimensional (2‐D) model for sensing the characteristics of electrostatic sensors for electrical charge tomography system. The proposed model uses the finite‐element method.

The domain is discretized into discrete shapes, called finite elements, by using a MATLAB. Each of these elements is taken as image pixels, on which the electric charges carried by conveyed particles are transformed into equations. The charges' interaction and the sensors installed around the circumference, at the sensing zone of the conveying pipeline are related by the proposed model equations. A matrix compression technique was also introduced to solve the problem of unevenly sensing characteristics of the sensors due to elements' number's concentration. The model equations were used to simulate the modeled electrostatic charge distribution carried by the particles moving in the pipeline.

The simulated results show that the proposed sensors are highly sensitive to electrostatic charge at any position in the sensing zone, thereby making it a good candidate for tomographic image reconstruction.

Tomographic imaging using finite element method is found to be more accurate and reliable compared to linear and filtered back projection method.

The purpose of this paper is to conduct a review of types of tomographic systems that have been widely researched within the past 10 years. Decades of research on non-invasively and non-intrusively visualizing and monitoring gas-liquid multi-phase flow in process plants in making sure that the industrial system has high quality control. Process tomography is a developing measurement technology for industrial flow visualization.

A review of types of tomographic systems that have been widely researched especially in the application of gas-liquid flow within the past 10 years was conducted. The sensor system operating fundamentals and assessment of each tomography technology are discussed and explained in detail.

Potential future research on gas-liquid flow in a conducting vessel using ultrasonic tomography sensor system is addressed.

The authors would like to undertake that the above-mentioned manuscript is original, has not been published elsewhere, accepted for publication elsewhere or under editorial review for publication elsewhere and that my Institute’s Universiti Teknologi Malaysia representative is fully aware of this submission.

The use of pneumatic conveying of solid bulk over long distance has become a popular technique due to low operational cost, low maintenance requirement, layout flexibility and ease of automation. The purpose of this paper is to identifity the flow regime in a pneumatic conveyor system by electrodynamic sensor placed around the pipe using fuzzy logic tools.

Electrical charge tomography is used to detect the existence of inherent charge on the moving particles through the pipe. Linear back projection algorithm and filtered back projection algorithm are employed to produce tomography image. Baffles of different shapes are inserted to create various flow regimes, such as full flow, three quarter flow, half flow and quarter flow. Fuzzy logic tools are used to identify different flow regimes and produce filtered back concentration profiles for each flow regime.

The results show significant improvement in the pipe flow image resolution and measurement.

This paper presents a flow identifier method using electrical charge tomography and fuzzy logic to monitor solid particles flow in pipeline.

Solid particles flowing in a pipeline is a common mode of transport in industries. This is because pipeline transportation can avoid waste through spillage and minimizes the risk of handling of hazardous materials. Pharmaceutical industries, food stuff manufacturing industries, cement, and chemical industries are a few industries to exploit this transportation technique. For such industries, monitoring and controlling material flow through the pipe is an essential element to ensure efficiency and safety of the system. The purpose of this paper is to present electrical charge tomography, which is one of the most efficient, robust, cost‐effective, and non‐invasive tomographic methods of monitoring solid particles flow in a pipeline.

Process flow data are captured by fitting an array of 16 discrete electrodynamic sensors about the circumference of the flow pipe. The captured data are processed using two tomographic algorithms to obtain tomographic images of the flow. Then a neural network tool is used to improve image resolution and accuracy of measurements.

The results from the above technique show significant improvements in the pipe flow image resolution and measurements.

The paper presents electrical charge tomography, which is one of the most efficient, robust, cost‐effective, and non‐invasive tomographic methods of monitoring solid particles flow in a pipeline.

An identification model for materials flow through a pipeline is presented in this paper. The development of the model involves fuzzy C-means clustering, in which different flow regimes can be identified by every adaptive network-based fuzzy inference system (ANFIS). The paper aims to discuss these issues.

For experimentation, 16 electrodynamic sensors were used to monitor and measure the charge carried by dense particles flow through a pipeline in a vertical gravity flow rig system. Four ANFIS models were also used simultaneously to provide the expected output on thresh-holding and were evaluated for ten different flow regimes, which produced satisfactory results at high flow rate.

