Artificial Neural Network Modeling for Sorption of Cadmium from Aqueous System by Shelled Moringa Oleifera Seed Powder as an Agricultural Waste

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Water Resource and Protection

ISSN Print: 1945-3094
ISSN Online: 1945-3108
www.scirp.org/Journal/jwarp
E-mail: jwarp@scirp.org

Google-based Impact Factor: 1.01
Citations h5-index & Ranking

Journals Menu

"Artificial Neural Network Modeling for Sorption of Cadmium from Aqueous System by Shelled Moringa Oleifera Seed Powder as an Agricultural Waste"
written by Abhishek Kardam, Kumar Rohit Raj, 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):

Google Scholar
CrossRef

[1]	COMPARISON BETWEEN TREATMENT OF RIVER NILE WATER WITH ALUMINUM SULPHATE AND AQUEOUS MORINGA OLEIFERA SEED EXTRACT
	2021

[2]	Kinetic and thermodynamic studies of sorption of lead and cadmium from aqueous solution by Moringa oleifera pod wastes
	2020

[3]	Modeling of Removal of Chromium (VI) from Aqueous Solutions Using Artificial Neural Network
	2020

[4]	Artificial neural network model for removal of copper ions from pollutant solutions by olives seeds powder
	2020

[5]	Arsenic Removal from Aqueous Solutions using Fe3O4-NaA Zeolite: Experimental and Modeling Investigations
	2020

[6]	A comparative study of artificial neural network models for the prediction of Cd removal efficiency of polymer inclusion membranes
	2019

[7]	Soft computing techniques in prediction Cr (VI) removal efficiency of polymer inclusion membranes
	2019

[8]	Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network
	Journal of Physics: Conference Series, 2018

[9]	Does the application of silicon and Moringa seed extract reduce heavy metals toxicity in potato tubers treated with phosphate fertilizers?
	Environmental Science and Pollution Research, 2018

[10]	EFFECT OF DIFFERENT MEDIA ON SEED GERMINATION AND IN-VITRO
	Journal of Forestry Research and Management, 2018

[11]	Establecimiento de la primera etapa de un cultivo in vitro de moringa (moringa oleifera lam.)
	2017

[12]	Moringa oleifera (drumstick tree) seed coagulant protein (MoCP) binds cadmium-preparation and characterization of nanoparticles
	2017

[13]	An ANN Model for Predicting the Quantity of Lead and Cadmium Ions in Industrial Wastewater
	International Journal of Information Communication Technologies and Human Development (IJICTHD), 2017

[14]	Chemically modified Moringa oleifera seed husks as low cost adsorbent for removal of copper from aqueous solution
	AIP Conference Proceedings, 2017

[15]	In vitro propagation of Moringa oleifera L. under salinity and ventilation conditions
	2016

[16]	Evaluate the Rate of Contamination in Soil by Using Modified Mathematical Methods
	2016

[17]	Farah Feasal Ghazi
	2016

[18]	Application of Statistical Techniques In Order To Improve Neural Modeling of Industrial Waste Water Treatment Plants
	2015

[19]	Umjetna inteligencija u zaštiti okoliša
	2015

[20]	به کارگیری روش های آماری برای افزایش دقت مدلسازی تصفیه خانه های فاضلاب صنعتی با استفاده از شبکه های عصبی مصنوعی‎
	2015

[21]	UMJETNA INTELIGENCIJA U ZA?TITI OKOLI?A
	THESIS, 2015

[22]	Application of factor analysis in a large‐scale industrial wastewater treatment plant simulation using principal component analysis–artificial neural network hybrid …
	… Progress & Sustainable …, 2015

[23]	Artificial neural networks for modeling the reverse osmosis unit in a wastewater pilot treatment plant
	Desalination and Water Treatment, 2015

[24]	Biocoagulation Activity of Moringa oleifera Seeds for Water Treatment
	2015

[25]	Knowledge and intelligent computing methods in e-learning
	International Journal of Technology Enhanced Learning, 2015

