Neural Network Modeling for Ni(II) Removal from Aqueous System Using Shelled Moringa Oleifera Seed Powder as an Agricultural Waste

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Journal of Water Resource and Protection

ISSN Print: 1945-3094
ISSN Online: 1945-3108
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"Neural Network Modeling for Ni(II) Removal from Aqueous System Using Shelled Moringa Oleifera Seed Powder as an Agricultural Waste"
written by Kumar Rohit Raj, Abhishek Kardam, Jyoti Kumar Arora, Man Mohan Srivastava, Shalini Srivastava,
published by Journal of Water Resource and Protection, Vol.2 No.4, 2010

has been cited by the following article(s):

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[8]	REMOCIÓN DE COBRE (II) EN SISTEMAS ACUOSOS USANDO CÁPSULAS DE MORINGA OLEIFERA: INFLUENCIA DEL pH.
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[11]	KINETICS OF METAL POLLUTANTS BIOSORPTION IN LAKE VICTORIA AND ITS ENVIRONS USING MORINGA OLEIFERA SEED POWDER
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[13]	Un estudio de la remoción de manganeso (II) a partir de sistemas acuosos usando cápsulas de moringa oleifera como bioadsorbente/A study of the removal of manganese (II) from aqueous systems using the moringaoleifera pods as bioadsorbent
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[15]	Bioadsorção de metais pela semente da Moringa oleífera: avaliação do processo empregando a fluorescência de raios X por reflexão total com radiação síncroton
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[46]	Un estudio de la remoción de manganeso (II) a partir de sistemas acuosos usando cápsulas de moringa oleifera como bioadsorbente/A study of the …
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[1]	Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network Environmental Monitoring and Assessment, 2023 DOI:10.1007/s10661-022-10904-0

[2]	Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network Environmental Monitoring and Assessment, 2023 DOI:10.1007/s10661-022-10904-0

[3]	Carboxyl appended polymerized seed composite with controlled structural properties for enhanced heavy metal capture Separation and Purification Technology, 2022 DOI:10.1016/j.seppur.2021.120247

[4]	Carboxyl appended polymerized seed composite with controlled structural properties for enhanced heavy metal capture Separation and Purification Technology, 2022 DOI:10.1016/j.seppur.2021.120247

[5]	Modeling the adsorption of benzeneacetic acid on CaO2 nanoparticles using artificial neural network Resource-Efficient Technologies, 2016 DOI:10.1016/j.reffit.2016.10.004

[6]	Functionalized nanocrystalline cellulose: Smart biosorbent for decontamination of arsenic International Journal of Mineral Processing, 2015 DOI:10.1016/j.minpro.2015.04.014

[7]	Voltammetric speciation of arsenic species in plant biomaterial: bioremediation Clean Technologies and Environmental Policy, 2014 DOI:10.1007/s10098-013-0633-3

[8]	Potentiality of uranium biosorption from nitric acid solutions using shrimp shells Journal of Environmental Radioactivity, 2014 DOI:10.1016/j.jenvrad.2014.03.007

[9]	Cellulosic Nanocomposites: Functional Vector For Arsenic Remediation i-manager's Journal on Material Science, 2014 DOI:10.26634/jms.1.4.2691

[10]	Evaluation of the Opuntia dillenii as Natural Coagulant in Water Clarification: Case of Treatment of Highly Turbid Surface Water Journal of Water Resource and Protection, 2013 DOI:10.4236/jwarp.2013.512133

[11]	The Design and Implementation of Adsorptive Removal of Cu(II) from Leachate Using ANFIS The Scientific World Journal, 2013 DOI:10.1155/2013/590267

[12]	Efficient arsenic depollution in water using modified maize powder Environmental Chemistry Letters, 2013 DOI:10.1007/s10311-012-0376-0

[13]	An application of ANN Modeling on the Biosorption of Arsenic Waste and Biomass Valorization, 2013 DOI:10.1007/s12649-012-9164-0

[14]	PEI modified Leucaena leucocephala seed powder, a potential biosorbent for the decontamination of arsenic species from water bodies: bioremediation Applied Water Science, 2013 DOI:10.1007/s13201-012-0057-y

[15]	Artificial Neural Network and Response Surface Methodology Approach for Modeling and Optimization of Chromium (VI) Adsorption from Waste Water using Ragi Husk Powder Indian Chemical Engineer, 2013 DOI:10.1080/00194506.2013.829257

[16]	Prediction of the As(III) and As(V) Abatement Capacity of Zea mays Cob Powder: ANN Modelling National Academy Science Letters, 2013 DOI:10.1007/s40009-012-0101-3

[17]	Development of polyethylenimine modified Zea mays as a high capacity biosorbent for the removal of As (III) and As (V) from aqueous system International Journal of Mineral Processing, 2013 DOI:10.1016/j.minpro.2013.02.010

[18]	Development of Experimental Results by Artificial Neural Network Model for Adsorption of Cu2+Using Single Wall Carbon Nanotubes Separation Science and Technology, 2013 DOI:10.1080/01496395.2012.738276

[19]	Chemistry of Phytopotentials: Health, Energy and Environmental Perspectives 2012 DOI:10.1007/978-3-642-23394-4_53

[20]	Chemistry of Phytopotentials: Health, Energy and Environmental Perspectives 2012 DOI:10.1007/978-3-642-23394-4_79

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