Volatile organic compounds (VOCs) are the most frequently occurring pollutants in
groundwater which can impart significant health hazards to the end user even at levels of …

The application of artificial neural networks on adsorption modeling has significantly
increased during the last decades. These artificial intelligence models have been utilized to …

The objective of this work is to compare the efficiency of three computational intelligence
techniques: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and …

A new low-cost activated carbon (AC) was produced from pulp and paper mill sludge
through chemical activation by zinc chloride. Different characterization analyses were …

Background: Escalation of industrial processes continues to increase the concentrations of
Cr (VI) in wastewater above permissible discharge limits. Persistent exposure to Cr (VI) may …

This paper illustrates the application of artificial neural network (ANN) for prediction of
performances in competitive adsorption of phenol and resorcinol from aqueous solution by …

The aim of this work was to model multi-system dynamic adsorption using an artificial
intelligence technique. A set of data points, collected from scientific papers containing the …

Abstract Artificial Neural Networks are applied to the literature data pertaining to adsorption
batch studies to develop and validate a model that can predict the Pollutant Removal …

Granular activated carbon is a frequently explored technology for removing synthetic organic
contaminants from drinking water sources. The success of this technology relies on a …

A three-layered feed-forward artificial neural network (ANN) model has been designed to
predict the adsorption efficiency and adsorption capacity for the adsorptive removal of …