W. Warsito

Neural network based multi-criteria optimization image reconstruction technique for imaging two- and three-phase flow systems using electrical capacitance tomography

A new image reconstruction technique for imaging two- and three-phase flows using electrical capacitance tomography (ECT) has been developed based on multi-criteria optimization using an analog neural network, hereafter referred to as Neural Network Multi-criteria Optimization Image Reconstruction (NN-MOIRT)). The reconstruction technique is a combination between multi-criteria optimization image reconstruction technique for linear tomography, and the so-called linear back projection (LBP) technique commonly used for capacitance tomography. The multi-criteria optimization image reconstruction problem is solved using Hopfield model dynamic neural-network computing. For three-component imaging, the single-step sigmoid function in the Hopfield networks is replaced by a double-step sigmoid function, allowing the neural computation to converge to three-distinct stable regions in the output space corresponding to the three components, enabling the differentiation among the single phases.

Measurement of real-time flow structures in gas–liquid and gas–liquid–solid flow systems using electrical capacitance tomography (ECT)

Abstract The real-time cross-sectional distributions of the gas holdups in gas–liquid and gas–liquid–solid systems are measured using electrical capacitance tomography. For the gas–liquid system, air as the gas phase and both Norpar 15 (paraffin) and Paratherm as the liquid phases are used. Polystyrene beads whose permittivity is similar to that of Paratherm are used as the solid phase in the gas–liquid–solid system. The three-phase system is essentially a dielectrically two-phase system enabling measurement of the gas holdup in the gas–liquid–solid system independent of the other two phases. A new reconstruction algorithm based on a modified Hopfield dynamic neural network optimization technique developed by the authors is used to reconstruct the tomographic data to obtain the cross-sectional distribution of the gas holdup. The real-time flow structure and bubbles flow behavior in the two- and three-phase systems are discussed along with the effects of the gas velocity and the solid particles.

Electrical Capacitance Volume Tomography

A dynamic volume imaging based on the principle of electrical capacitance tomography (ECT), namely, electrical capacitance volume tomography (ECVT), has been developed in this study. The technique generates, from the measured capacitance, a whole volumetric image of the region enclosed by the geometrically three-dimensional capacitance sensor. This development enables a real-time, 3-D imaging of a moving object or a real-time volume imaging (4-D) to be realized. Moreover, it allows total interrogation of the whole volume within the domain (vessel or conduit) of an arbitrary shape or geometry. The development of the ECVT imaging technique primarily encloses the 3-D capacitance sensor design and the volume image reconstruction technique. The electrical field variation in three-dimensional space forms a basis for volume imaging through different shapes and configurations of ECT sensor electrodes. The image reconstruction scheme is established by implementing the neural-network multicriterion optimization image reconstruction (NN-MOIRT), developed earlier by the authors for the 2-D ECT. The image reconstruction technique is modified by introducing into the algorithm a 3-D sensitivity matrix to replace the 2-D sensitivity matrix in conventional 2-D ECT, and providing additional network constraints including 3-to-2-D image matching function. The additional constraints further enhance the accuracy of the image reconstruction algorithm. The technique has been successfully verified over actual objects in the experimental conditions

Electrical Capacitance Volume Tomography: Design and Applications

This article reports recent advances and progress in the field of electrical capacitance volume tomography (ECVT). ECVT, developed from the two-dimensional electrical capacitance tomography (ECT), is a promising non-intrusive imaging technology that can provide real-time three-dimensional images of the sensing domain. Images are reconstructed from capacitance measurements acquired by electrodes placed on the outside boundary of the testing vessel. In this article, a review of progress on capacitance sensor design and applications to multi-phase flows is presented. The sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of three-dimensional capacitance sensors are illustrated. The article also highlights applications of ECVT sensors on vessels of various sizes from 1 to 60 inches with complex geometries. Case studies are used to show the capability and validity of ECVT. The studies provide qualitative and quantitative real-time three-dimensional information of the measuring domain under study. Advantages of ECVT render it a favorable tool to be utilized for industrial applications and fundamental multi-phase flow research.

A nonlinear image reconstruction technique for ECT using a combined neural network approach

A combined multilayer feed-forward neural network (MLFF-NN) and analogue Hopfield network is developed for nonlinear image reconstruction of electrical capacitance tomography (ECT). The (nonlinear) forward problem in ECT is solved using the MLFF-NN trained with a set of capacitance data from measurements based on a back-propagation training algorithm with regularization. The inverse problem is solved using an analogue Hopfield network based on a neural-network multi-criteria optimization image reconstruction technique (HN-MOIRT). The nonlinear image reconstruction based on this combined MLFF-NN + HN-MOIRT approach is tested on measured capacitance data not used in training to reconstruct the permittivity distribution. The performance of the technique is compared against commonly used linear Landweber and semi-linear image reconstruction techniques, showing superiority in terms of both stability and quality of reconstructed images.

