Artificial neural network modelling of Cr(VI) surface adsorption with NiO nanoparticles using the results obtained from optimization of response surface methodology

Fabrication of Polyamide6/Polyaniline as an Effective Nano-web Membrane for Removal of Cr (VI) from Water and a Black Box Approach in Modeling of Adsorption Process

Chromium (Cr), as a highly toxic heavy metal ion, is still a severe environmental issue, although many research efforts have been put into its removal from water. Polyaniline (PANI), as a conductive polymer, demonstrated great capability in heavy metal adsorption due to its low cost, ease of synthesis, reversible redox behavior, and chemical stability. However, using PANI powder alone in heavy metal removal causes secondary pollution and aggregation in water. The PANI coating on a substrate could tackle this problem. In this study, polyaniline-coated polyamide6 (PA6/PANI) nano-web membrane was used for the removal of Cr(VI) in both adsorption and filtration-adsorption modes. The PA6/PANI nano-web membrane was fabricated via PA6 electrospinning followed by in-situ polymerization of the aniline monomer. The electrospinning condition of PA6 was optimized by the Taguchi method. The PA6/PANI nano-web membrane was characterized by FESEM, N2-adsorption/desorption, FT-IR, contact angle measurement, and tensile test. FT-IR and FESEM results demonstrated the successful synthesis of PA6/PANI nano-web and PANI homogeneous coating on PA6 nanofibers, respectively. The N2 adsorption/desorption results indicated that the pore volume of the PA6/PANI nano-web decreased by 39% compared to PA6 nanofibers. The tensile test and water contact angle studies showed that the coating of PANI on PA6 nanofibers improves the mechanical properties and hydrophilicity of PA6 by 10% and 25%, respectively. The application of PA6/PANI nano-web in the removal of Cr(VI) in batch and filtration modes exhibits excellent removal of 98.4 and 86.7%, respectively. A pseudo first order model well described the adsorption kinetics, and the adsorption isotherm was best fitted by the Langmuir model. A black box modeling approach based on artificial neural networks (ANN) was developed to predict the removal efficiency of the membrane. The superior performance of PA6/PANI in both adsorption and filtration-adsorption systems makes it a potential candidate for the removal of heavy metals from water on an industrial scale.

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.