G.-M. Zeng

Use of iron oxide nanomaterials in wastewater treatment: A review

Nowadays there is a continuously increasing worldwide concern for the development of wastewater treatment technologies. The utilization of iron oxide nanomaterials has received much attention due to their unique properties, such as extremely small size, high surface-area-to-volume ratio, surface modifiability, excellent magnetic properties and great biocompatibility. A range of environmental clean-up technologies have been proposed in wastewater treatment which applied iron oxide nanomaterials as nanosorbents and photocatalysts. Moreover, iron oxide based immobilization technology for enhanced removal efficiency tends to be an innovative research point. This review outlined the latest applications of iron oxide nanomaterials in wastewater treatment, and gaps which limited their large-scale field applications. The outlook for potential applications and further challenges, as well as the likely fate of nanomaterials discharged to the environment were discussed.

Novel magnetic chitosan/poly(vinyl alcohol) hydrogel beads: preparation, characterization and application for adsorption of dye from aqueous solution.

Novel magnetic chitosan/poly(vinyl alcohol) hydrogel beads (m-CS/PVA HBs) were prepared by an instantaneous gelation method and characterized by X-ray diffraction (XRD), vibrating sample magnetometry (VSM) and thermogravimetric analysis (TGA). Results of characterization indicated that m-CS/PVA HBs have been prepared successfully without damaging the crystal structure of Fe(3)O(4) and their saturation magnetization were about 21.96 emu g(-1). The adsorption capacity of Congo Red on the m-CS/PVA HBs was 470.1 mg g(-1). The adsorption was well described by pseudo-second-order kinetics and Langmuir equation. Positive value of enthalpy change (ΔH(∘)) (13.32 kJ mol(-1)) showed that the adsorption was endothermic and physical in nature. The values of Gibbs free energy change (ΔG(∘)) were found to be -3.321 kJ mol(-1) at 298 K for m-CS/PVA HBs, indicating the spontaneity of Congo Red adsorption. Therefore, the m-CS/PVA HBs could be employed as a low-cost alternative to other adsorbents in the removal of dyes from aqueous solution.

Modeling of a permeate flux of cross-flow membrane filtration of colloidal suspensions: A wavelet network approach

Although traditional artificial neural networks have been an attractive topic in modeling membrane filtration, lower efficiency by trial-and-error constructing and random initializing methods often accompanies neural networks. To improve traditional neural networks, the present research used the wavelet network, a special feedforward neural network with a single hidden layer supported by the wavelet theory. Prediction performance and efficiency of the proposed network were examined with a published experimental dataset of cross-flow membrane filtration. The dataset was divided into two parts: 62 samples for training data and 329 samples for testing data. Various combinations of transmembrane pressure, filtration time, ionic strength and zeta potential were used as inputs of the wavelet network so as to predict the permeate flux. Through the orthogonal least square alogorithm, an initial network with 12 hidden neurons was obtained which offered a normalized square root of mean square of 0.103 for the training data. The initial network led to a wavelet network model after training procedures with fast convergence within 30 epochs. Futher the wavelet network model accurately depicted the positive effects of either transmembrane pressure or zeta potential on permeate flux. The wavelet network also offered accurate predictions for the testing data, 96.4 % of which deviated the measured data within the ± 10 % relative error range. Moreover, comparisons indicated the wavelet network model produced better predictability than the back-forward backpropagation neural network and the multiple regression models. Thus the wavelet network approach could be employed successfully in modeling dynamic permeate flux in cross-flow membrane filtration.

BAYESIAN APPROACH TO QUANTIFY PARAMETER UNCERTAINTY AND IMPACTS ON PREDICTIVE FLOW AND MASS TRANSPORT IN HETEROGENEOUS AQUIFER

Groundwater flow and mass transport predictions are subjected to uncertainty due to heterogeneity of hydraulic conductivity, whose variability in space is considerably higher than that of other hydraulic properties relevant to groundwater flow. To characterize the distribution of hydraulic conductivity, random space function (RSF) is often used. The Bayesian approach was applied to quantitatively study the effect of parameter uncertainty in RSF on a hypothetical two-dimensional uniform groundwater flow and mass transport. Specifically, the parameter uncertainty transmitted to macrodispersion in mass transport model was also inferred. The results showed that the posterior probability distributions of parameters were updated after Bayesian inference. The numerical experiments indicated that the overall predictive uncertainty was increased with simulating time along the flow direction. As to the relative contribution of the two types of uncertainty, it indicated that parametric uncertainty was a little more important than stochastic uncertainty for the predictive uncertainty of hydraulic head. When the uncertainty of hydraulic head as well as macrodispersion was transported to mass transport model, a much bigger contribution of stochastic uncertainty was observed. Therefore, parametric uncertainty should not be neglected during the process of subsurface simulation.