[26]	Application of factor analysis in a large‐scale industrial wastewater treatment plant simulation using principal component analysis–artificial neural network hybrid approach:(Case Study: Fajr Industrial Wastewater Treatment Plant, Mahshahr, Iran)
	Environmental Progress & Sustainable Energy, 2015

[27]	OPTIMIZATION OF FLUORIDE REMOVAL SYSTEM USING Ocimum SP. LEAVES AND RAGI SEED HUSK BY APPLYING BIO-STATISTICAL TOOLS
	Journal of Environmental Research and Development, 2015

[28]	ARTIFICIAL NEURAL NETWORK: A NOVEL METHOD FOR OPTIMIZATION OF BIOPRODUCTS AND BIOPROCESSES: A CRITICAL REVIEW
	MSR Journal of Sciences, 2014

[29]	Simulation and optimization of artificial neural network modeling for prediction of sorption efficiency of nanocellulose fibers for removal of Cd (II) ions from aqueous system.
	Walailak Journal of Science and Technology, 2014

[30]	RECENT ADVANCES IN BIOSORPTION OF HEAVY METALS: SUPPORT TOOLS FOR BIOSORPTION EQUILIBRIUM, KINETICS AND MECHANISM
	Revue Roumaine de Chimie, 2014

[31]	Biosorption Activity of Moringa oleifera Seeds Powder
	International Journal of Innovative Research and Development, 2014

[32]	Artificial Neural Network (ANN) Approach for Modeling Chromium (VI) Adsorption From Aqueous Solution Using a Borasus Flabellifer Coir Powder
	International Journal of Applied Science and Engineering 12 (3), 2014

[33]	Functionalization of nanocrystalline cellulose for decontamination of Cr (III) and Cr (VI) from aqueous system: computational modeling approach
	Clean Technologies and Environmental Policy, 2014

[34]	Nanocellulose fibers for biosorption of cadmium, nickel, and lead ions from aqueous solution
	Clean Technologies and Environmental Policy, 2014

[35]	Modeling of methane oxidation in landfill cover soil using an artificial neural network
	Journal of the Air & Waste Management Association, 2014

[36]	Voltammetric speciation of arsenic species in plant biomaterial: bioremediation
	Clean Technologies and Environmental Policy, 2014

[37]	Artificial Neural Network (ANN) Approach for Modeling Chromium (VI) Adsorption From Aqueous Solution Using
	International Journal of Applied Science and …, 2014

[38]	Simulation and optimization of artificial neural network modeling for prediction of sorption efficiency of nanocellulose fibers for removal of Cd (II) ions from aqueous …
	2014

[39]	Artificial neural network and response surface methodology approach for modeling and optimization of chromium (VI) adsorption from waste water using Ragi husk …
	Indian Chemical Engineer, 2013

[40]	Development of 'Environmental Friendly Aminated Nanocrystalline Cellulose'for Decontamination of Arsenic Species from Water Bodies: Bioremediation
	K Singh, R Rani, TJM Sinha, S Srivastava - ripublication.com, 2013

[41]	Research on Ability Evaluation Model of Compound Talents of Information Technology Based on Artificial Neural Network
	Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, 2013

[42]	Simulation and Optimization of Artificial Neural Network Modeling for Prediction of Sorption Efficiency of Nanocellulose Fibers for Removal of Cd (II) Ions from Aqueous System
	Walailak Journal of Science and Technology (WJST), 2013

[43]	Neural networks-based modeling applied to a process of heavy metals removal from wastewaters
	Journal of Environmental Science and Health, Part A, 2013

[44]	PEI modified Leucaena leucocephala seed powder, a potential biosorbent for the decontamination of arsenic species from water bodies: bioremediation
	Applied Water Science, 2013

[45]	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

[46]	FACULTAD DE CIENCIAS QUíMICAS
	2013

[47]	Uso de redes neuronales artificiales (RNA) para la modelación de una unidad de ósmosis inversa en una planta piloto de tratamiento de aguas residuales.
	2013

[48]	Nanopartículas de plata: Aplicaciones biomédicas
	Química Hoy, 2013

[49]	Adsorption behavior of dyes from aqueous solution using agricultural waste: modeling approach
	Clean Technologies and Environmental Policy, 2013

