Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks.

Numerous harmful phenolic contaminants are discharged into water that pose a serious threat to environment where two of the most important purification methodologies for the mitigation of phenolic contaminants are adsorption and photocatalysis. Besides cost, each process has drawbacks in terms of productivity, environmental impact, sludge creation, and the development of harmful by-products. To overcome these limitations, the modeling and optimization of water treatment methods is required. Artificial Intelligence (AI) is employed for the interpretation of treatment-based processes due to powerful learning, simplicity, high estimation accuracy, effectiveness, and improvement of process efficiency where artificial neural networks (ANNs) are most frequently employed for predicting and analyzing the efficiency of processes applied for the mitigation of these phenolic contaminants from water. ANNs are superior to conventional linear regression models because the latter are incapable of dealing with non-linear systems. ANNs can also reduce the operational cost of treating phenol-contaminated water. A correlation coefficient of >0.99 can be achieved using ANN with enhanced phenol mitigation percentage accuracy generally ranging from 80 % to 99.99 %. Using ANN optimization, the maximum phenol mitigation efficiencies achieved were 99.99 % for phenol, 99.93 % for bisphenol A, 99.6 % for nonylphenol, 97.1 % for 2-nitrophenol, 96.6 % for 4-chlorophenol and 90 % for 2,6-dichlorophenol. In numerous ANN models, Levenberg-Marquardt backpropagation algorithm for training was employed using MATLAB software. This study overviews their employment and application for optimization and modeling of removal processes and explicitly discusses the important input and output parameters necessary for better performance of the system. The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. Furthermore, several challenges and future prospects have also been discussed.

Biocompatible cellulose acetate supported ammonium based ionic liquid membranes; way forward to remediate water pollution.

Dyes contaminated water has caused various environmental and health impacts in developing countries especially Pakistan due to different industrial activities. This issue has been addressed in present study by fabricating biocompatible ionic liquid (IL) membranes for the remediation of Crystal violet (CV) dye from contaminated water. Novel ammonium-based IL such as Triethyl dimethyl ammonium sulfate ([C3A][C2H6]SO4); (A2) was synthesized and further functionalized with hydroxyapatite (HAp; extracted from refused fish scales) resulting in the formation of HA2. Furthermore, A2 and HA2 were then used to fabricate the cellulose acetate (CA) based membranes with different volume ratios. The physicochemical properties of membranes-based composite materials were investigated using FTIR, XRD, and TGA and used for the adsorption of CV in the closed batch study. In results, CA-HA2 (1:2) showed higher efficiency of 98% for CV reduction, after the contact time of 90 min. Kinetic studies showed that the adsorption of CV followed the pseudo-second-order kinetic model for all adsorbents. The antibacterial properties of the synthesized membrane were investigated against gram-positive strain, S. aureus and CA-A2 (1:1) showed better antibacterial properties against S. aureus. The developed membrane is sustainable to be used for the adsorption of CV and against bacteria.

Chemical composition and antimicrobial activity of Boswellia serrata oleo-gum-resin essential oil extracted by superheated steam.

Oleo-gum-resin is a complex mixture of essential oils, polysaccharides, and resin acids. The objectives of the present study were to evaluate the variation in chemical components and antimicrobial activity of essential oils extracted by superheated steam at various temperatures. The optimum essential oil yield was obtained at the highest superheated steam temperature (210 °C). In total, twenty-one compounds were quantified by GC-MS with α-pinene as the major compound, followed by α-thujene, trans-verbenol, ß-thujone, p-cymene, m-cymene, and sabinene. Antimicrobial activity was performed by disc diffusion, resazurin microtitre-plate and micro-dilution broth susceptibility assays in which essential oil extracted at 150 °C and 180 °C revealed the highest antibacterial and antifungal activity, respectively. It is concluded that superheated steam is an effective method for the isolation of essential oil from oleo-gum-resin that improves the recovery of essential oil as well as antimicrobial activity.