Dye and nitro-compound pollution has become a significant issue worldwide. The adsorption and degradation of dyes and nitro-compounds have recently become important areas of study. Different methods, such as precipitation, flocculation, ultra-filtration, ion exchange, coagulation, and electro-catalytic degradation have been adopted for the adsorption and degradation of these organic pollutants. Apart from these methods, adsorption, photocatalytic degradation, and chemical degradation are considered the most economical and efficient to control water pollution from dyes and nitro-compounds. In this review, different kinds of dyes and nitro-compounds, and their adverse effects on aquatic organisms and human beings, were summarized in depth. This review article covers the comprehensive analysis of the adsorption of dyes over different materials (porous polymer, carbon-based materials, clay-based materials, layer double hydroxides, metal-organic frameworks, and biosorbents). The mechanism and kinetics of dye adsorption were the central parts of this study. The structures of all the materials mentioned above were discussed, along with their main functional groups responsible for dye adsorption. Removal and degradation methods, such as adsorption, photocatalytic degradation, and chemical degradation of dyes and nitro-compounds were also the main aim of this review article, as well as the materials used for such degradation. The mechanisms of photocatalytic and chemical degradation were also explained comprehensively. Different factors responsible for adsorption, photocatalytic degradation, and chemical degradation were also highlighted. Advantages and disadvantages, as well as economic cost, were also discussed briefly. This review will be beneficial for the reader as it covers all aspects of dye adsorption and the degradation of dyes and nitro-compounds. Future aspects and shortcomings were also part of this review article. There are several review articles on all these topics, but such a comprehensive study has not been performed so far in the literature.

The current article aims to present a numerical analysis of MHD Williamson nanofluid flow maintained to flow through porous medium bounded by a non-linearly stretching flat surface. The second law of thermodynamics was applied to analyze the fluid flow, heat and mass transport as well as the aspects of entropy generation using Buongiorno model. Thermophoresis and Brownian diffusion is considered which appears due to the concentration and random motion of nanoparticles in base fluid, respectively. Uniform magnetic effect is induced but the assumption of tiny magnetic Reynolds number results in zero magnetic induction. The governing equations (PDEs) are transformed into ordinary differential equations (ODEs) using appropriately adjusted transformations. The numerical method is used for solving the so-formulated highly nonlinear problem. The graphical presentation of results highlights that the heat flux receives enhancement for augmented Brownian diffusion. The Bejan number is found to be increasing with a larger Weissenberg number. The tabulated results for skin-friction, Nusselt number and Sherwood number are given. A decent agreement is noted in the results when compared with previously published literature on Williamson nanofluids.

In current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.