A Multimodal Tomography System Based on ECT Sensors

A new noninvasive system for multimodal electrical tomography based on electrical capacitance tomography (ECT) sensor hardware is proposed. Quasistatic electromagnetic fields are produced by ECT sensors and used for interrogating the sensing domain. The new system is noninvasive and based on capacitance measurements for permittivity and power balance measurements for conductivity (impedance) imaging. A dual sensitivity map of perturbations in permittivity and conductivity is constructed. The measured data along with the sensitivity matrix are used for the actual image reconstruction. The new multimodal tomography system has the advantage of using already established reconstruction techniques, and the potential for combination with new reconstruction techniques by taking advantage of combined conductivity/permittivity data. Moreover, it does not require direct contact between the sensor and the region of interest. The system performance has been tested on representative data, producing good results

ECT imaging of three-phase fluidized bed based on three-phase capacitance model

In this work, the electrical capacitance tomography (ECT) with neural network multi-criteria optimization image reconstruction technique (NN-MOIRT), early developed by the authors, is applied to imaging bubble column and three-phase fluidized bed systems in the real time manner. Air, norpar (paraffin) and glass-beads are used as the gas, liquid, and solid phases, respectively. A three-phase capacitance model coupled with a two-region model is proposed to attain the gas holdup and the solids fraction from the permittivity maps of the three-phase system. The two-region model assumes that the solids fraction in the emulsion phase in the no bubble region is the same as in the bubble region. The three-phase capacitance model combines series and parallel capacitance connections among gas, liquid and solid components to relate the three-phase permittivity to each phase holdup. A direct image calculation to obtain the gas holdup from the permittivity map of the three-phase system is also performed by determining the permittivity threshold for the gas bubbles. Comparisons of the gas holdup obtained by ECT with that obtained from liquid head measurement showed a good agreement, validating the applicability of the model and its associated image calculation.

Evaporative liquid jets in gas–liquid–solid flow system☆

Abstract Some aspects of the fundamental characteristics of evaporative liquid jets in gas–liquid–solid flows are studied and some pertinent literature is reviewed. Specifically, two conditions for the solids concentration in the flow are considered, including the dilute phase condition as in pneumatic convey and the dense phase condition as in bubbling or turbulent fluidized beds. Comparisons of the fundamental behavior are made of the gas–solid flow with dispersed non-evaporative as well as with evaporative liquids. For dilute phase conditions, experiments and analyses are conducted to examine the individual phase motion and boundaries of the evaporative region and the jet. Effects of the solids loading and heat capacity, system temperature, gas flow velocity and liquid injection angle on the jet behavior in gas and gas–solid flows are discussed. For dense phase conditions, experiments are conducted to examine the minimum fluidization velocity and solids distribution across the bed under various gases and liquid flow velocities. The electric capacitance tomography is developed for the first time for three-phase real time imaging of the dense gas–solid flow with evaporative liquid jets. The images reflect significantly varied bubbling phenomenon compared to those in gas–solid fluidized beds without evaporative liquid jets.

Nonlinear forward problem solution for electrical capacitance tomography using feed-forward neural network

A new technique for solving the forward problem in electrical capacitance tomography sensor systems is introduced. The new technique is based on training a feed-forward neural network (NN) to predict capacitance data from permittivity distributions. The capacitance data used in training and testing the NN is obtained from preprocessed and filtered experimental measurements. The new technique has shown better results when compared to the commonly used linear forward projection (LFP) while maintaining fast prediction speed. The new technique has also been integrated into a modified iterative linear back projection (Landweber) reconstruction algorithm. Reconstruction results are found to be in favor of the NN forward solver when compared to the widely used Landweber reconstruction technique with LFP forward solver.

Neural network multi-criteria optimization image reconstruction technique (NN-MOIRT) for linear and non-linear process tomography

In this work, an analog neural network is utilized to develop a new image reconstruction technique for the linear as well as the non-linear process tomography. The ultrasonic computed tomography (CT) and the electrical capacitance tomography (ECT) are chosen to represent the linear and the non-linear tomography. The image reconstruction technique is based on a multi-criteria optimization, namely neural network multi-criteria optimization image reconstruction technique (NN-MOIRT). The optimization technique utilizes multi-objective functions: (a) the negative entropy function, (b) the function of the least weighted square error of projection (integral) values between the measured data and the estimated projection data from the reconstructed image, and (c) a smoothness function that gives a relatively small peakedness in the reconstructed image. The optimization image reconstruction problem is then solved using the Hopfield model with dynamic neural-network computing. The technique has been tested using simulated and measured data; this technique has shown significant improvement in accuracy and consistency compared with other available techniques for both linear and non-linear tomography.