"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):
  • Google Scholar
  • CrossRef
[1] Modelling of lead removal from battery industrial wastewater treatment sludge leachate on cement kiln dust by using Elman's RNN
International Journal of Global Warming, 2017
[2] ワサビノキ (モリンガ) の種子・葉に含まれる有用成分とその多目的利用
熱帯農業研究, 2016
[3] Modeling the adsorption of benzeneacetic acid on CaO 2 nanoparticles using artificial neural network
Resource-Efficient Technologies, 2016
[4] Comparison between Neural Network and Genetic Algorithm in Prediction Adsorption Capacity of Natural Sorbent
International Statistics Days Conference, 2016
[5] REMOCIÓN DE COBRE (II) EN SISTEMAS ACUOSOS USANDO CÁPSULAS DE MORINGA OLEIFERA: INFLUENCIA DEL pH.
Acta Microscopica, 2016
[6] 5. REMOCIÓN DE COBRE (II) EN SISTEMAS ACUOSOS USANDO CÁPSULAS DE MORINGA OLEIFERA: INFLUENCIA DEL pH
2016
[7] 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
Revista CENIC. Ciencias Biológicas, 2015
[8] Evaluation of Aloe vera leaf gel as a Natural Flocculant: Phytochemical Screening and Turbidity removal Trials of water by Coagulation flocculation
Research Journal of Recent Sciences, 2015
[9] Bioadsorção de metais pela semente da Moringa oleífera: avaliação do processo empregando a fluorescência de raios X por reflexão total com radiação síncroton
2015
[10] Un estudio de la remoción de manganeso (II) a partir de sistemas acuosos usando cápsulas de moringa oleifera como bioadsorbente
2015
[11] 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
[12] Cellulosic Nanocomposites: Functional Vector For Arsenic Remediation
i-Manager's Journal on Material Science, 2014
[13] Moringa Oleifera as a Low Cost Adsorbent
HYDROLOGY AND WATERSHED MANAGEMENT: Ecosystem Resilience-Rural and Urban Water Requirements, 2014
[14] 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, 2014
[15] Potential of M. oleifera for the treatment of water and wastewater
Chemical Reviews, 2014
[16] Artificial Neural Network (ANN) Approach for Modeling Chromium (VI) Adsorption From Aqueous Solution Using
International Journal of Applied Science and Engineering, 2014
[17] Potentiality of uranium biosorption from nitric acid solutions using shrimp shells
Journal of environmental radioactivity, 2014
[18] PREDICTION OF ADSORPTION EFFICIENCY FOR THE REMOVAL MALACHITE GREEN AND ACID BLUE 161 DYES BY WASTE MARBLE DUST USING ANN
2014
[19] Voltammetric speciation of arsenic species in plant biomaterial: bioremediation
Clean Technologies and Environmental Policy, 2014
[20] 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
[21] 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
[22] Development of Experimental Results by Artificial Neural Network Model for Adsorption of Cu2+ Using Single Wall Carbon Nanotubes
Separation Science and Technology, 2013
[23] Prediction of the As (III) and As (V) Abatement Capacity of Zea mays Cob Powder: ANN Modelling
National Academy Science Letters, 2013
[24] Adsorption behavior of dyes from aqueous solution using agricultural waste: modeling approach
Clean Technologies and Environmental Policy, 2013
[25] The design and implementation of adsorptive removal of Cu (II) from leachate using ANFIS
The Scientific World Journal, 2013
[26] An application of ANN Modeling on the Biosorption of Arsenic
Waste and Biomass Valorization, 2013
[27] Efficient arsenic depollution in water using modified maize powder
Environmental Chemistry Letters, 2013
[28] PEI modified Leucaena leucocephala seed powder, a potential biosorbent for the decontamination of arsenic species from water bodies: bioremediation
Applied Water Science, 2013
[29] 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
[30] 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
[31] Neural networks-based modeling applied to a process of heavy metals removal from wastewaters
Journal of Environmental Science and Health, Part A, 2013
[32] 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
[33] Green Nanotechnology for Bioremediation of Toxic Metals from Waste Water
Chemistry of Phytopotentials: Health, Energy and Environmental Perspectives. Springer Berlin Heidelberg, 2012
[34] MATHEMATICAL MODELLING OF TRAFFIC NOISE IN AGRA CITY: ANN APPROACH (DECEMBER 2012)
PV Mosahari, JK Arora - CONTROL SYSTEMS ENGINEERING, 2012
[35] Artificial Neural Network Modelling of Traffic Noise in Agra-Firozabad Highway
International Journal of Computer Applications, 2012
[36] Artificial Neural Network Modeling & Standardization of HPTLC Method for the Estimation of Cholesterol in Edible Oils
Proceedings of the World Congress on Engineering, 2011
[37] Waste of rapeseed from biodiesel production as a potential biosorbent for heavy metal ions
BioResources, 2011
[38] Evaluating Multiple Heavy Metal Pollutants in Soil by Artificial Neural Network: A Case Study in Baotou, China
Energy Procedia, 2011
[39] Artificial Neural Network modelling for the System of blood flow through tapered artery with mild stenosis
International Journal of Mathematics Trends & Technology, 2011