The effects of different operating parameters such as initial pH, initial Pb(II) ion concentration, adsorbent dosages, and contact time are studied to optimize the conditions for maximum batch adsorptive removal of Pb(II) ions from aqueous solution using six different low cost natural bio-sorbents. Applicability of artificial neural network (ANN) analysis is investigated for the removal of Pb(II). Three standard training algorithms (Backpropagation, Levenberg-Marquardt and Scaled Conjugate Gradient) along with four different standard transfer functions in a hidden layer with a linear transfer function in the output layer are used for the analysis. Statistical analysis show that the ANN model with a BP algorithm, using transfer function 1 and 25 processing elements in a single hidden layer gives best predictability of the percentage removal of Pb(II) ions from aqueous solutions. © 2015 Elsevier B.V. All rights reserved.

SUDIP DAS

Chemical Engineering

B SINGHA

N BAR

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Journal of Molecular Liquids

The bed expansion characteristics in inverse fluidization using non-Newtonian liquids are reported. Experiments have been carried out using single and binary systems of four different polymeric solids and four different non-Newtonian pseudoplastic liquids in two different columns. Empirical correlation has been developed to determine the bed expansion characteristics as a function of physical and dynamic variables of the system. A multilayer perceptron trained with backpropagation and Levenberg-Marquardt algorithm have been used for the Artificial Neural Network (ANN) analysis. Four different standard transfer functions in a single hidden layer are used. The ANN model with Levenberg-Marquardt algorithm with transfer function 2 having 12 processing elements in hidden layer gives good predictability of the bed height. Statistical analysis indicates that both the empirical correlation and the ANN prediction give acceptable results. © 2014 Elsevier B.V.

Powder Technology

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

In this study, an artificial neural network (ANN) based techniques is applied for the prediction of the percentage removal of Cr(VI) ions from aqueous solution using eight different natural biosorbents. The effects of operating parameters such as initial pH, initial Cr(VI) ion concentration, adsorbent dosages, and contact time are studied to optimize the conditions for maximum removal of Cr(VI) ions. The ANN with a single hidden layer trained with Levenberg-Marquardt algorithm predicted the percentage removal of Cr(VI) ions from aqueous solution accurately. © 2013 © 2013 Balaban Desalination Publications. All rights reserved.

Desalination and Water Treatment

The use of artificial neural networks (ANN) for modeling of adsorption of Cr(VI) ions

Hyacinth root was used as a biosorbent for generating adsorption data in fixed-bed glass column. The influence of different operating parameters like inlet Pb(II) ion concentration, liquid flow rate and bed height on the breakthrough curves and the performance of the column was studied. The result showed that the adsorption efficiency increased with increase in bed height and decreased with increase in inlet Pb(II) ion concentration and flow rate. Increasing the flow rate resulted in shorter time for bed saturation. The result showed that as the bed height increased the availability of more number of adsorption sites in the bed increased, hence the throughput volume of the aqueous solution also increased. The adsorption kinetics was analyzed using different models. It was observed that maximum adsorption capacity increased with increase in flow rate and initial Pb(II) ion concentration but decreased with increase in bed height. Applicability of artificial neural network (ANN) modeling for the prediction of Pb(II) ion removal was also reported by using multilayer perceptron with backpropagation, Levenberg-Marquardt and scaled conjugate algorithms and four different transfer functions in a hidden layer and a linear output transfer function. © 2014 Elsevier B.V.

Journal of Hazardous Materials

Removal of Pb(II) ions from aqueous solution using water hyacinth root by fixed-bed column and ANN modeling

Purpose: The purpose of the research is to investigate the applicability of the low-cost natural biosorbents for the removal of Pb(II) ions from aqueous solution and effluent from battery industry. Methods: Six different biosorbents namely rice straw, rice bran, rice husk, coconut shell, neem leaves, and hyacinth roots have been used for the removal of Pb(II) ions from aqueous solution in batch process. All the biosorbents were collected from local area near Kolkata, West Bengal, India. The removal efficiency was determined in batch experiments for each biosorbent. Results: The biosorbents were characterized by SEM, FTIR, surface area, and point of zero charge. The sorption kinetic data was best described by pseudo-second-order model for all the biosorbents except rice husk which followed intraparticle diffusion model. Pb(II) ions adsorption process for rice straw, rice bran, and hyacinth roots were governed predominately by film diffusion, but in the case of rice husk, it was intraparticle diffusion. Film diffusion and intraparticle diffusion were equally responsible for the biosorption process onto coconut shell and neem leaves. The values of mass transfer coefficient indicated that the velocity of the adsorbate transport from the bulk to the solid phase was quite fast for all cases. Maximum monolayer sorption capacities onto the six natural sorbents studied were estimated from the Langmuir sorption model and compared with other natural sorbents used by other researchers. The Elovich model, the calculated values of effective diffusivity, and the sorption energy calculated by using the Dubinin-Radushkevich isotherm were indicated that the sorption process was chemical in nature. The thermodynamic studies indicated that the adsorption processes were endothermic. FTIR studies were carried out to understand the type of functional groups responsible for Pb(II) ions binding process. Regeneration of biosorbents were carried out by desorption studies using HNO3. Battery industry effluents were used for the application study to investigate applicability of the biosorbents. Conclusion: The biosorbents can be utilized as low-cost sorbents for the removal of Pb(II) ions from wastewater. © 2012 Springer-Verlag.

Environmental Science and Pollution Research

Removal of Pb(II) ions from aqueous solution and industrial effluent using natural biosorbents

Experiments have been carried out to determine the two-phase frictional pressure drop across 90° and 135° bend for gas-non-Newtonian liquid flow on the horizontal plane. Empirical correlation has been developed to predict the twophase friction factor using the physical and dynamic variables of the system. The applicability of Artificial Neural Networks (ANN) methodology have also been reported. The ANN prediction have been reported using Multilayer Perceptrons (MLP) trained with five different algorithms, namely: Backpropagation (BP), Scaled Conjugate gradient (SCG), Delta-Bar-Delta (DBD), Levenberg -Marquardt (LM), Quick-Prop (QP). Four different transfer functions were used in a single hidden layer for all algorithms. The X-square test confirms that the best network for prediction of frictional pressure drop is when it is trained with Backpropagation algorithm in the hidden and output layer with the transfer function 4 in hidden layer having 13 processing elements for 90° bend. The X-square test also confirms that the best network for prediction of frictional pressure drop is when it is trained with Levenberg -Marquardt algorithm in the hidden and output layer with the transfer function 1 in hidden layer having 7 processing elements for 135° bend. Both the methods are equally predictive in nature but the empirical correlation is based on the physical and dynamic variables of the system, whereas the ANN prediction is not dependent on the individual relationship between the input variables. © 2012 Begell House, Inc.

Journal	Data powered by TypesetJournal of Molecular Liquids
Publisher	Data powered by TypesetElsevier
ISSN	0167-7322