Application of linear and non-linear methods for modeling removal efficiency of textile dyes from aqueous solutions using magnetic Fe3O4 impregnated onto walnut shell

Enhancing Methylene Blue Removal through Adsorption and Photocatalysis-A Study on the GO/ZnTiO3/TiO2 Composite

This study focuses on synthesizing and characterizing a graphene oxide/ZnTiO3/TiO2 (GO/ZTO/TO) composite to efficiently remove methylene blue (MB) from water, presenting a novel solution to address industrial dye pollution. GO and ZTO/TO were synthesized by the modified Hummers and sol-gel methods, respectively, while GO/ZTO/TO was prepared using a hydrothermal process. The structural and surface properties of the composite were characterized using various analytical techniques confirming the integration of the constituent materials and suitability for dye adsorption. The study revealed that GO/ZTO/TO exhibits an adsorption capacity of 78 mg g-1 for MB, with only a 15% reduction in adsorption efficiency until the fifth reuse cycle. Furthermore, the study suggests optimal adsorption near neutral pH and enhanced performance at elevated temperatures, indicating an endothermic reaction. The adsorption behavior fits the Langmuir isotherm, implying monolayer adsorption on homogeneous surfaces, and follows pseudo-second-order kinetics, highlighting chemical interactions at the surface as the rate-limiting step. The photocatalytic degradation of MB by GO/ZTO/TO follows pseudo-first-order kinetics, with a higher rate constant than that of GO alone, demonstrating the enhanced photocatalytic activity of the composite. In conclusion, GO/ZTO/TO emerges as a promising and sustainable approach for water purification, through an adsorption process and subsequent photocatalytic degradation.

Application of chemometrics tools for removal of crystal violet and methylene blue in binary solution by eco-friendly magnetic adsorbent modified on Heracleum persicum waste

Synthetic dyes can be hazardous to the ecosystem, even at low concentrations in the effluent. In this research, the Heracleum persicum stems-Fe3O4 (MHPS) adsorbent performance for the removal of crystal violet (CV) and methylene blue (MB) from binary aqueous solutions was investigated in a batch method under the influence of different parameters. In addition, predictive models for the adsorption process were developed using machine learning techniques such as artificial neural networks and random forests. ANN and RF models achieved high R² values of 0.9501 and 0.9797 for CV, 0.9471, and 09,834 for MB, respectively, and obtained low MSE values of 0.07107 and 0.03405 for CV, 0.09933, and 0.02908 for MB. The proposed adsorbent is cheap and eco-friendly and, on the other hand, is easily collected by the magnetic field. The adsorbent was characterized by applying FESEM-EDX, FESEM, BET, and FTIR. Various isotherm and kinetics models for the simultaneous adsorption of CV and MB were investigated in aqueous solutions. The adsorption isotherm and kinetics studies explain that the extended Langmuir model and pseudo-second-order models are best suited for CV and MB in the binary solution. The exothermic adsorption was achieved in the temperature range of 5-45 °C.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Application of random forest for modeling batch and continuous fixed-bed removal of crystal violet from aqueous solutions using Gypsophila aretioides stem-based biosorbent

In this work, the Gypsophila aretioides (GYP-A) stem is used as a biosorbent to remove crystal violet (CV) by the static and dynamic systems from aqueous solutions; the biosorbent is interesting in green chemistry and, on the other hand, cheaper than activated carbon and does not have the limitation of industrialization. The effects of different operating parameters such as pH(3-9), biosorbent dosage(0.4-1.8 mg/L), and initial concentration of CV(100-250 mg/L) and time for the batch method and the bed height, inlet CV concentration(75-250 mg/L), and flow rate(3-8) on the breakthrough curves for the continuous method is investigated. The result of CV adsorption onto GYP-A using the batch method indicates that the model fits Freundlich > Temkin > Langmuir > R-D, and R² equal 0.9953, 0.9847, 0.9161, 0.7909 were obtained for isotherm model, respectively. A pseudo-second-order model (R² = 0.9995-0.9997) is recommended to describe the adsorption kinetics. The Thomas and Yoon-Nelson models were analyzed to study the adsorption kinetics. The random forest model shows an excellent ability to predict the parameters involved in the CV adsorption process with appropriate accuracy and useable for large data, robust against noise; it can be very effective in selecting important variables.

