Electrical capacitance tomography (ECT) has been used to measure flow by applying gas-solid flow in coal gasification, pharmaceutical, and other industries. ECT is also used for creating images of physically confined objects. The data collected by the acquisition system to produce images undergo blurring because of ambient conditions and the electronic circuitry used. This research includes the principle of ECT techniques for deblurring images that were created during measurement. The data recorded by the said acquisition system ascends a large number of linear equations. This system of equations is sparse and ill-conditioned and hence is ill-posed in nature. A variety of reconstruction algorithms with many pros and cons are available to deal with ill-posed problems. Large-scale systems of linear equations resulting during image deblurring problems are solved using iterative regularization algorithms. The conjugate gradient algorithm for least-squares problems (CGLS), least-squares QR factorization (LSQR), and the modified residual norm steepest descent (MRNSD) algorithm are the famous variations of iterative algorithms. These algorithms exhibit a semiconvergence behavior; that is, the computed solution quality first improves and then reduces as the error norm decreases and later increases with each iteration. In this work, soft thresholding has been used for image deblurring problems to tackle the semiconvergence issues. Numerical test problems were executed to indicate the efficacy of the suggested algorithms with criteria for optimal stopping iterations. Results show marginal improvement compared to the traditional iterative algorithms (CGLS, LSQR, and MRNSD) for resolving semiconvergence behavior and image restoration.

This article proposed a methodology that combines two well-known projections and modified density-based optimized techniques in one formulation methodology. This methodology contains an effective explicit geometric entity identified by shape variables that provide easy control in desired particular regions; implicit density-based topological optimization entities utilized topological variables that offer critical design elsewhere. Our main attractive key point of this combined formulation approach is structural assemblies. Structure always manufactures in many patches and joins them by utilizing the structural assemblies such as welding and riveting. It is not easy to execute the structural patches at the required region without acknowledging their dimensions. This proposed approach demonstrates the competence to impose the restrictions related to shape and topological variables of interfaces among the specific patches. Numerous standard numerical examples make sure the validation of the introduced methodology. It remarked that the optimal design could minimize compliance and the minimum number of iterations through numerically performed, concerning computational cost minimized without any kind loss of accuracy of the final structure.

Gas-Solid flow finds its application in various industries such as chemical and food processes, pharmaceuticals, automobile and power generation. The precise measurement and real-time monitoring of the flow process have garnered a lot of attraction in the modern industrial era. The effective utilization, with high efficiency, of the resources have compelled the researchers to investigate Gas-Solid flow monitoring and measurement process for the last two decades. Efforts are extended to design instruments for flow pattern visualization and quantification of mass flow rate. Generally, volumetric flow concentration and velocity profile of the solid particles are required to find the mass flow rate (MRF) in the case of non-invasive measurement techniques. This paper proposes a novel non-invasive mass flow rate measurement system for solid particles where electrical and mechanical sensors are simultaneously used to compensate each other’s associated drawbacks. The solid particles are allowed to slide along an insulated inclined channel under the effect of gravity. The velocity profile and volumetric flow concentration are calculated using electrical sensors. Furthermore, load cell serves as a second sensor to calculate the mass flow rate. The experimental results from both the sensors are compared to investigate the accuracy and relative errors of both the sensors under different conditions.

This paper covers the application of the cross-correlation method for measuring the velocity of solid particles. Capacitive electrodes are used as primary sensors to measure the time required by the solid particles to cover a known distance between the electrodes. The capacitive variations of both the electrode sensors are stored in computer synchronously for offline estimation of the velocity of the solid particles. Single glass marble is used as a solid particle to conduct the performance in the lab for its good signal to noise ratio (SNR) and vivid graphs. Matlab R2018a is used as a programming tool to perform the cross-correlation algorithm. The distance between the sensors is adjusted optimally. The results were realistic to ensure the correctness of the system.

The precise and accurate measurement of flow rate in the batch flow of the solid particles is of primary importance in many process industries for the improvement of the efficiency of the system. Many techniques developed for the measurement of mass flow rate. The capacitive sensors has a significance of being non-invasive, higher accuracy and low cost for mass flow measurement despite the fact that many factors adversely affect the performance- including non-uniform flow, multiphase flow, temperature, pressure, and moisture in the solid particles. This paper covers preliminary investigations of the offline estimation of mass flow concentration based upon the calibration of capacitance electrodes to quantify the mass of dielectric as a function of capacitance variation between the electrodes.