Abstract
Metal pollutants such as copper released into the aqueous environment have been increasing as a result of anthropogenic activities. Adsorption-based treatment technologies offer opportunities to remediate metal pollutants from municipal and industrial wastewater effluent. The aim of this work was to evaluate the capability of modified cellulose nanowhisker (CNW) adsorbents for the remediation of copper from water matrices under realistic conditions using response surface methodology (RSM) and artificial neural network (ANN) models. Considerations for design and application to remediate Cu(II) from wastewater by developing a continuous flow experiment are described in this study. However, the physical structure of modified CNW adsorbents renders them unsuitable for use in column operation. Therefore, a more detailed study of the mechanical properties of CNW adsorbents would be necessary in order to improve the strength and stability of the adsorbents. This work has demonstrated that modified CNW are promising adsorbents to remediate copper from water matrices under realistic conditions including wastewater complexity and variability. The use of models to predict the test parameter system and account for matrix variability when evaluating CNW adsorbents for remediating Cu from a real-world wastewater matrix may also provide the foundation for assessing other treatment technologies in the future.
Highlights
The complexity and variability of wastewater is difficult to model and simulate using traditional modelling procedures
The statistical aspects of response surface methodology (RSM) and artificial neural network (ANN) enable the identification of factors that have a significant effect on the adsorption process and are able to provide a large amount of knowledge from a small number of experimental runs
Ghosh et al, (2013) applied RSM with central composite design (CCD) to investigate the removal of Cu(II) from aqueous solution using modified orange peel, and their study showed that pH, sorbent dosage and initial metal ion concentration influenced the adsorption process
Summary
The complexity and variability of wastewater is difficult to model and simulate using traditional modelling procedures. Because of the interaction between a numbers of adsorption variables/factors, the resulting relationships are highly non-linear and require a large number of experiments This has placed increasing demands on both research and process optimization, and has resulted in the increased use of RSM and ANN modelling tools. An ANN model was developed by Krishna and Sree (2013) to predict the removal efficiency of Cr(VI) from aqueous solution using coir powder as adsorbent They found that the model and the test data showed a high R2 value (0.992), and the ANN model successfully tracked the non-linear behaviour of percentage removal of Cr(VI) versus independent variables, with low relative percentage error. Oguz and Ersoy (2010) studied the feasibility of sunflower shell for the removal of Cu(II) from aqueous solution in a fixed-bed adsorption column with an ANN approach They noted that ANN effectively predicted the removal efficiency of Cu(II) using sunflower shell as adsorbent. To the variation in wastewater compositions, results obtained from the benchmark experiments are included as one of the independent variables for ANN modelling, unlike in other optimization studies
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