All-cellulose functional membranes for water treatment: Adsorption of metal ions and catalytic decolorization of dyes

In this study, we present a facile, one-step method for the manufacturing of all-cellulose, layered membranes containing cellulose nanocrystals (CNC), TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyl radical)-mediated oxidized cellulose nanofibers (TO-CNF), or zwitterionic polymer grafted cellulose nanocrystals (CNC-g-PCysMA) as functional entities in combination with cellulose fibers and commercial grade microfibrillated cellulose. The presence of active sites such as hydroxyl, carbonyl, thioethers, and amines, gave the membranes high adsorption capacities for the metal ions Au (III), Co (II), and Fe (III), as well as the cationic organic dye methylene blue (MB). Furthermore, the membranes served as excellent metal-free catalysts for the decolorization of dyes via hydrogenation. A 3-fold increase of the hydrogenation efficiency for cationic dyes such as rhodamine B (RhB) and methylene blue was obtained in the presence of membranes compared to NaBH₄ alone. Water-based processing, the abundance of the component materials, and the multifunctional characteristics of the membranes ensure their potential as excellent candidates for water purification systems.

A new approach in adsorption modeling using random forest regression, Bayesian multiple linear regression, and multiple linear regression: 2,4-D adsorption by a green adsorbent

The mathematical model's usage in water quality prediction has received more interest recently. In this research, the potential of random forest regression (RFR), Bayesian multiple linear regression (BMLR), and multiple linear regression (MLR) were examined to predict the amount of 2,4-dichlorophenoxy acetic acid (2,4-D) elimination by rice husk biochar from synthetic wastewater, using five input operating parameters including initial 2,4-D concentration, adsorbent dosage, pH, reaction time, and temperature. The equilibrium and kinetic adsorption data were fitted best to the Freundlich and pseudo-first-order models. The thermodynamic parameters also indicated the exothermic and spontaneous nature of adsorption. The modeling results indicated an R² of 0.994, 0.992, and 0.945 and RMSE of 1.92, 6.17, and 2.10 for the relationship between the model-estimated and measured values of 2,4-D removal for RFR, BMLR, and MLR, respectively. Overall performances indicated more proficiency of RFR than the BMLR and MLR models due to its capability in capturing the non-linear relationships between input data and their associated removal capacities. The sensitivity analysis demonstrated that the 2,4-D adsorption process is more sensitive to initial 2,4-D concentration and adsorbent dosage. Thus, it is possible to permanently monitor waters more cost-effectively with the suggested model application.

Performance Evaluation and Optimization of Dyes Removal using Rice Bran-Based Magnetic Composite Adsorbent

Although several studies have explored green adsorbent synthesized from many types of agriculture waste, this study represents the first attempt to prepare an environmentally friendly rice bran/SnO₂/Fe₃O₄-based absorbent with economic viability and material reusability, for the promotion of sustainable development. Here, rice bran/SnO₂/Fe₃O₄ composites were successfully synthesized and applied for adsorption of reactive blue 4 (RB4) and crystal violet (CV) dyes in aqueous solutions. The adsorption data were well-fitted by the Langmuir isotherm model and the pseudo-second-order kinetic model. The maximum adsorption capacities of the RB4 and CV dyes as indicated by the Langmuir isotherm model were 218.82 and 159.24 mg/g, respectively. As results of response surface methodology (RSM) showed, the quadratic model was appropriate to predict the performance of RB4 dye removal. The findings exhibited that an optimum removal rate of 98% was achieved at 60 °C for pH 2.93 and adsorption time of 360 min. Comparative evaluation of different agricultural wastes indicated that the rice bran/SnO₂/Fe₃O₄ composite appeared to be a highly promising material in terms of regeneration and reusability, and showed that the composite is a potential adsorbent for dye removal from aqueous solutions. Overall, the study results clearly suggest that an adsorbent synthesized from rice bran/SnO₂/Fe₃O₄ magnetic particle composites provides encouraging adsorption capacity for practical applications for environmental prevention.

Modeling of azo dyes adsorption on magnetic NiFe2O4/RGO nanocomposite using response surface methodology

BACKGROUND

Azo group dyes are the largest group of synthetics dyes that widely used in industries, especially in textile industry. The presence of these organic compounds in wastewaters and their discharge into environment without efficient treatment may cause adverse effect on human, living and aquatic environment. The purpose of this study was to optimize the adsorption of azo dye of Direct Red 81 (anionic dye) and Basic Blue 41 (cationic dye) from aqueous solution onto magnetic NiFe₂O₄/RGO nanocomposite.

