Experimental Investigation on Interaction of Side Gas Injection with Gas Fluidized Bed Using γ-Ray Transmission Technique

Bhowmick, Sandip; Sharma, Vijay Kumar; Samantray, J.S.; Pant, H.J.; Shenoy, K.T.; Dash, Ashutosh; Roy, Somnath

doi:10.1021/acs.iecr.5b02741

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…Coal particle size has a moderate impact on the temperatures from the volatile gases. The liquid feed rate is expected to have little impact on the void fraction below the nozzle measured with gamma-ray transmission [14] , and little effect on the velocity below the nozzle [15] , consistent with the results shown here. Within the ranges in the study, bed and coal particle sizes have the most impact on void fraction and low particle velocity.…”

Section: Experimental Design Materials and Methodssupporting

confidence: 85%

Response surface data for sensitivity study of industrial spray injected fluidized bed reactor

Abboud¹,

Guillen²

2018

Data in Brief

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An industrial fluidized bed reactor was designed to convert an aqueous solid laden stream into a consistent granular product. CFD simulations were run using the MFiX two-fluid model for a fluidizing bed operating at 650 °C. A set of simulations were run over a Latin-hypercube sample of five model parameters – bed particle size, bed particle density, coal particle size, spray feed flow rate, and fluidizing gas flow rate. Data from the simulations were collected on three quantities of interest – bed differential temperature, low solids velocity, and bed void fraction. The data presented here is the full set of response surfaces generated using the process Gaussian response surface model in the Dakota toolkit, as well as the table of data for coefficients of the fitted model. The fits to the five-dimensional Gaussian Process models were 0.7797, 0.8664, and 0.9440 for the temperature, velocity, and solids packing, respectively.

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Section: Experimental Design Materials and Methodssupporting

confidence: 85%

Response surface data for sensitivity study of industrial spray injected fluidized bed reactor

Abboud¹,

Guillen²

2018

Data in Brief

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Important advances have contributed to the understanding of the fluidized bed phenomena and equipment monitoring. Various probes and corresponding monitoring methods have been introduced, such as the temperature−pressure method, 1,2 the γ-ray method, 3 the fiber-optic method, 4 the electrostatic method, 5 and the acoustic method. 6 As temperature and pressure sensors are installed in most FBRs, the corresponding monitoring method, which is known as the temperature−pressure method and is based on pressuredrop and temperature signals, is the most widely used technique in industrial production.…”

Section: Introductionmentioning

confidence: 99%

Agglomeration-Monitoring Method for a Fluidized Bed with Multiacoustic Sensors

Chen

Jing

et al. 2019

Ind. Eng. Chem. Res.

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The acoustic method is sensitive to agglomerations in fluidized bed reactors; however, a single acoustic sensor is not sufficient for industrial-scale reactors with regard to size, environmental noise, and signal pollution. Therefore, a multiacoustic sensor-based monitoring approach is investigated in this study. Different agglomeration warning models are established on the basis of each separate sensor, and then information-fusion technology is introduced. In the modeling process, an early warning method for industrial applications is designed. First, singular value decomposition is introduced for feature extraction to simplify the signal preprocessing and reduce the number of preset parameters. Thereafter, as agglomeration samples are lacking in industrial data, support vector data description (SVDD), an unsupervised learning method, is employed to establish the agglomeration warning model according to samples under normal conditions. As the Boolean outputs of SVDD are not suitable for information fusion, the output probability estimation method for SVDD is studied. In the estimation method, additional sigmoid functions are incorporated into the SVDD models, and the maximum likelihood algorithm is combined with the expectation−maximization algorithm to optimize the parameters of the sigmoid functions. Finally, the Dempster−Shafer evidence theory is applied for information fusion. Experiments conducted on a pilot plant prove that the proposed early agglomeration detection method is more stable than the S statistics of acoustic signals. The multisensor-based warning method can provide better detection of agglomeration than the temperature−pressure method, and for industrial applications, it provides more reliable detection results than single-sensor systems.

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Experimental Investigation on Interaction of Side Gas Injection with Gas Fluidized Bed Using γ-Ray Transmission Technique

Cited by 2 publications

References 31 publications

Response surface data for sensitivity study of industrial spray injected fluidized bed reactor

Response surface data for sensitivity study of industrial spray injected fluidized bed reactor

Agglomeration-Monitoring Method for a Fluidized Bed with Multiacoustic Sensors

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