Modeling the Removal of Phenol Dyes Using a Photocatalytic Reactor with SnO2/Fe3O4Nanoparticles by Intelligent System

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.

Machine learning analysis of PM1 impact on visibility with comprehensive sensitivity evaluation of concentration, composition, and meteorological factors

This study introduces a novel approach to visibility modelling, focusing on PM1 concentration, its chemical composition, and meteorological conditions in two distinct Polish cities, Zabrze and Warsaw. The analysis incorporates PM1 concentration measurements as well as its chemical composition and meteorological parameters, including visibility data collected during summer and winter measurement campaigns (120 samples in each city). The developed calculation procedure encompasses several key steps: formulating a visibility prediction model through machine learning, identifying data in clusters using unsupervised learning methods, and conducting global sensitivity analysis for each cluster. The multi-layer perceptron methods developed demonstrate high accuracy in predicting visibility, with R values of 0.90 for Warsaw and an RMSE of 1.52 km for Zabrze. Key findings reveal that air temperature and relative humidity significantly impact visibility, alongside PM1 concentration and specific heavy metals such as Rb, Vi, and Cd in Warsaw and Cr, Vi, and Mo in Zabrze. Cluster analysis underscores the localized and complex nature of visibility determinants, highlighting the substantial but previously underappreciated role of heavy metals. Integrating the k-means clustering and GSA methods emerges as a powerful tool for unravelling complex mechanisms of chemical compound changes in particulate matter and air, significantly influencing visibility development.

Modeling the removal of methylene blue dye using a graphene oxide/TiO2/SiO2 nanocomposite under sunlight irradiation by intelligent system

In this study, a model to improve the degradability of methylene blue (MB) dye using graphene oxide/TiO2/SiO2 nanocomposite under sunlight irradiation is investigated. The effect of operative parameters comprising catalyst concentration, initial dye concentration, and pH on the photocatalytic batch during removal of MB is studied. Fractional factorial design (FFD) and response surface methodology (RSM) are used to design the experiment layout. Graphene oxide (GO)/TiO2/SiO2 nanoparticles are synthesized through sonication and sol–gel methodologies. In the experiments, three levels of catalyst varied in the percentage of TiO2 pointed as (I) TiO2:GO (100%), (II) TiO2:GO:SiO2 (50%), and (III) TiO2:GO:SiO2 (25%) are used. The irradiation interval was 7 h at solar radiation energy 6.35–5.00 kW h/m2/day. In the experiments, three levels of catalyst varied in the percentage of TiO2 pointed as (I) TiO2:GO (100%), (II) TiO2:GO:SiO2 (50%), and (III) TiO2:GO:SiO2 (25%) are used. The synthesized catalysts are characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscope, and X-ray photoelectron spectroscopy. ANOVA under 23 FFD is conducted to evaluate the effect of independent factors depending on the value of F as pH of solution, weight of catalyst, and concentration of MB. The adsorption kinetics, experimental design with FFD, and RSM are investigated in this study. The Surface Adsorption kinetics were statistically analyzed, the model that best described the results of each experiment was determined out of the two evaluated kinetics (pseudo-first order, pseudo-second order), for the three photocatalyst composites I, II, and III with the parameters; weight of the catalyst, pH, and initial MB concentration, also percentage degradation is evaluated. RSM results are consistent with the kinetic model; first, the pH is considered as the most significant parameter affecting the removal of the organic pollutant, and second, catalyst II gives the highest percentage removal efficiency of MB. FFD results are consistent with both models where the effect of the independent factor depending on the value of F was pH of solution > weight of catalyst > initial concentration of MB. The percentage removal was in the range from 30 to 99%.

Optimization of process parameters in plastic injection molding for minimizing the volumetric shrinkage and warpage using radial basis function (RBF) coupled with the k-fold cross validation technique

In this study, a multi-objective design optimization method based on a radial basis function (RBF) model was applied to minimize the volumetric shrinkage and warpage of hip liners as an injection-molded biomedical part. The hip liners included an ultrahigh molecular weight polyethylene (UHMWPE) liner and UHMWPE reinforced with a nano-hydroxyapatite (nHA) liner. The shrinkage and warpage values of the hip liners were generated by simulation of the injection molding process using Autodesk Moldflow. The RBF model was used to build an approximate function relationship between the objectives and the process parameters. The process parameters, including mold temperature, melt temperature, injection time, packing time, packing pressure, coolant temperature, and type of liner, were surveyed to find the interaction effects of them on the shrinkage and warpage of the liners. The results indicated that the addition of nHA helps the liners to obtain more dimensional stability. The model was validated by the k-fold cross validation technique. Finally, the model revealed the optimal process conditions to achieve the minimized shrinkage and warpage simultaneously for various weights.