AIMC Topic: Charcoal
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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.
Environmental monitoring and assessment
Jun 17, 2020
Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research c...
Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators.
Journal of visualized experiments : JoVE
Apr 25, 2020
Ionic electromechanically active capacitive laminates are a type of smart material that move in response to electrical stimulation. Due to the soft, compliant and biomimetic nature of this deformation, actuators made of the laminate have received inc...
Single and competitive dye adsorption onto chitosan-based hybrid hydrogels using artificial neural network modeling.
Journal of colloid and interface science
Oct 29, 2019
Chitosan-based hybrid hydrogels such as chitosan hydrogel (CH), chitosan hydrogel with activated carbon (CH-AC), scaffold-chitosan hydrogel (SCH), scaffold-chitosan hydrogel with activated carbon (SCH-AC) and scaffold-chitosan hydrogel with carbon na...
The application of machine learning methods for prediction of metal sorption onto biochars.
Journal of hazardous materials
Jun 3, 2019
The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regres...
Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.
Chemosphere
Oct 11, 2018
Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of co...
Modeling and optimization by particle swarm embedded neural network for adsorption of zinc (II) by palm kernel shell based activated carbon from aqueous environment.
Journal of environmental management
Dec 7, 2017
Zn (II) is one the common pollutant among heavy metals found in industrial effluents. Removal of pollutant from industrial effluents can be accomplished by various techniques, out of which adsorption was found to be an efficient method. Applications ...
Impact of density of coating agent on antibacterial activity of silver nanoparticle impregnated plasma treated activated carbon.
Journal of environmental sciences (China)
Aug 30, 2017
To use stabilized nanoparticles (NPs) in water as disinfectants over a very long period, the amount of coating agent (for NP stabilization) needs to be optimized. To this end, silver nanoparticles (Ag-NPs) with two different coating densities of tri-...
Modified Activated Carbon Prepared from Acorn Shells as a New Solid-Phase Extraction Sorbent for the Preconcentration and Determination of Trace Amounts of Nickel in Food Samples Prior to Flame Atomic Absorption Spectrometry.
Journal of AOAC International
Nov 17, 2016
A new solid-phase extraction (SPE) sorbent was introduced based on acidic-modified (AM) activated carbon (AC) prepared from acorn shells of native oak trees in Kurdistan. Hydrochloric acid (15%, w/w) and nitric acid (32.5%, w/w) were used to conditio...
Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach.
Journal of environmental sciences (China)
Jul 5, 2016
Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) a...
Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.
Environmental science and pollution research international
Jul 11, 2015
Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and developmen...
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