METHODS

In this study the response surface methodology (RSM) based on the central composite design (CCD), was used to optimization and modeling of adsorption process DR81 and BB41 dye on NiFe₂O₄/RGO. in order to investigating the effect of the operating parameters on the adsorption efficiency DR81 and BB41, four influential factors were chosen that includes of pH (3-9), contact time (5-25 min), adsorbent amount (0.02-0.05 g) and initial dye concentration (40-200 mg/L). A total of 30 experiments were performed for each dye in this study. The concentration of dye in solution was measured by spectrophotometer. The structure of synthesized adsorbent was investigated using Scanning Electron Microscope (SEM), X-ray diffraction (XRD), Fourier transform irradiation (FTIR), transmission electron microscope (TEM) and vibrating sample magnetometer (VSM).

RESULTS

Analysis of variance (ANOVA) showed that regression model for both dye adsorption with value of P value <0.001 is significant statistically. The correlation coefficient (R²) for DR81 (R² = 0.9968) and BB41 (R² = 0.9948) indicated which there is a good agreement between predicted values and the results of the experiments and the model also well predict the adsorption efficiency. Furthermore, the factors of pH, dye concentration and adsorbent dose, have the greatest effect on adsorption, respectively, while contact time have the lowest effect on adsorption of both dyes. The adsorption behavior of the DR81 and BB41 onto NiFe₂O₄/RGO was best described by the Langmuir and Freundlich isotherm, respectively. The optimum conditions for maximum removal of DR81 (96.41%) was found to be at pH 3, contact time 19.68 min, adsorbent dose 0.02 g and initial dye concentration 40 mg/L. However, the optimum conditions for maximum removal of BB41 (97.87%) was found to be at pH 9 contact time 18.16 min, adsorbent dose 0.02 g and initial dye concentration 40 mg/L.

CONCLUSION

The present study shows that magnetic NiFe₂O₄/RGO nanocomposite have much potential as a powerful adsorbent for the rapid adsorption of anionic (DR81) and cationic dyes (BB41) from aqueous solution.

Selective adsorption of phenanthrene dissolved in Tween 80 solution using activated carbon derived from walnut shells

In order to remove phenanthrene (PHE) from surfactant solution, activated carbon (AC) was prepared from waste walnut shells and characterized by Brunauer-Emmett-Teller (BET), field-emission scanning electron microscopy (FESEM), Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS). For solutions containing PHE and Tween 80, the former was effectively removed and the latter could be economically recovered after adsorption by the prepared AC. The π-π interactions and oxygen containing functional groups of AC play important roles in the PHE adsorption process. The adsorption kinetics process could best be described using the pseudo-second-order model and adsorption isotherm results indicated that the Langmuir model best fitted the data. Adsorption thermodynamic parameters, including enthalpy change, Gibbs free energy change and entropy change were calculated. Under optimal conditions, PHE removal and Tween 80 recovery reached 95% and 90%, respectively. The results suggest that AC provided an efficient alternative for selective adsorption of PHE and recovery of Tween 80 after the soil washing processes. After adsorption AC could be regenerated with ethanol and even if AC were regenerated twice PHE removal reached 80%.

No time to waste organic waste: Nanosizing converts remains of food processing into refined materials

Modern food processing results in considerable amounts of side-products, such as grape seeds, walnut shells, spent coffee grounds, and harvested tomato plants. These materials are still rich in valuable and biologically active substances and therefore of interest from the perspective of waste management and "up-cycling". In contrast to traditional, often time consuming and low-value uses, such as vermicomposting and anaerobic digestion, the complete conversion into nanosuspensions unlocks considerable potentials of and new applications for such already spent organic materials without the need of extraction and without producing any additional waste. In this study, nanosuspensions were produced using a sequence of milling and homogenization methods, including High Speed Stirring (HSS) and High Pressure Homogenization (HPH) which reduced the size of the particles to 200-400 nm. The resulting nanosuspensions demonstrated nematicidal and antimicrobial activity and their antioxidant activities exceeded the ones of the bulk materials. In the future, this simple nanosizing approach may fulfil several important objectives, such as reducing and turning readily available waste into new value and eventually closing a crucial cycle of agricultural products returning to their fields - with a resounding ecological impact in the fields of medicine, agriculture, cosmetics and fermentation. Moreover, up-cycling via nanosizing adds an economical promise of increased value to residue-free waste management.

