Investigation of Bubbling Behavior in Deep Fluidized Beds at Different Gas Velocities using Electrical Capacitance Tomography

Agu, Cornelius Emeka; Ugwu, Ambrose; Pfeifer, Christoph; Eikeland, Marianne Sørflaten; Tokheim, Lars-André; Moldestad, Britt Margrethe Emilie

doi:10.1021/acs.iecr.8b05013

Cited by 17 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further advantages include: 1) possessing a rapid imaging speed which typically reaches 100 frames/s, 2) it does not suffer from any hazard radiations, 3) it can bear up high temperature and high pressure. The ECT sensor is composed of multiple exciting electrodes, which are evenly mounted around the non-conductive dielectric medium of the material inside the vessel to be imaged [6]. There is an earthed screen positioned outside the electrodes to depress the exterior interference noise to accomplish such a process.…”

Section: Introductionmentioning

confidence: 99%

“…The potential applications of ECT are broadly extensive [6], with several practical and applicable domains such as biomedical [8], industrial tomography and non-destructive assessment [9]. So, there is a substantial research landscape for the successful and practical applications of ECT in a variety of target applications such as grounded metal observation in a casting process [5], imaging dilute in addition to large multi-phase flows in oil purification, in powder flow in a vertical tube for petroleum refining [10], and the food industry and observation of chemical and pharmaceutical processes [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-Based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

2021

View full text Add to dashboard Cite

Image reconstruction for industrial applications based on Electrical Capacitance Tomography (ECT) has been broadly applied. The goal of image reconstruction based ECT is to locate the distribution of permittivity for the dielectric substances along the cross-section based on the collected capacitance data. In the ECT-based image reconstruction process: (1) the relationship between capacitance measurements and permittivity distribution is nonlinear, (2) the capacitance measurements collected during image reconstruction are inadequate due to the limited number of electrodes, and (3) the reconstruction process is subject to noise leading to an ill-posed problem. Thence, constructing an accurate algorithm for real images is critical to overcoming such restrictions. This paper presents novel image reconstruction methods using Deep Learning for solving the forward and inverse problems of the ECT system for generating high-quality images of conductive materials in the Lost Foam Casting (LFC) process. Here, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models were implemented to predict the distribution of metal filling for the LFC process-based ECT. The recurrent connection and the gating mechanism of the LSTM is capable of extracting the contextual information that is repeatedly passing through the neural network while filtering out the noise caused by adverse factors. Experimental results showed that the presented ECT-LSTM-RNN model is highly reliable for industrial applications and can be utilized for other manufacturing processes.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-Based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

2021

View full text Add to dashboard Cite

show abstract

“…Electrical capacitance tomography (ECT) is one of major tomographic techniques that has been widely used in the gas–solid fluidized bed measurements − because of the characteristics of nonintrusiveness, noninvasiveness, no radiation, fast imaging speed, withstanding harsh environments, and low cost . A variety of fluidized bed characteristics, such as minimum fluidization velocity, minimum bubbling velocity, minimum slugging velocity, bubble size, bubble rise velocity, solid concentration distribution, and dominant frequency of flow fluctuations in fluidized beds, could be measured by ECT. − Recently, Li et al used a twin-plane sensor mounted on a 59 mm diameter fluidized bed, with each plane having eight electrodes with 10 mm axial length, to evaluate bubble size. They first used a hollow phantom with different diameters to estimate the suitable cutoff value, to evaluate bubble diameters, and to compare the results with correlations.…”

Section: Introductionmentioning

confidence: 99%

“…Then, they , studied bubble characteristics in fluidized beds using a cutoff value of 0.2 (i.e., solid concentration) to identify bubbles and proposed models to predict bubble diameter, volumetric bubble flux, transition velocity from bubbling to slugging bed, bubble rise velocity, bubble frequency, and bed expansion. They also studied the effect of particle properties and bed height on bubble characteristics of fluidized beds. Chandrasekera et al considered the sharp transition between bubble and emulsion phases and proposed a new algorithm that penalizes the total variation of images to reconstruct images with sharp boundary.…”

Section: Introductionmentioning

confidence: 99%

Effect of Electrode Length of an Electrical Capacitance Tomography Sensor on Gas−Solid Fluidized Bed Measurements