[50]	Equilibrium, kinetic and thermodynamic studies, modeling and optimization of the experimental data for the removal of chromium (vi) from waste water using low cost adsorbents
	2013

[51]	Simulation and Optimization of Artificial Neural Network Modeling for Prediction of Sorption Efficiency of Nanocellulose Fibers for Removal of Cd (II) Ions from …
	2013

[52]	Performance Simulation of H-TDS Unit of Fajr Industrial Wastewater Treatment Plant Using a Combination of Neural Network and Principal Component Analysis
	Journal of Water Resource and Protection, 2012

[53]	Simulation of low TDS and biological units of Fajr industrial wastewater Treatment plant using artificial neural network and principal component analysis hybrid method
	Journal of water resource and protection, 2012

[54]	Green Nanotechnology for Bioremediation of Toxic Metals from Waste Water
	Chemistry of Phytopotentials: Health, Energy and Environmental Perspectives. Springer Berlin Heidelberg, 2012

[55]	MATHEMATICAL MODELLING OF TRAFFIC NOISE IN AGRA CITY: ANN APPROACH (DECEMBER 2012)
	PV Mosahari, JK Arora - CONTROL SYSTEMS ENGINEERING, 2012

[56]	Novel nano cellulosic fibers for remediation of heavy metals from synthetic water
	INTERNATIONAL JOURNAL OF NANO DIMENSION (IJND), 2012

[57]	Nanotech reinforcement onto cellulosic fibers: green remediation of toxic metals
	International Journal of Green Nanotechnology, 2012

[58]	Investigating the Performance of Advanced Treatment Unit of Industrial Wastewater Treatment Plant Using Artificial Neural Network Model
	N Mehrdadi, H Hasanlou - psrcentre.org, 2012

[59]	Simulation and Optimization of Biosorption Studies for Prediction of Sorption Efficiency of Leucaena Leucocephala Seeds for the Removal of Ni (II) From Waste Water
	Chemistry of Phytopotentials: Health, Energy and Environmental Perspectives. Springer Berlin Heidelberg, 2012

[60]	Removal of Diesel Oil from Water Bodies Using Agricultural Waste Zea Mays Cob Powder
	Chemistry of Phytopotentials: Health, Energy and Environmental Perspectives. Springer Berlin Heidelberg, 2012

[61]	Simulation of low TDS and biological units of Fajr industrial wastewater Treatment plant using artificial neural network and principal component analysis …
	2012

[62]	Artificial Neural Network Modeling & Standardization of HPTLC Method for the Estimation of Cholesterol in Edible Oils
	Proceedings of the World Congress on Engineering, 2011

[1]	Biobased Materials 2023 DOI:10.1007/978-981-19-6024-6_12

[2]	Application of neural network in metal adsorption using biomaterials (BMs): a review Environmental Science: Advances, 2023 DOI:10.1039/D2VA00200K

[3]	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

[4]	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

[5]	Moringa oleifera: Miracle Plant with a Plethora of Medicinal, Therapeutic, and Economic Importance Horticulturae, 2022 DOI:10.3390/horticulturae8060492

[6]	Deep Learning and Neural Networks 2020 DOI:10.4018/978-1-7998-0414-7.ch011

[7]	Artificial neural network model for removal of copper ions from pollutant solutions by olives seeds powder IOP Conference Series: Materials Science and Engineering, 2020 DOI:10.1088/1757-899X/870/1/012097

[8]	Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes Environmental Engineering Research, 2019 DOI:10.4491/eer.2019.085

[9]	Does the application of silicon and Moringa seed extract reduce heavy metals toxicity in potato tubers treated with phosphate fertilizers? Environmental Science and Pollution Research, 2018 DOI:10.1007/s11356-018-1823-7

[10]	Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network Journal of Physics: Conference Series, 2018 DOI:10.1088/1742-6596/1003/1/012058

[11]	Chemically modified Moringa oleifera seed husks as low cost adsorbent for removal of copper from aqueous solution 2017 DOI:10.1063/1.5010524