The observations made on the four ANFIS models in the flow identification experimentation (in ten different flow regimes) have shown convincing and satisfactory results at high-flow rate of the particles.

Electrodynamic sensors have shown strong sensing capability in identification of dense-particle flows within a conveyor; and also proven capability to operate effectively in harsh industrial environments due to their firm and simple structures. Moreover, it has been verified that these sensors can conveniently be applied in flow regime identification of solid particles.

The purpose of this paper is to investigate the achievable improvement in reconstruction accuracy in electrical tomography through the incorporation of physical bound constraints as prior knowledge in the inverse problem solution.

The structure of the nonlinear least squares inverse problem formulation and the importance of prior knowledge are addressed. Several different methods for the incorporation of bound constraints are discussed. The methods are compared by means of reconstructions from simulated and measured data and the computational demands.

The inclusion of bound constraints on the material values in the inverse problem solution results in a considerable improvement of the reconstructions. The occurrence of artefacts and blurring can be reduced. Among the investigated constraint handling methods, the logarithmic parameter reconstruction approach can be implemented with minimal additional computational effort.

The study is performed with discrete two‐phase material distributions as occurring in industrial problems. A further step would be the extension to multiple phases.

The logarithmic transform method is a novel approach for the incorporation of bound constraints in tomography. It outperforms other constraint handling approaches and may be of interest for electrical tomography systems in various applications.

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application.

In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis.

This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis.

The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.

Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.

The investigation of the influence of the modeling error on the solution of the inverse problem given uncertain measured data in electrical capacitance tomography (ECT).

The solution of the nonlinear inverse problem in ECT and hence, the obtainable accuracy of the reconstruction result, highly depends on the numerical modeling of the forward map and on the required regularization. The inherent discretization error propagates through the forward map, the solution of the inverse problem, the subsequent calculation of process parameters and properties and may lead to a substantial estimation error. Within this work different finite element meshes are compared in terms of obtainable reconstruction accuracy. In order to characterize the reconstruction results, two error measures are introduced, a relative integral error and the relative error in material fraction. In addition, the influence of the measurement noise given different meshes is investigated from the statistical point of view using repeated measurements.

The modeling error, the degree of regularization, and measurement uncertainties are the determining and limiting factors for the obtainable reconstruction accuracy of electrical tomography systems. The impact of these key influence factors on the calculation of process properties given both synthetic as well as measured data is quantified. Practical implications – The obtained results show that especially for measured data, the variability in calculated parameters strongly depends on the efforts put on the forward modeling, i.e. on an appropriate finite element mesh size. Hence, an investigation of the modeling error is highly recommended when real‐world tomography problems have to be solved.

The results presented in this work clearly show how the modeling error as well as inherent measurement uncertainties influence the solution of the inverse problem and the posterior calculation of certain parameters like void fraction in process tomography.

The purpose of this paper is to demonstrate improvement of the accuracy of electrical tomography reconstruction by incorporation of a priori knowledge into the inverse problem solution.

The fusion of two different inversion algorithms capable of real‐time operation is discussed, namely a non‐iterative monotonicity‐based approach, determining the a priori knowledge and an iterative Gauss‐Newton (GN)‐based reconstruction algorithm. Furthermore, the method is compared with the unmodified algorithms themselves by means of reconstructions from simulated inclusions at different noise levels.

The accuracy of the inverse problem reconstructions, especially at the boundary regions of the unknown inclusions, benefit from the investigations of incorporating a priori knowledge about material values and can be considerable improved. The monotonicity method itself, which has low complexity, provides remarkable reconstruction results in electrical tomography.

The paper is applied to simulated discrete two‐phase scenarios, e.g. gas/oil mixtures. In a further step the method would be tested with measured data. Moreover, investigations have to be carried out in order to make the monotonicity‐based reconstruction principle more robust against disturbing artifacts.

The fusion of the non‐iterative monotonicity‐based method with the GN‐based algorithm demonstrates a novel approach of improving the reconstruction accuracy in electrical tomography.