Huang

Meng

Guo

et al. 2019

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Electrical capacitance tomography (ECT) has been widely used in fluidized bed measurements because of the noninvasive and nonintrusive features. The successful application of ECT in fluidized bed measurements depends on the appropriate design of sensors. This paper discusses the effect of ECT sensor length on fluidized bed measurements by simulations and experiments. The sensitivity distributions in different axial positions of a sensor are compared, which shows the similarity of sensitivity distribution contours and decline of intensity along the sensor axial direction. Fluidized beds of three different diameters (45, 55, and 65 mm) are used to compare the effect of sensor electrode length on fluidized bed measurements. In each fluidized bed, five sensors with different electrode lengths are tested. The experimental results show that the sensor electrode length has significant effect on bubble detection in fluidized bed measurements, and it is recommended that the sensor electrode length should be as short as possible considering the signal-to-noise ratio constraint.

show abstract

“…For example, the bed aspect ratio affects the temperature gradient and fluidizing fluid and reactant gas residence time inside the bed reactor, particle mixing and contact time, and chemical reaction time [50,[69][70][71][72]]. In addition, particle mixing becomes more challenging for the deep beds (i.e., beds with a high aspect ratio) with feedstock top-feeding (i.e., on the bed), due to the lightweight and density of the feedstock particles coupled with the slug formations (bubble formations with the diameter close or equal to the fluidized bed (column) diameter, see Section 1.2.1 Fluidization Regimes) [73].…”

Section: Figure 2 Gasification Reaction Diagrammentioning

confidence: 99%

A Study on the Hydrodynamics of a Bench-Scale Top-Fed Bubbling Fluidized Bed Gasifier using Biomass and Coal as Feedstocks

Sivri¹

View full text Add to dashboard Cite

The production of synthetic gas (syngas) from renewable or carbon-neutral sources can significantly reduce greenhouse and other emissions associated with conventional fuels. One of the most promising technologies to efficiently convert carbonaceous feedstocks such as biomass, coal, or municipal waste into syngas for transportation, power, heat, electricity generation, and or production of added-value chemicals is the bubbling fluidized-bed gasifier (BFBG). However, the gasification process inside a BFBG is a very complex high-temperature multiphase flow phenomena still not well understood, particularly when binary mixtures are investigated. As a result, despite the numerous correlations in the literature developed to predict the hydrodynamics inside a BFBG, the results are inconsistent, particularly for the minimum fluidization velocity, Umf.As predicting the fluidization hydrodynamics is paramount for optimum gasification, this investigation observed the effect of some of the most important fluidization parameters such as the particle size and shape, fluidizing gas properties, moisture content, bed aspect ratio etc. This study designed and built two separate experimental platforms: a bench-scale BFBG with automated feeding and a cold flow model with the same geometry and dimensions as the BFBG. The experiments used well-characterized (i.e., known size and shape distribution, density, moisture content, initial mixing condition) inert material (sand, glass beads) and feedstock (biomass (sawdust) and coal). The cold flow investigation results showed that the initial mixing conditions for binary mixtures with biomass had a significant effect on the measured Umf. For example, the relative error in predicting Umf using the available correlation in the literature increased for segregated mixtures. Moreover, lower relative errors in Umf suggested that the fluidization quality was better if the mixture was initially well-mixed (premixed). In addition, a larger biomass moisture content decreased Umf of premixed binary mixtures but increased the relative error between the predicted and the experimental Reynolds number, Re. After reaching the minimum fluidization condition, the fluidization behavior and mixing at various flow rates were also recorded with a high-speed camera. The processed images were used to determine the interval for the fluidizing-gas superficial velocity that produced the best mixing for a particular mixture composition and initial conditions. The images showed that while segregated biomass mixtures did not mix if the bed aspect ratio was larger than five, coal mixtures did mix homogeneously along the reactor bed. Finally, experiments performed at temperatures up to 800 C showed a large increase in the bed pressure drop at minimum fluidization velocity with the bed temperature due to the large effect on the fluidizing gas density and viscosity. On the contrary, Umf decreased when the process temperature increased. Finally, preliminary biomass and coal gasification experiments in the BFBG setup produced...

show abstract

Investigation of Bubbling Behavior in Deep Fluidized Beds at Different Gas Velocities using Electrical Capacitance Tomography

Cited by 17 publications

References 31 publications

ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-Based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-Based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

Effect of Electrode Length of an Electrical Capacitance Tomography Sensor on Gas−Solid Fluidized Bed Measurements

A Study on the Hydrodynamics of a Bench-Scale Top-Fed Bubbling Fluidized Bed Gasifier using Biomass and Coal as Feedstocks

Contact Info

Product

Resources

About