Prediction of methyl orange removal by iron decorated activated carbon using an artificial neural network

Sustainable removal of caffeine and acetaminophen from water using biomass waste-derived activated carbon: Synthesis, characterization, and modelling

The removal of caffeine (CFN) and acetaminophen (ACT) from water using low-cost activated carbons prepared from artichoke leaves (AAC) and pomegranate peels (PAC) was reported in this paper. These activated carbons were characterized using various analytical techniques. The results showed that AAC and PAC had surface areas of 1203 and 1095 m2 g-1, respectively. The prepared adsorbents were tested for the adsorption of these pharmaceuticals in single and binary solutions. These experiments were performed under different operating conditions to evaluate the adsorption properties of these adsorbents to remove CFN and ACT. AAC and PAC showed maximum adsorption capacities of 290.86 and 258.98 mg g-1 for CFN removal, 281.18 and 154.99 mg g-1 for the ACT removal over a wide pH range. The experimental equilibrium adsorption data fitted to the Langmuir model and the kinetic data were correlated with the pseudo-second order model. AAC showed the best adsorption capacities for the removal of these pharmaceuticals in single systems and, consequently, it was tested for the simultaneous removal of these pollutants in binary solutions. The simultaneous adsorption of these compounds on AAC was improved using the central composite design and response surface methodology. The results indicated an antagonistic effect of CFN on the ACT adsorption. AAC regeneration was also analyzed and discussed. A statistical physics model was applied to describe the adsorption orientation of the tested pollutants on both activated carbon samples. It was concluded that AAC is a promising adsorbent for the removal of emerging pollutants due to its low cost and reusability properties.

Degradation of methyl orange using hydrodynamic Cavitation, H2O2, and photo-catalysis with TiO2-Coated glass Fibers: Key operating parameters and synergistic effects

Advanced oxidation processes (AOPs) are eco-friendly, and promising technology for treating dye containing wastewater. This study focuses on investigating the removal of methyl orange (MO), an azo dye, from a synthetic wastewater through the use of hydrodynamic cavitation (HC), both independently and in combination with hydrogen peroxide (H2O2), as an external oxidant, as well as photocatalysis (PC) employing catalyst coated on glass fibers tissue (GFT). The examination of various operating parameters, including the pressure drop and the concentration of H2O2, was systematically conducted to optimize the degradation of MO. A per-pass degradation modelwas used to interpret and describe the experimental data. The data revealed that exclusive employment of HC using a vortex-based cavitation device at 1.5 bar pressure drop, resulted in a degradation exceeding 96 % after 100 passes, equivalent to 230 min of treatment (cavitation yield of 3.6 mg/kJ for HC), with a COD mineralization surpassing 12 %. The presence of a small amount of H2O2 (0.01 %) significantly reduced the degradation time from 230 min to 36 min (16 passes), achieving a degradation of 99.8 % (cavitation yield of 6.77 mg/kJ for HC) with COD mineralization rate twice as much as HC alone, indicating a synergistic effect of 4.8. The degradation time was further reduced to 21 min by combining HC with PC using TiO2-coated glass fibers and H2O2, (cavitation yield of 11.83 mg/kJ for HC), resulting in an impressive synergistic effect of 9.2 and COD mineralization twice as high as the HC/H2O2 system. The results demonstrate that HC based hybrid AOPs can be very effective for treating and mineralizing azo dyes in water.

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.

Prediction of methyl orange dye (MO) adsorption using activated carbon with an artificial neural network optimization modeling

In this study, methyl orange (MO) dye removal by adsorption utilizing activated carbon made from date seeds (DPAC) was modeled using an artificial neural network (ANN) technique. Instrumental investigations such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET) analysis were used to assess the physicochemical parameters of adsorbent. By changing operational parameters including adsorbent dosage (0.01-0.03 g), solution pH 3-8, initial dye concentration (5-20 mg/L), and contact time (2-60 min), the viability of date seeds for the adsorptive removal of methyl orange dye from aqueous solution was assessed in a batch procedure. The system followed the pseudo 2nd order kinetic model for DPAC adsorbent, according to the kinetic study (R2 = 0.9973). The mean square error (MSE), relative root mean square error (RRMSE), root mean square error (RMSE), mean absolute percentage error (MAPE), relative error (RE), and correlation coefficient (R2) were used to measure the ANN model performance. The maximum RE was 8.24% for the ANN model. Two isotherm models, Langmuir and Freundlich, were studied to fit the equilibrium data. Compared with the Freundlich isotherm model (R2 = 0.72), the Langmuir model functioned better as an adsorption isotherm with R2 of 0.9902. Thus, this study demonstrates that the dye removal process can be predicted using an ANN technique, and it also suggests that adsorption onto DPAC may be employed as a main treatment for dye removal from wastewater.

Low-cost treated lignocellulosic biomass waste supported with FeCl3/Zn(NO3)2 for water decolorization

Dye pollution has always been a serious concern globally, threatening the lives of humans and the ecosystem. In the current study, treated lignocellulosic biomass waste supported with FeCl₃/Zn(NO₃)₂ was utilized as an effective composite for removing Reactive Orange 16 (RO16). SEM/EDAX, FTIR, and XRD analyses exhibited that the prepared material was successfully synthesized. The removal efficiency of 99.1% was found at an equilibrium time of 110 min and dye concentration of 5 mg L^-1 Adsorbent mass of 30 mg resulted in the maximum dye elimination, and the efficiency of the process decreased by increasing the temperature from 25 to 40 °C. The effect of pH revealed that optimum pH was occurred at acidic media, having the maximum dye removal of greater than 90%. The kinetic and isotherm models revealed that RO16 elimination followed pseudo-second-order (R² = 0.9982) and Freundlich (R² = 0.9758) assumptions. Surprisingly, the performance of modified sawdust was 15.5 times better than the raw sawdust for the dye removal. In conclusion, lignocellulosic sawdust-Fe/Zn composite is promising for dye removal.