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

ASIT BISWAS

Chemical Engineering

SUDIP DAS

K GHOSH

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

Canadian Journal of Chemical Engineering

The use of sustainable, green and biodegradable natural wastes for Cr(VI) detoxification from the contaminated wastewater is considered as a challenging issue. The present research is aimed to assess the effectiveness of seven different natural biomaterials, such as jackfruit leaf, mango leaf, onion peel, garlic peel, bamboo leaf, acid treated rubber leaf and coconut shell powder, for Cr(VI) eradication from aqueous solution by biosorption process. Characterizations were conducted using SEM, BET and FTIR spectroscopy. The effects of operating parameters, viz., pH, initial Cr(VI) ion concentration, adsorbent dosages, contact time and temperature on metal removal efficiency, were studied. The biosorption mechanism was described by the pseudo-second-order model and Langmuir isotherm model. The biosorption process was exothermic, spontaneous and chemical (except garlic peel) in nature. The sequence of adsorption capacity was mango leaf &gt; jackfruit leaf &gt; acid treated rubber leaf &gt; onion peel &gt; bamboo leaf &gt; garlic peel &gt; coconut shell with maximum Langmuir adsorption capacity of 35.7 mg g−1 for mango leaf. The treated effluent can be reused. Desorption study suggested effective reuse of the adsorbents up to three cycles, and safe disposal method of the used adsorbents suggested biodegradability and sustainability of the process by reapplication of the spent adsorbent and ultimately leading towards zero wastages. The performances of the adsorbents were verified with wastewater from electroplating industry. The scale-up study reported for industrial applications. ANN modelling using multilayer perception with gradient descent (GD) and Levenberg-Marquart (LM) algorithm had been successfully used for prediction of Cr(VI) removal efficiency. The study explores the undiscovered potential of the natural waste materials for sustainable existence of small and medium sector industries, especially in the third world countries by protecting the environment by eco-innovation. © 2017, Springer-Verlag Berlin Heidelberg.

Environmental Science and Pollution Research

Biosorption of chromium (VI) from aqueous solutions and ANN modelling

In this present study, the technical feasibility of rice husk ash for adsorptive removal of Cr(VI) and Cu(II) is examined in packed bed from aqueous solutions. The effects of different operating parameters such as height of the packed bed column (5–15 cm), flow rate of aqueous metal ion solution (10–30 ml min−1), and initial metal ion concentration (10–30 mg L−1) were studied. Capacity of the bed to absorb metal ions was found to increase with an increase in bed height, decrease in flow rate, and metal ion concentration. Adams–Bohart, Yoon–Nelson, and Thomas models were applied to the experimental data for the prediction of breakthrough point and also to determine the model parameters to characterize the column. The Yoon–Nelson model and Thomas model were applicable for Cr(VI) removal, and for Cu(II), Thomas model was suited. © 2015 Balaban Desalination Publications. All rights reserved.

Desalination and Water Treatment

Removal of Cr(VI) and Cu(II) ions from aqueous solution by rice husk ashâ€”column studies

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.

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

The potentiality of low cost natural/agricultural waste biomasses for the removal of Cu(II) ion from aqueous solution has been investigated in batch experiments. The effect of various physico-chemical parameters such as initial pH, initial Cu(II) concentration, adsorbent dosage, contact time and temperature has been studied. The optimum pH for adsorption was found to be 6 for all adsorbents used. Kinetics data were best described by the pseudo-2nd-order model. The experimental data were fitted well with Freundlich and Halsey isotherm models. The diffusion coefficient and sorption energy indicated that the adsorption process was chemical in nature. Thermodynamic parameters such as Δ G°, Δ H° and Δ S° were calculated, and it was observed that the adsorption process was spontaneous and endothermic. The mean sorption energy was calculated using Dubinin-Radushkevich isotherm model and it confirmed that the sorption process was chemical in nature. Different active functional groups were identified by FTIR studies which were responsible for Cu(II) ion adsorption process. Application study using electroplating industrial waste water and regeneration experiment of the adsorbent were also investigated. Design procedure for the batch process was also reported. © 2013 Elsevier B.V.

Colloids and Surfaces B: Biointerfaces

Adsorptive removal of Cu(II) from aqueous solution and industrial effluent using natural/agricultural wastes

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 TypesetCanadian Journal of Chemical Engineering
Publisher	Data powered by TypesetWiley-Liss Inc.
ISSN	0008-4034
Open Access	No