The development of machine learning-based ensemble models for the prediction of complex
processes with non-linear nature (such as adsorption) has been remarkably advanced over …

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

This study aims to explore the potential of integrating Design of Expert (DOE) with Machine
Learning (ML) to optimize and predict the adsorption process of solid adsorbent The …

Organic waste-derived carbon-based materials (CBMs) are commonly applied in
sustainable wastewater treatment and waste management. CBMs can remove toxic, non …

It is crucial to reduce the concentration of pollutants in water environment to below safe
levels. Some cost-effective pollutant removal technologies have been developed, among …

In the present study, hybrid prediction models were used to estimate the adsorption of
ammonium from landfill leachate by using zeolite in batch and column systems. The effects …

This study investigates chromium removal onto modified maghemite nanoparticles in batch
experiments based on a central composite design. The effect of modified maghemite …

Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies
of different heavy metals have been limited by the adsorbate–adsorbent pair and the …

In the present study, adsorption of methylene blue dye in residual agricultural biomass
(orange bagasse) was modelled using o machine learning algorithm Random Forest (RF) …

Predictive models are useful tools for aqueous adsorption research; existing models such as
multilinear regression (MLR), however, can only predict adsorption under specific …