Enhanced sonocatalytic degradation of organic dyes from aqueous solutions by novel synthesis of mesoporous Fe3O4-graphene/ZnO@SiO2 nanocomposites

Fe₃O₄-graphene/ZnO@mesoporous-SiO₂ (MGZ@SiO₂) nanocomposites was synthesized via a simple one pot hydrothermal method. The as-obtained samples were investigated using various techniques, as follows: scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and specific surface area (BET) vibrating sample magnetometer (VSM), among others. The sonocatalytic activities of the catalysts were tested according to the oxidation for the removal of methylene blue (MB), methyl orange (MO), and rhodamine B (RhB) under ultrasonic irradiation. The optimal conditions including the irradiation time, pH, dye concentration, catalyst dosage, and ultrasonic intensity are 60min, 11, 50mg/L, 1.00g/L, and 40W/m², respectively. The MGZ@SiO₂ showed the higher enhanced sonocatalytic degradation from among the three dyes; furthermore, the sonocatalytic-degradation mechanism is discussed. This study shows that the MGZ@SiO₂ can be applied asa novel-design catalyst for the removal of organic pollutants from aqueous solutions.

Removal of Colored Organic Pollutants from Wastewaters by Magnetite/Carbon Nanocomposites: Single and Binary Systems

This work develops a methodology for selective removal of industrial dyes from wastewaters using adsorption technology based on magnetic adsorbents. The magnetic nanoparticles embedded within a matrix of activated carbon were tested as adsorbents for removal of industrial dyes from aqueous solutions. The effects of four independent variables, solution pH, initial concentration of pollutant, adsorbent dose, contact time, and their interactions on the adsorption capacity of the nanocomposite were investigated in order to optimize the process. The removal efficiency of pollutants depends on solution pH and increases with increasing the carbon content, with initial concentration of the pollutants, the temperature, and the dose of magnetite/carbon nanocomposites. Pseudo-second-order kinetic model was fitted to the kinetic data, and adsorption isotherm analysis and thermodynamics were used to elucidate the adsorption mechanism. The maximum adsorption capacities were 223.82 mg g−1 for Nylosan Blue, 114.68 mg g−1 for Chromazurol S, and 286.91 mg g−1 for Basic Red 2. The regeneration and reuse of the sorbent were evaluated in seven adsorption/desorption cycles. The optimum conditions obtained for individual adsorption were selected as starting conditions for simultaneous adsorption of dyes. In binary systems, in normal conditions, selectivity decreases in the order: Red Basic 2 > Nylosan Blue > Chromazurol S.

Photocatalytic degradation of methyl orange, methylene blue and rhodamine B with AgCl nanocatalyst synthesised from its bulk material in the ionic liquid [P6 6 6 14]Cl

The photocatalytic degradation of wastewater containing three industrial dyes belonging to different families, methyl orange (MO), methylene blue (MB) and Rhodamine B (RhB), was studied under UV-Vis irradiation using synthesised silver chloride nanoparticles. The nanocatalyst was prepared by a dissolution/reprecipitation method starting from the bulk powder and the ionic liquid trihexyl(tetradecyl)phosphonium chloride, [P_{6 6 6 14}]Cl, without addition of other solvents. The obtained catalyst was characterised by UV-Vis absorbance, X-ray powder diffraction, transmission electron microscopy and scanning electron microscopy. The decolourisation of the samples was studied by UV-Vis absorbance at the corresponding wavelength. Starting from 10 ppm dye solutions and 1 g L^-1 of the synthesised AgCl nanoparticles, degradation efficiencies of 98.4% for MO, 98.6% for MB and 99.9% for RhB, were achieved in 1 h. The degradation mechanisms for the different dyes were studied. Comparison with other frequently used nanocatalysts, namely P-25 Degussa, TiO₂ anatase, Ag and ZnO, highlights the strong catalytic activity of AgCl nanoparticles. Under the same experimental conditions, these nanoparticles led to higher (more than 10%) and faster degradations.