Development of new mathematical modelling for remediation process: case studies on remediation of copper from water matrices using cellulose nanowhisker adsorbents

Abdul Hamid, Nor Hazren (2017) Development of new mathematical modelling for remediation process: case studies on remediation of copper from water matrices using cellulose nanowhisker adsorbents. PhD thesis, University of Nottingham.

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Metal pollutants such as copper released into the aqueous environment have been increasing as a result of anthropogenic activities, a topic causing global concern. 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.

The first part of the study explored the preparation and characterisation of modified CNW adsorbents. It also focused on the stability of the modified CNW adsorbents at different time intervals under dry conditions (up to 28 days) and in the water matrix (up to 7 days). The results showed that the modified CNW adsorbents were stable at different time intervals under dry conditions and in the water matrix and proved that the functional groups were permanent and did not degrade under the tested conditions. The stability of these modified CNW adsorbents under these conditions, which is relevant from both the manufacturing and application perspectives, is reported for the first time in this study.

The second part of the work focused on using copper as a case study for heavy metal pollution in a clean water matrix, to evaluate removal by modified CNWs under several conditions and ranges appropriate to wastewater treatment plants (WWTPs), using factorial experimental design. RSM and ANN models were employed in order to optimise the system and to create a predictive model to evaluate the Cu(II) removal performance by the modified CNW adsorbents. Moreover, unseen experiments not belonging to the training data set, located both inside and outside the test parameter system, were performed to test the model suitability. This is also novel, as generally only one or two parameter variations have been tested, without checking the chosen model suitability for parameters lying between the tested parameters, and certainly not for parameters lying outside the tested parameter space, as has been done in this study. The results obtained showed that the ANN model outperformed the RSM model when predicting copper removal from a clean water matrix. The Langmuir andFreundlich isotherm models were applied to the equilibrium data, and the results revealed that the Langmuir isotherm (R2 = 0.9998) had better correlation than the Freundlich isotherm (R2 = 0.9461). Experimental data was also tested in terms of kinetics studies using pseudo-first order and pseudo-second order kinetic models. The results showed that the pseudo-second-order model accurately described the kinetics of adsorption.

The third part of the work was aimed at gaining a deeper understanding of the complexity and variability of the wastewater matrix, including evaluating the impact of the wastewater matrix temporally on adsorbent performance to remediate copper pollutant from a real-world wastewater matrix. This study has demonstrated that the wastewater matrix composition, which is both complex and variable, has an impact on adsorbent capability and performance. A benchmark study was adopted as a ‘new’ water quality parameter to inform on the effects of the wastewater matrix (wastewater composition and its variability) on the modified CNW adsorbent’s capability to remediate copper from this matrix. Since the process of adsorption from wastewater is often complicated due to the variation in wastewater composition, results obtained from the benchmark experiments were included as one of the independent variables in ANN modelling, unlike in other optimisation studies. The performance of the ANN and RSM models was statistically evaluated in terms of coefficient of determination (R2), absolute average deviation (AAD), and root mean squared error (RMSE) on predicted experimental outcomes. The ANN model including the variability of wastewater composition fitted the experimental data with excellent accuracy and better prediction (R2 = 0.9963) than both the ANN model that did not include this variability (R2 = 0.9945), and the RSM model (R2 = 0.9409). The outcome of this study showed that by supplying the ANN model with the data obtained from the benchmark experiments as the fourth independent variable, it was possible to improve the predictability of the ANN model.

Continuous flow experiments for remediation of spiked Cu(II) from the wastewater matrix were conducted. 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.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Gomes, Rachel
Somerfield, Christopher
Keywords: Artificial neural networks, adsorption, Cu(II) ions, cellulose nanowhiskers, Response surface methodology
Subjects: T Technology > TD Environmental technology. Sanitary engineering
Faculties/Schools: UK Campuses > Faculty of Engineering
Item ID: 43316
Depositing User: ABDUL HAMID, NOR
Date Deposited: 13 Jul 2017 04:41
Last Modified: 13 Oct 2017 00:17

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