Artificial neural network implementation for dissolved organic carbon quantification using fluorescence intensity as a predictor in wastewater treatment plants.

Although spectroscopic methods provide a fast and cost-effective means of monitoring dissolved organic carbon (DOC) in natural and engineered water systems, the prediction accuracy of these methods is limited by the complex relationship between optical properties and DOC concentration. In this study, we developed DOC prediction models using multiple linear/log-linear regression and feedforward artificial neural network (ANN) and investigated the effectiveness of spectroscopic properties, such as fluorescence intensity and UV absorption at 254 nm (UV254), as predictors. Optimum predictors were identified based on correlation analysis to construct models using single and multiple predictors. We compared the peak-picking and parallel factor analysis (PARAFAC) methods for selecting appropriate fluorescence wavelengths. Both methods had similar prediction capability (p-values >0.05), suggesting PARAFAC was not necessary for choosing fluorescence predictors. Fluorescence peak T was identified as a more accurate predictor than UV254. Combining UV254 and multiple fluorescence peak intensities as predictors further improved the prediction capability of the models. The ANN models outperformed the linear/log-linear regression models with multiple predictors, achieving higher prediction accuracy (peak-picking: R2 = 0.8978, RMSE = 0.3105 mg/L; PARAFAC: R2 = 0.9079, RMSE = 0.2989 mg/L). These findings suggest the potential to develop a real-time DOC concentration sensor based on optical properties using an ANN for signal processing.

Effects of Benzalkonium Chloride Contents on Structures, Properties, and Ultrafiltration Performances of Chitosan-Based Nanocomposite Membranes.

The effects of benzalkonium chloride (BKC) contents on the structure, properties, and ultrafiltration performance of chitosan-based nanocomposite membranes containing poly(ethylene glycol) and multi-walled carbon nanotube (chitosan/BKC/PEG/CNT) were examined. The membranes were prepared by a mixing solution method and phase inversion before being characterized with microscopic techniques, tensile tests, thermogravimetric analysis, water contact angle, and porosity measurements. The performance of the nanocomposite membranes in regard to permeability (flux) and permselectivity (rejection) was examined. The results show that the incorporation of BKC produced nanocomposite membranes with smaller pore structures and improved physico-chemical properties, such as an increase in porosity and surface roughness (Ra = 45.15 to 145.35 nm and Rq = 53.69 to 167.44 nm), an enhancement in the elongation at break from 45 to 109%, and an enhancement in the mechanical strength from 31.2 to 45.8 MPa. In contrast, a decrease in the membrane hydrophilicity (water contact angle increased from 56.3 to 82.8°) and a decrease in the average substructure pore size from 32.64 to 10.08 nm were observed. The membrane rejection performances toward Bovine Serum Albumin (BSA) increased with the BKC composition in both dead-end and cross-flow filtration processes. The chitosan/BKC/PEG/CNT nanocomposite membranes have great potential in wastewater treatments for minimizing biofouling without reducing the water purification performance.

Toughened chitosan-based composite membranes with antibiofouling and antibacterial properties via incorporation of benzalkonium chloride.

Biofouling due to biofilm formation is a major problem in ultrafiltration membrane applications. In this work, a potential approach to solve this issue has been developed by functionalization of chitosan-based membranes with benzalkonium chloride (BKC). The chitosan composite membranes consisting of poly(ethylene glycol) (PEG), multiwalled carbon nanotubes (MWCNT), and BKC were synthesized by mixing the membrane precursors and the antibacterial solution, and casting via an inversed phase technique. The effects of the BKC content on the morphology and performance of the membranes are investigated by varying the BKC feed compositions. The composite membranes demonstrate better antibacterial efficacy against Staphylococcus aureus than Escherichia coli. The permeability and selectivity performances of the composites as filter membranes are examined by employing a dead-end filtration system. Interestingly, enhanced toughness of the membranes is observed as a function of the BKC content. Mechanisms of the structural formation are investigated. The results from SEM, XRD, and FTIR spectroscopy revealed that MWCNT/BKC are located as nanoclusters with π-π stacking interactions, and are covered by PEG chains. The shape of the dispersed domains is spherical at low BKC contents, but becomes elongated at high BKC contents. These act as soft domains with an anisotropic shape with toughening of the brittle chitosan matrix, leading to enhanced durability of the membranes, especially in ultrafiltration applications. The composite membranes also demonstrate improved rejection in dead-end ultrafiltration systems due to high porosity, high hydrophilicity, and the positive charges of the membrane surface.