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.

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

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

Desalination and Water Treatment

In the present batch study, eucalyptus leaves (EUL), H2SO4-treated eucalyptus leaves (SEUL), and H3PO4-treated eucalyptus leaves (PEUL) are used as bio-adsorbents for the removal of methylene blue (MB). The bio-adsorption is executed to inspect the results of the variation between different experimental variables such as pH (2–10), adsorbent dose (1–10 g/L), contact time (5–360 min), and temperature (298–318 K) on the bio-adsorption of MB. The Langmuir isotherm (R2 = 0.99) fitted adequately to the bio-adsorption data for the initial MB concentrations of 10–300 mg/L. It is also necessary to mention that the MB bio-adsorption occurred in the order of a monolayer on the EUL, SEUL, and PEUL. The bio-adsorption kinetics have been fitted by the pseudo-second-order model (R2 ≥ 0.99) for various MB concentrations. The maximum bio-adsorption capacity was 194.34 mg/g and was achieved for the H3PO4-treated eucalyptus leaves (PEUL). These results showed that EUL, SEUL, and PEUL may be utilized as a favourable low-cost bio-adsorbent to eliminate MB from aqueous solutions. With safe disposal methods in mind, this investigation has revealed the eco-friendliness of the bio-adsorbents. A prediction of the removal percentage of methylene blue using a genetic algorithm (GA) from the data collected from the experiment has also been tested. The results related to the prediction using the GA-ANN are accurate. © 2019 Canadian Society for Chemical Engineering

Canadian Journal of Chemical Engineering

Removal of methylene blue (aq) using untreated and acid-treated eucalyptus leaves and GA-ANN modelling

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.

Journal	Desalination and Water Treatment
Publisher	Desalination Publications
ISSN	1944-3994