[12]	An ANN Model for Predicting the Quantity of Lead and Cadmium Ions in Industrial Wastewater International Journal of Information Communication Technologies and Human Development, 2017 DOI:10.4018/IJICTHD.2017100103

[13]	Moringa oleifera (drumstick tree) seed coagulant protein (MoCP) binds cadmium - preparation and characterization of nanoparticles The EuroBiotech Journal, 2017 DOI:10.24190/ISSN2564-615X/2017/04.05

[14]	Moringa oleifera (drumstick tree) seed coagulant protein (MoCP) binds cadmium - preparation and characterization of nanoparticles The EuroBiotech Journal, 2017 DOI:10.24190/ISSN2564-615X/2017/04.05

[15]	Moringa oleifera (drumstick tree) seed coagulant protein (MoCP) binds cadmium - preparation and characterization of nanoparticles The EuroBiotech Journal, 2017 DOI:10.24190/ISSN2564-615X/2017/04.05

[16]	Application of factor analysis in a large‐scale industrial wastewater treatment plant simulation using principal component analysis–artificial neural network hybrid approach: (Case Study: Fajr Industrial Wastewater Treatment Plant, Mahshahr, Iran) Environmental Progress & Sustainable Energy, 2015 DOI:10.1002/ep.12120

[17]	Application of factor analysis in a large-scale industrial wastewater treatment plant simulation using principal component analysis-artificial neural network hybrid approach: (Case Study: Fajr Industrial Wastewater Treatment Plant, Mahshahr, Iran) Environmental Progress & Sustainable Energy, 2015 DOI:10.1002/ep.12120

[18]	Modeling of methane oxidation in landfill cover soil using an artificial neural network Journal of the Air & Waste Management Association, 2014 DOI:10.1080/10962247.2013.842510

[19]	Nanocellulose fibers for biosorption of cadmium, nickel, and lead ions from aqueous solution Clean Technologies and Environmental Policy, 2014 DOI:10.1007/s10098-013-0634-2

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

[21]	Functionalization of nanocrystalline cellulose for decontamination of Cr(III) and Cr(VI) from aqueous system: computational modeling approach Clean Technologies and Environmental Policy, 2014 DOI:10.1007/s10098-014-0717-8

[22]	Adsorption behavior of dyes from aqueous solution using agricultural waste: modeling approach Clean Technologies and Environmental Policy, 2013 DOI:10.1007/s10098-012-0480-7

[23]	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

[24]	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

[25]	Neural networks-based modeling applied to a process of heavy metals removal from wastewaters Journal of Environmental Science and Health, Part A, 2013 DOI:10.1080/10934529.2013.781896

[26]	Artificial neural networks for modeling the reverse osmosis unit in a wastewater pilot treatment plant Desalination and Water Treatment, 2013 DOI:10.1080/19443994.2013.862023

[27]	Research on Ability Evaluation Model of Compound Talents of Information Technology Based on Artificial Neural Network 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2013 DOI:10.1109/IHMSC.2013.111

[28]	Modelación de un tratamiento avanzado de aguas residuales, provenientes de una planta de envasado, usando las metodologías de análisis de superficie de respuesta (ASR) y de redes neuronales (RNA) Quimica Hoy, 2013 DOI:10.29105/qh3.2-97

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

[30]	Chemistry of Phytopotentials: Health, Energy and Environmental Perspectives 2012 DOI:10.1007/978-3-642-23394-4_52

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

[32]	Nanotech Reinforcement onto Cellulosic Fibers: Green Remediation of Toxic Metals International Journal of Green Nanotechnology, 2012 DOI:10.1080/19430892.2012.654744

[33]	Simulation of Low TDS and Biological Units of Fajr Industrial Wastewater Treatment Plant Using Artificial Neural Network and Principal Component Analysis Hybrid Method Journal of Water Resource and Protection, 2012 DOI:10.4236/jwarp.2012.46042

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Water Resource and Protection

Journal of Water Resource and Protection

Journals Menu

Home

About SCIRP

Service

Policies