RESUMEN
Wastewater pollution caused by organic dyes is a growing concern due to its negative impact on human health and aquatic life. To tackle this issue, the use of advanced wastewater treatment with nano photocatalysts has emerged as a promising solution. However, experimental procedures for identifying the optimal conditions for dye degradation could be time-consuming and expensive. To overcome this, machine learning methods have been employed to predict the degradation of organic dyes in a more efficient manner by recognizing patterns in the process and addressing its feasibility. The objective of this study is to develop a machine learning model to predict the degradation of organic dyes and identify the main variables affecting the photocatalytic degradation capacity and removal of organic dyes from wastewater. Nine machine learning algorithms were tested including multiple linear regression, polynomial regression, decision trees, random forest, adaptive boosting, extreme gradient boosting, k-nearest neighbors, support vector machine, and artificial neural network. The study found that the XGBoosting algorithm outperformed the other models, making it ideal for predicting the photocatalytic degradation capacity of BiVO4. The results suggest that XGBoost is a suitable model for predicting the photocatalytic degradation of wastewater using BiVO4 with different dopants.
Asunto(s)
Nanopartículas , Aguas Residuales , Humanos , Algoritmos , Colorantes , Aprendizaje AutomáticoRESUMEN
A facile method was approached to synthesize a new series of highly porous polyethylene glycol (PEG) and Ag-ZnO (AZO) grafted polyaniline (PANI) nanocomposites (PAPE/AZO) for investigation of the adsorption behavior of brilliant green dye (BG). The compositions of Ag (1, 3 and 5%) in AZO were varied to assess the influence of bimetallic oxide incorporation. The nanocomposites were characterized by FTIR, XRD, SEM and TEM analyses. The adsorption performance of the PAPE/AZO was assessed by various parametric changes viz. contact time, pH, temperature, concentration of the dye and adsorbent dosage. The highest adsorption capacity of 94.46 mg g-1 was achieved at the optimum conditions of 0.075 g adsorbent dosage, 70 mg L-1 dye concentration, 1% AZO and pH 2. BET and BJH analyses of the nanocomposite confirmed the higher surface area and pore volume with lower amount of AZO that supported PAPE for enhanced dye removal. The Langmuir isotherm model fits the equilibrium conditions indicating a homogeneous distribution of active sites on the surface of the nanocomposite. Adsorption kinetic model ensued pseudo second order exhibiting chemisorption process. From the results, it is obvious that PAPE/AZO can be used as a potential core substance for treating real-time industrial dye effluents.