Minimum Fluidization Velocities of Binary Solid Mixtures: Empirical Correlation and Genetic Algorithm‐Artificial Neural Network Modeling

Let, Sudipta; Bar, Nathalie; Basu, Rikmantra; Das, Sudip

doi:10.1002/ceat.202100170

Cited by 20 publications

(5 citation statements)

References 38 publications

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“…The detailed experimental setup was described earlier. [36] Initially, the liquid was pumped to the columns through the liquid distribution section from the reservoir through the pump and was controlled by a valve. Solid particles were mixed in the different mass ratios for various particle sizes and loaded into the column.…”

Section: Methodsmentioning

confidence: 99%

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Bed expansion of binary mixtures of irregular particles in solid–liquid fluidization: Experimental, empirical correlation, and GA‐ANN modelling

et al. 2022

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Expansion behaviour for a bed of binary mixture of the irregularly shaped particle in Newtonian liquid was measured in two different circular columns. Variations in the physical parameters on the expansion behaviour have been reported. Bed expansion increases with an increase in liquid velocity and a decrease in particle diameter. Static bed height and expansion of the bed are low for higher diameter columns. An empirical correlation has been developed for predicting the ratio of bed height at the fluidized condition to the initial bed height as a function of the physical and dynamic variables related to the system for the binary particle mixtures. The correlation coefficient and variance of the estimate are 0.9299 and 0.0013, respectively, which is acceptable statistical accuracy. A hybrid of the genetic algorithm and neural network modelling for the prediction of the same has also been attempted where the input parameters are optimized using the Levenberg–Marquardt algorithm. With a relative error of 1.46%, the genetic algorithm performed well. So, the modelling has successfully predicted the bed height ratio at fluidized conditions to the initial bed height.

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Section: Methodsmentioning

confidence: 99%

“…The detailed particle diameters, sphericities, and their mixture parameters are reported in our earlier paper. [ 36 ]…”

Section: Methodsmentioning

confidence: 99%

Bed expansion of binary mixtures of irregular particles in solid–liquid fluidization: Experimental, empirical correlation, and GA‐ANN modelling

et al. 2022

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“…In the optimization part of the genetic algorithm, the weights and thresholds of the BP neural network are mainly optimized by the genetic algorithm, and each individual of the population in the genetic algorithm contains the weights and thresholds of the BP neural network [24][25]. The fitness function calculates individual fitness values, and the selection operation, crossover operation and mutation operation are performed sequentially in the genetic algorithm to determine the individual corresponding to the optimal fitness value.…”

Section: Optimization Of Bp Neural Network By Genetic Algorithmmentioning

confidence: 99%

Voltage transformer metering error state prediction method based on GA-BP algorithm

Gao,

Zhao,

Jiang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The metering accuracy of the voltage transformer is related to the normal operation of the power system, and the metering results can be optimized through the prediction of the error state. In this paper, according to the generation mechanism of the measurement error of the transformer, the maximum information coefficient is used to extract the error characteristic quantity, and the measurement perturbation model is constructed by combining the ambient temperature and the secondary load factor. Due to the specificity of the ambient temperature, a BP neural network is also used to compensate for the temperature of the perturbation model, which prepares for the improved BP neural network based on a genetic algorithm to recognize the error data. Finally, the simulated operation of the three-phase voltage transformer and the measured data of the wiring substation were utilized for validation, respectively. With the help of three-phase CVT simulation, the error change of A-phase simulated CVT amplitude information at the 4001st sampling point is 0.0962%, and the error change of phase information is -4.572′.GA-BP neural network also has high sensitivity to the difficult-to-detect asymptotic error and is able to realize the error calibration of voltage transformer.

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“…In recent years, the study of spherical beds fluidized by non-NFs has gained momentum due to its applications in the production of microbial mass in fermenters, extraction of metals from leached pulps by ion exchange, food and polymer processing, biotechnological applications, − biochemical reactors with a bioagent attacher, catalytic polymerization in a hydroxidation process, and fluidized bed electrodes . However, in comparison to NFs, little information is available for both non-Newtonian inelastic fluids and viscoelastic fluids (VEFs).…”

Section: Introductionmentioning

confidence: 99%

Minimum Fluidization Velocity and Expansion Behavior of Spherical Particle Beds Fluidized with Inelastic and Viscoelastic Fluids

Kaur,

Sobti,

Wanchoo

2023

Ind. Eng. Chem. Res.

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Experimental investigations depicting the effect of pseudoplasticity and fluid elasticity on the minimum fluidization velocity and bed expansion behavior of spherical particles are presented. Experiments have been carried out to fluidize the beds of spherical particles using different types of fluids, viz. Newtonian fluid (NF), inelastic fluid, viscoelastic (shear-thinning) fluid (VEF), and Boger (almost constant viscosity) fluid (BF) , covering a wide range of conditions: 0.002 < Re t < 4163; 0.48 < n ≤ 1; 0.42 ≤ λ ≤ 2.07 s. Based on the experimental data and pictorial representation, it is observed that the course of bed expansion follows nonhomogeneous fluidization behavior along with channeling for VEFs, which is predominant in shear-thinning VEFs compared to BFs. Further, the minimum fluidization velocity decreases as the elasticity and shear thinning of the fluid increase. This reduction in minimum fluidization velocity represented as a function of elasticity number (E) follows asymptotic behavior, which shows the dominance of viscous forces over elastic forces at a higher Reynolds number. A simple form of corrective elasticity number function f(E) is proposed to extend the use of the equation proposed by Bena, applicable for NFs, for predicting the minimum fluidization velocity in the case of VEFs as well as BFs. Further, the equation proposed by Garside and Al-Dibouni, applicable for NFs, has also been extended to predict bed expansion behavior for VEFs and BFs.

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Minimum Fluidization Velocities of Binary Solid Mixtures: Empirical Correlation and Genetic Algorithm‐Artificial Neural Network Modeling

Cited by 20 publications

References 38 publications

Bed expansion of binary mixtures of irregular particles in solid–liquid fluidization: Experimental, empirical correlation, and GA‐ANN modelling

Bed expansion of binary mixtures of irregular particles in solid–liquid fluidization: Experimental, empirical correlation, and GA‐ANN modelling

Voltage transformer metering error state prediction method based on GA-BP algorithm

Minimum Fluidization Velocity and Expansion Behavior of Spherical Particle Beds Fluidized with Inelastic and Viscoelastic Fluids

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