Holdup prediction in inverse fluidization using non-Newtonian pseudoplastic liquids: Empirical correlation and ANN modeling

Das, Bimal; Ganguly, U. P.; Bar, Nathalie; Das, Sudip

doi:10.1016/j.powtec.2014.12.034

Cited by 22 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The validity of the FF Multilayer Perceptron Neural Networks (MLPNN), with a backpropagation reinforcement learning training algorithm, has been demonstrated in the literature, see Haykin [67]. Several scientists [5,12,32,43,89,125,134,136] have employed ANNs to model the mechanical properties of materials.…”

Section: Backpropagation Efficacymentioning

confidence: 99%

“…We followed a series of standardized procedures to characterize these particles, as explained by various authors (Carson and Wilms [33], Das et al [43], Ganesan et al [47], Lumay et al [95], Moysey et al [103], Simsek et al [126], Snider [128]). These experimental data were later used to determine DEM numerical parameters, as explained in Chapters 7 and 8.…”

Section: Experimental Characterizationmentioning

confidence: 99%

See 1 more Smart Citation

Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

2016

View full text Add to dashboard Cite

Numerous industries process particles. In this work, we focused on how to efficiently picture the behaviour of particles by means of numerical simulations, laboratory experiments, and Artificial Neural Networks (ANNs). Particle-particle contact laws and particles size distributions determine the macroscopic results in Discrete Element Method (DEM) simulations. Commonly, contact laws depend on semi-empirical parameters which are difficult to obtain by direct microscopic measurements. To clarify this aspect, we present the related elements of the DEM theory. The ANN theory is also introduced to demonstrate ANN effectiveness towards the solution of inverse problems with non linear regression. Later, we describe the series of small scale DEM simulations with different sets of particle-based simulation parameters and particle distributions, which we performed. The macroscopic results of these simulations were used to train dedicated feed-forward ANNs by the backward propagation reinforcement algorithm. Concurrently, the bulk behaviours of raw particles were characterized by means of macroscopic laboratory experiments. These particles were those commonly used by metallurgical industries. At this point, the relationship between macroscopic results and microscopic DEM simulation parameters could be investigated. We subsequently utilized this artificial neural network to predict the macroscopic ensemble behaviour in relation to additional sets of particle-based simulation parameters and particle distributions. By this method, a comprehensive database was established, relating particle-based simulation parameters to macroscopic ensemble output. If compared to an experiment of a specific granular material, this database identifies valid sets of DEM parameters which lead to the same macroscopic results as observed in the experiments. Finally, we applied the results of this method of DEM parameter identification to two industrial scale processes of steel production.

show abstract

Section: Backpropagation Efficacymentioning

confidence: 99%

Section: Experimental Characterizationmentioning

confidence: 99%

Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

2016

View full text Add to dashboard Cite

show abstract

“…6 represent the cross-validation curve for training with the LM algorithm. A similar type of procedure had been followed in our earlier work [11][12][13][14][15][16][17]. Table 4 presents various values of the statistical parameters related to the neural network analysis for final prediction.…”

Section: Application Of Annmentioning

confidence: 99%

“…The usefulness of artificial neural network (ANN) for the study of prediction by various researchers is discussed in detail in our earlier studies [11][12][13][14][15][16][17]. The aim of this paper is to investigate the applicability of ANN on percentage removal of Pb(II) ions from aqueous solution onto rice straw, rice bran, rice husk, hyacinth roots, neem leaves and coconut shell as low cost natural bio-adsorbents.…”

Section: Introductionmentioning

confidence: 99%

The use of artificial neural network (ANN) for modeling of Pb(II) adsorption in batch process

Singha¹,

Bar

Das

2015

Journal of Molecular Liquids

Self Cite

View full text Add to dashboard Cite

“…Data modeling has been performed using multiple linear regression (MLR) (Nag et al, 2020;Das et al, 2020;Mandal et al, 2020) and Artificial Neural Network (ANN) (Kumar et al, 2019;Mitra et al, 2014;Das et al, 2015;Maiti et al, 2018) with success for the last few years. Proper analysis of data produces beneficial results if interpreted correctly using a statistically approved process like MLR.…”

Section: Introductionmentioning

confidence: 99%

Biosorption of Cu(ii) Ions From Industrial Effluents by Rice Husk: Experiment, Statistical, and Ann Modeling

Bar

Mitra

Das

2021

Journal of Environmental Engineering and Landscape Management

Self Cite

View full text Add to dashboard Cite

Heavy metal removal from wastewater is a significant research area and recommends sustainable development. The heavy metals cause harmful health effects, increase environmental toxicity. Adsorption is a very effective method for heavy metal removal. A fixed bed for Cu(II) removal using rice hush, an agricultural waste, is reported in this paper. The study was carried out to determine the breakthrough curves with varying operating variables like influent concentration (10–30 mg/L), flow rate (10–40 ml/min), and bed height (4–10 cm) at pH 6. The variation of the process variables like influent concentration, flow rate, and bed height were investigated. The experimental data shows that adsorption capacity increases with the rise of influent concentration. The maximum value of adsorption capacity is 10.93 mg/g at a flow rate of 10 ml/min, bed height 4 cm, and influent concentration 30 mg/L. The applicability of the MLR and ANN modeling has also been successfully carried out. ANN has better predictability than MLR. The findings revealed that rice husk could be used to treat copper-containing industrial effluents.

show abstract

Holdup prediction in inverse fluidization using non-Newtonian pseudoplastic liquids: Empirical correlation and ANN modeling

Cited by 22 publications

References 17 publications

Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments

The use of artificial neural network (ANN) for modeling of Pb(II) adsorption in batch process

Biosorption of Cu(ii) Ions From Industrial Effluents by Rice Husk: Experiment, Statistical, and Ann Modeling

Contact Info

Product

Resources

About