Terminal settling velocity for binary irregularly shaped particle mixture from fluidization study: experiment, empirical correlation, and GA-ANN modeling

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

doi:10.1080/02726351.2022.2056551

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…The application of the Levenberg–Marquardt algorithm has been very successful in optimizing the input parameters for GA for improved generation 67,68 . Then, the iterative process produced the generation for which the least cross‐validation error was produced.…”

Section: Resultsmentioning

confidence: 99%

“…The application of the Levenberg-Marquardt algorithm has been very successful in optimizing the input parameters for GA for improved generation. 67,68 Then, the iterative process produced the generation for which the least cross-validation error was produced. Researchers used a procedure like minor modification of the chromosomes by maintaining the mutation probability at 0.1 and the crossover probability at 0.9.…”

Section: Data Analysis With Genetic Algorithmmentioning

confidence: 99%

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Adsorptive elimination of methylene blue dye from aqueous solution by chitosan‐nSiO₂ nanocomposite: Adsorption and desorption study, scale‐up design, statistical, and genetic algorithm modeling

Bhattacharya,

Bar,

Rajbansi

et al. 2023

Env Prog and Sustain Energy

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Heavy metals and organic dyes in water in concentrations exceeding the tolerable limit are unsafe for aquatic life and humans. Chitosan nanocomposites show a potential adsorption capacity to organic dyes. The present study encompasses the preparation of chitosan‐nSiO2 nanocomposites (CSNC) with different weight ratios of chitosan to nSiO2 and its application in the methylene blue (MB) dye adsorption. The nanocomposites were characterized with the help of SEM, BET, FTIR, XRD, and TGA. The adsorption experimentation was executed in batch mode under varying experimental conditions. Several isotherm and kinetic models were analyzed with the experimental data, and thermodynamic conditions required for adsorption were also determined. Maximum Langmuir adsorption capacities (qL) of the adsorbents for MB varied in the range of 21.32–31.34 mg g−1. The pseudo‐second‐order model was the best‐fitted kinetic model concerning all three adsorbents. The statistical modeling using multiple polynomial regression (MPR) for CWS CSNC1‐1 and CSNC2‐1 yielded the equations with R‐square ranging from 0.984 to 0.996. For GA modeling, it is more than 0.999. So, the efficient employment of statistical modeling, and genetic algorithms has also been achieved.

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Section: Resultsmentioning

confidence: 99%

Section: Data Analysis With Genetic Algorithmmentioning

confidence: 99%

Adsorptive elimination of methylene blue dye from aqueous solution by chitosan‐nSiO₂ nanocomposite: Adsorption and desorption study, scale‐up design, statistical, and genetic algorithm modeling

Bhattacharya,

Bar,

Rajbansi

et al. 2023

Env Prog and Sustain Energy

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“…A similar process was used by researchers in the past. 20,21,57,58 Variation of generation number with respect to the generation error was described in Figure 16. Table 8 gave the result of GA analysis.…”

Section: F I U R E 1mentioning

confidence: 99%

“…19 The hybrid of GA and artificial neural network (ANN) was employed as an optimization technique over the past few years. 20,21 This work uses GA-ANN to speculate the percentage removal of Cu(II).…”

mentioning

confidence: 99%

Synthesis of chitosan‐nTiO₂ nanocomposite, application in adsorptive removal of Cu(II)—Adsorption and desorption study, mechanism, scale‐up design, statistical, and genetic algorithm modeling

Bhattacharya

Bar²,

Rajbansi

et al. 2023

Applied Organom Chemis

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The present study comprises the preparation of chitosan-nTiO 2 nanocomposites and their characterization with the help of scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). Various adsorbent samples are prepared with different weight ratios of chitosan to nTiO 2 . The adsorption capacity of the prepared adsorbents towards Cu(II) in an aqueous solution is also tested. The adsorption experiment is performed in batch mode under varying experimental conditions. Several isotherm and kinetic models are analyzed with the experimental data, and thermodynamic conditions required for adsorption are also determined. The highest elimination of Cu(II) was 39.19% with the adsorbent chitosan without nTiO 2 (CWT) with 20 mg L À1 initial Cu (II) concentration at adsorbent dosage 3 gL À1 and 98.5% with the adsorbent CTNC1-1 (chitosan/nTiO 2 nanocomposite with the mass proportions of chitosan:nTiO 2 = 1:1) and 93% with CTNC2-1 (chitosan/nTiO 2 nanocomposite with the mass proportions of chitosan:nTiO 2 = 2:1) with 10 mg L À1 initial Cu(II) concentration at adsorbent dosage 5 g L À1 . The isotherm model confirmed the monolayer adsorption.Maximum Langmuir adsorption capacities (q L ) of the adsorbents for Cu(II) were given as CTNC1-1 > CTNC2-1 > CWT. The pseudo-second-order model excellently depicted the kinetic process with good correlation for all adsorbents. The statistical (R-square is more than 0.99) and genetic algorithm (GA) model (R-square is more than 0.99) effectively predicted the percentage removal of Cu(II).

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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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Terminal settling velocity for binary irregularly shaped particle mixture from fluidization study: experiment, empirical correlation, and GA-ANN modeling

Cited by 11 publications

References 35 publications

Adsorptive elimination of methylene blue dye from aqueous solution by chitosan‐nSiO₂ nanocomposite: Adsorption and desorption study, scale‐up design, statistical, and genetic algorithm modeling

Adsorptive elimination of methylene blue dye from aqueous solution by chitosan‐nSiO₂ nanocomposite: Adsorption and desorption study, scale‐up design, statistical, and genetic algorithm modeling

Synthesis of chitosan‐nTiO₂ nanocomposite, application in adsorptive removal of Cu(II)—Adsorption and desorption study, mechanism, scale‐up design, statistical, and genetic algorithm modeling

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

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Terminal settling velocity for binary irregularly shaped particle mixture from fluidization study: experiment, empirical correlation, and GA-ANN modeling

Cited by 11 publications

References 35 publications

Adsorptive elimination of methylene blue dye from aqueous solution by chitosan‐nSiO2 nanocomposite: Adsorption and desorption study, scale‐up design, statistical, and genetic algorithm modeling

Adsorptive elimination of methylene blue dye from aqueous solution by chitosan‐nSiO2 nanocomposite: Adsorption and desorption study, scale‐up design, statistical, and genetic algorithm modeling

Synthesis of chitosan‐nTiO2 nanocomposite, application in adsorptive removal of Cu(II)—Adsorption and desorption study, mechanism, scale‐up design, statistical, and genetic algorithm modeling

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

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Adsorptive elimination of methylene blue dye from aqueous solution by chitosan‐nSiO₂ nanocomposite: Adsorption and desorption study, scale‐up design, statistical, and genetic algorithm modeling

Adsorptive elimination of methylene blue dye from aqueous solution by chitosan‐nSiO₂ nanocomposite: Adsorption and desorption study, scale‐up design, statistical, and genetic algorithm modeling

Synthesis of chitosan‐nTiO₂ nanocomposite, application in adsorptive removal of Cu(II)—Adsorption and desorption study, mechanism, scale‐up design, statistical, and genetic algorithm modeling