Related references
Note: Only part of the references are listed.The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning
Nawal Taoufik et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2022)
Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects
Gulzar Alam et al.
CHEMICAL ENGINEERING JOURNAL (2022)
Prediction of arsenic removal in aqueous solutions with non-neural network algorithms
Noor Hafsa et al.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING (2021)
Modelling of the adsorption of Pb, Cu and Ni ions from single and multi-component aqueous solutions by date seed derived biochar: Comparison of six machine learning approaches
Ali El Hanandeh et al.
ENVIRONMENTAL RESEARCH (2021)
Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model
Suraj Kumar Bhagat et al.
CHEMOSPHERE (2021)
Pre-deposited dynamic membrane adsorber formed of microscale conventional iron oxide-based adsorbents to remove arsenic from water: application study and mathematical modeling
Muhammad Usman et al.
JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY (2021)
Adsorption of copper(II) on chemically modified biochar: a single-stage batch adsorber design and predictive modeling through artificial neural network
Krishna Yadav et al.
BIOMASS CONVERSION AND BIOREFINERY (2021)
Amine-Terminated PAN Membranes as Anion-Adsorber Materials
Sarah Glass et al.
CHEMIE INGENIEUR TECHNIK (2021)
Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network
Bo Ke et al.
JOURNAL OF ENVIRONMENTAL MANAGEMENT (2021)
Technical aspects of selected minerals transformation to LDH-containing materials: The structure, chemistry and affinity towards As(V)
Karolina Rybka et al.
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING (2021)
Removal of mercury from polluted water by a novel composite of polymer carbon nanofiber: kinetic, isotherm, and thermodynamic studies
Mohammad Al-Yaari et al.
RSC ADVANCES (2021)
Influence of chitosan and magnetic iron nanoparticles on chromium adsorption behavior of natural clay: Adaptive neuro-fuzzy inference modeling
Rauf Foroutan et al.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES (2020)
Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel
Yong Jie Wong et al.
ENVIRONMENTAL MONITORING AND ASSESSMENT (2020)
Mathematical modeling of arsenic(V) adsorption onto iron oxyhydroxides in an adsorption-submerged membrane hybrid system
Muhammad Usman et al.
JOURNAL OF HAZARDOUS MATERIALS (2020)
A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms
Noor Hafsa et al.
WATER (2020)
Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction
Phong Tung Nguyen et al.
APPLIED SCIENCES-BASEL (2019)
A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings' Energy Efficiency for Smart City Planning
Le Thi Le et al.
APPLIED SCIENCES-BASEL (2019)
Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis
Binh Thai Pham et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2019)
Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases
Mosbeh R. Kaloop et al.
APPLIED SCIENCES-BASEL (2019)
Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
Hai Xu et al.
APPLIED SCIENCES-BASEL (2019)
Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt
Hoang-Long Nguyen et al.
APPLIED SCIENCES-BASEL (2019)
Hybrid computational intelligence models for groundwater potential mapping
Binh Thai Pham et al.
CATENA (2019)
The application of machine learning methods for prediction of metal sorption onto biochars
Xinzhe Zhu et al.
JOURNAL OF HAZARDOUS MATERIALS (2019)
Prediction of shear strength of soft soil using machine learning methods
Binh Thai Pham et al.
CATENA (2018)
Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS
Maryam Dolatabadi et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2018)
Prediction of phycoremediation of As(III) and As(V) from synthetic wastewater by Chlorella pyrenoidosa using artificial neural network
M. S. Podder et al.
APPLIED WATER SCIENCE (2017)
Preparation of CuO nanoparticles using Tamarindus indica pulp extract for removal of As(III): Optimization of adsorption process by ANN-GA
Devendra Kumar Singh et al.
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING (2017)
Technologies for Arsenic Removal from Water: Current Status and Future Perspectives
Nina Ricci Nicomel et al.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2016)
The use of artificial neural network for modelling of phycoremediation of toxic elements As(III) and As(V) from wastewater using Botryococcus braunii
M. S. Podder et al.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY (2016)
Artificial neural network modelling of As(III) removal from water by novel hybrid material
S. Mandal et al.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2015)
Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting
Mohammad Esmaeili et al.
ENGINEERING WITH COMPUTERS (2014)
A nanofiltration-coagulation integrated system for separation and stabilization of arsenic from groundwater
P. Pal et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2014)
Arsenic Contamination of Groundwater: A Review of Sources, Prevalence, Health Risks, and Strategies for Mitigation
Shiv Shankar et al.
TheScientificWorldJOURNAL (2014)
Removal of arsenic from drinking water: A comparative study between electrocoagulation-microfiltration and chemical coagulation-microfiltration processes
Cesar Calderon Molgora et al.
SEPARATION AND PURIFICATION TECHNOLOGY (2013)
Adsorption studies of arsenic(III) removal from water by zirconium polyacrylamide hybrid material (ZrPACM-43)
Sandip Mandal et al.
WATER RESOURCES AND INDUSTRY (2013)
Arsenic(V) removal from underground water by magnetic nanoparticles synthesized from waste red mud
Ilker Akin et al.
JOURNAL OF HAZARDOUS MATERIALS (2012)
On sampling strategies for small and continuous data with the modeling of genetic programming and adaptive neuro-fuzzy inference system
S. Sen et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2012)
Artificial neural network (ANN) approach for modelling of arsenic (III) biosorption from aqueous solution by living cells of Bacillus cereus biomass
A. K. Giri et al.
CHEMICAL ENGINEERING JOURNAL (2011)
Share Paper
