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Cottoning Onto Novel Adsorbents: Modified Cotton For Remediation Of Copper From The Water Matrix Using Response Surface Methodology (rsm) And Artificial Neural Network (ann)

Congress: 2015
Author(s): Nor Hazren Abdul Hamid, Youla Jenidi, Joshua Pilkington, Wim Thielemans, Christopher Somerfield, Rachel Gomes
University of Nottingham1, KU Leuven Campus Kortrijk2

Keyword(s): Sub-theme 13: Non-conventional sources of water,
Abstract

Introduction

Heavy metals released into the water environment have been increasing as a result of anthropogenic activities such as mining, sludge disposal, and electroplating, with the effects of these metals on the ecosystem causing global concern (Wang 2009; Wang 2013). Adsorption offers an alternative to the remediation of industrial and municipal effluent as conventional technologies such as ion exchange, reverse osmosis, filtration, electrochemical treatment, and membrane technologies are expensive and generate large amount of sludge (O'Connell, Birkinshaw et al. 2006). The adsorption process is very effective especially in the case of low concentrations of pollutants in water matrix, where common methods are either economically unfavorable or technically complicated in removing these pollutants from the water matrix. Cellulose is one of the abundant renewable natural polymers and has potential for modification to improve the adsorption capacity for the removal of copper from the water matrix. A carboxyl group is one example functional groups that can be attached on the cellulose surface by 2, 2, 6, 6-tetramethylpiperidine 1-oxyl (TEMPO)-mediated oxidation and provide good adsorption capacity.

The main aim of the study was to determine the effectiveness of (TEMPO)-mediated oxidation cellulose nanowhiskers (CNWs) functionalized with carboxyl at removing copper ions from water. Copper is a toxic heavy metal frequently identified in wastewater and was used as a case study to determine the efficiency of modified CNWs in removing copper from water. This study investigated the combined effects of several factors, utilizing central composite design (CCD). Response surface methodology (RSM) and artificial neural network (ANN) models were employed to understand data fit and determine the predictive capability of each model for effective copper removal from water matrix.

Methods

CNWs were prepared from bleached cotton by hydrolysis with 64% sulphuric acid to produce a suspension of highly crystalline CNWs. The resulting nanowhiskers were then reacted with 2,2,6,6-tetramethyl-pyperidine-1-oxy radical (TEMPO), sodium bromide, and sodium hypochlorite for 45 minutes under constant stirring at 25°c, followed by recovery by freeze-drying from the water dispersion and ready for the next adsorption experiment in a batch system. An adsorption study of copper ions from water was also conducted by measuring the effects of temperature, initial copper concentration, and adsorbent dosage. The design of experiment (DoE) is an important aspect of RSM. Scoping experiments were undertaken in the early stages of the process, to identify the most appropriate parameters and ranges that influenced the adsorption process, with appreciation of operating under realistic environmental wastewater conditions.

Results and discussion

From the scoping studies, optimization studies were carried out by studying the effect of these three variables. With these three variables, a total of 20 experiments (Table 1) were required in order to find the optimum operating condition for the adsorption of Cu (II) using modified CNWs. Adsorption studies were conducted in the range of 10-60 mg/L initial copper concentration in water, at temperature in the range of 6-25°c and with sorbent dosage in the range of 0.2-10 g/L. The equilibrium time was also determined from the scoping studies where the process achieved its equilibrium after 30 minutes, thus the contact time was set at 30 minutes for all adsorption studies. The percentage removal was mathematically described as a function of experimental parameters and was modelled through RSM. The significance of each term in the equation on the percentage of the adsorbed Cu (II) ions and the adsorption capacity of adsorbent (q) will be validated by the statistical test known as ANOVA analysis. RSM will be used for the experimental results obtained from the effects of different variables on the percentage of adsorbed Cu (II) ions and q in order to evaluate the individual and cumulative effects of these independent variables and the interactions between them.

Table 1: The order and conditions for the central composite design (CCD)

Conclusion

The adsorption of copper on modified CNWs were carried out in a batch system and were optimized by using RSM and ANN to predict the optimum values of parameters affecting the adsorption process to achieve maximum removal efficiency. Study showed that sorbent dosage, initial concentration, and temperature were used as independent variables as these three parameters contribute more to adsorption. The percentage of the adsorbed Cu (II) ions and the adsorption capacity of adsorbent (q) at the end of 30 mins were taken as dependent (output) variables. Finally, with the optimization using RSM and ANN, heavy metals such as copper ions can be removed by using the renewable natural polymers such as CNWs which is abundant and is a very promising raw material available at low cost for the preparation of various functional polymers.

1. O'Connell, D. W., C. Birkinshaw, et al. (2006). A modified cellulose adsorbent for the removal of nickel(II) from aqueous solutions. Journal of Chemical Technology and Biotechnology 81(11): 1820-1828.

2. Wang, J., Chen, C. (2009). Biosorbents for heavy metals removal and their future. Biotechnology Advances 27(2): 195-226.

3. Wang, S., Wei, M., Huang, Y. (2013). Biosorption of Multifold Toxic Heavy Metal Ions from Aqueous Water onto Food Residue Eggshell Membrane Functionalized with Ammonium Thioglycolate. Journal of Agricultural and Food Chemistry 61(21): 4988-4996.

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