Removal of dye using peroxidase-immobilized Buckypaper/polyvinyl alcohol membrane in a multi-stage filtration column via RSM and ANFIS

Investigating the adsorption potential of char derived from waste latex for methylene blue removal

This study presents the adsorption of methylene blue (MB) dye using latex char derived from pyrolysis of latex gloves. The adsorption process was investigated systematically using Response Surface Methodology (RSM) with a Central Composite Design (CCD). The effects of four key variables, namely pH, time, temperature, and adsorbent dosage, were studied using a factorial design enriched with center points and axial points. Experimental data were analyzed using a second-order polynomial regression model to construct a response surface model, which elucidated the relationship between the variables and MB removal efficiency. The study found that the char obtained at 800 °C exhibited the highest adsorption capacity due to its increased carbonization, expanded surface area, and diverse pore structure. Analysis of Variance (ANOVA) confirmed the significance of the quadratic model, with remarkable agreement between predicted and experimental outcomes. Diagnostic plots validated the model's reliability, while 3D contour graphs illustrated the combined effects of variables on MB removal efficiency. Optimization using DoE software identified optimal conditions resulting in a 99% removal efficiency, which closely matched experimental results. Additionally, adsorption isotherms revealed that the Freundlich model best described the adsorption behavior, indicating heterogeneous surface adsorption with multilayer adsorption. This comprehensive study provides valuable insights into the adsorption process of MB dye using latex char, with implications for wastewater treatment and environmental remediation.

Applications and implications of carbon nanotubes for the sequestration of organic and inorganic pollutants from wastewater

The rapid growth in the population, industrial developments, and climate change over the century have contributed to a significant rise in aquatic pollution leading to a scarcity of clean, reliable, and sustainable water sources and supply. Exposure through ingestion, inhalation, and dermal absorption of organic/inorganic compounds such as heavy metals, pharmaceuticals, dyes, and persistent organic pollutants (POPs) discharged from municipalities, hospitals, textile industries, food, and agricultural sectors has caused adverse health outcomes in aquatic and terrestrial organisms. Owing to the high surface area, photocatalytic activity, antimicrobial, antifouling, optical, electronic, and magnetic properties, the application of nanotechnology offers unique opportunities in advanced wastewater management strategies over traditional approaches. Carbon nanomaterials and associated composites such as single-walled carbon nanotubes (SWCNT), multiwalled carbon nanotubes (MWCNT), and carbon nanotubes (CNT) buckypaper membranes have demonstrated efficiency in adsorption, photocatalytic activity, and filtration of contaminants and thus show immense potentiality in wastewater management. This review focuses on the application of CNTs in the sequestration of organic and inorganic contaminants from the aquatic environment. It also sheds light on the aquatic pollutant desorption processes, current safety regulations, and toxic responses associated with CNTs. Critical knowledge gaps involving CNT synthesis, surface modification processes, CNT-environment interactions, and risk assessments are further identified and discussed.

Treating reverse osmosis concentrate to address scaling and fouling problems in zero-liquid discharge systems: A scientometric review of global trends

Currently, reverse osmosis concentrate (ROC) treatment is one of the most promising techniques for its disposal because it produces freshwater with high recovery and valuable materials such as salts and reduces waste volume and environmental pollution. Public attention to the severe consequences of water pollution and strict environmental regulations on wastewater discharge has pushed water-polluting industries toward zero-liquid discharge (ZLD). However, scaling and fouling problems increase energy consumption and limit permeate flux at high salt concentrations, mainly due to calcium, magnesium, and silica precipitation, ultimately decreasing ZLD performance. Therefore, this study discusses drivers and ROC pretreatment technologies to improve ZLD efficiency and presents a scientometric review of global trends. The advantages, disadvantages, and economic and environmental aspects of conventional and emerging pre-treatment technologies were studied. Traditional treatment of chemical processes combined with precipitation removes a large amount of scaling ions; however, high operation and maintenance costs and limited full-scale plant experience are the main drawbacks. Softening and coagulation are most commonly applied to treat large volumes at a moderate cost; however, substantial sludge production and increased conductivity are major operational issues. Moreover, emerging technologies efficiently remove scale-forming ions with high capital and operating costs. New variations in standard reverse osmosis technologies have improved ZLD efficiency; nonetheless, scaling and fouling are of concern. Therefore, this review presents the studies on ROC pre-treatment technologies for removing scaling ions to enhance ZLD efficiency, which can help in future research.

Filtration with Multiple Species of Particles

Membrane filtration of feed containing multiple species of particles is a common process in the industrial setting. In this work, we propose a model for filtration of a suspension containing multiple particle species (concrete examples of our model are shown in two and three species), each with different affinities for the material of the porous filter membrane. Using the pore shape within the membrane as a design objective, we formulate a number of optimization problems pertaining to effective separation of desired and undesired particles in the special case of two-particle species and we present results showing how properties such as feed composition affect the optimal filter design. In addition, we propose a novel multi-stage filtration strategy, which provides a significant mass yield improvement for the desired particles, and, surprisingly, higher purity of the product as well.

Performance of novel GO-Gly/HNTs and GO-GG/HNTs nanocomposites for removal of Pb(II) from water: optimization based on the RSM-CCD model

For the first time, in this study, two novel glycogen-graphene oxide/halloysite nanotubes (GO-Gly/HNTs) and guar gum-graphene oxide/halloysite nanotubes (GO-GG/HNTs) nanocomposites were synthesized as the adsorbents for removal of Pb(II) from water, and the ionic liquid was used in the synthesis as a green solvent. According to the SEM, TEM, EDS, BET, zeta potential, FTIR, and XRD results, GO-Gly/HNTs and GO-GG/HNTs were synthesized successfully. Response surface methodology (RSM) was applied to optimize the experimental conditions. Nanocomposites followed the Langmuir equilibrium model and were best fitted to the pseudo-second-order model. According to the thermodynamic model, the adsorption process was endothermic. Due to several features, these two novel nanocomposites can be considered the proper candidate for Pb(II) removal from water and wastewater. First, these nanocomposites have good adsorption capacity for Pb(II) removal, which is 219 mg/g for GO-Gly/HNTs and 315 mg/g for GO-GG/HNTs. Moreover, nanocomposites can be recycled with proper adsorption capacity after four repeated cycles. These materials can be used to remove Pb(II) from water in the presence of other contaminants because nanocomposites have selective tendency toward Pb(II) in the presence of other pollutants such as Cd²⁺, Cu²⁺, Cr²⁺, and Co²⁺. In addition, the presence of Ca²⁺, Mg²⁺, Na⁺, and K⁺ improve Pb(II) removal. Finally, possible mechanisms for each nanocomposite were represented.

Development of artificial neural networks software for arsenic adsorption from an aqueous environment

Arsenic contamination is a global problem, as it affects the health of millions of people. For this study, data-driven artificial neural network (ANN) software was developed to predict and validate the removal of As(V) from an aqueous solution using graphene oxide (GO) under various experimental conditions. A reliable model for wastewater treatment is essential in order to predict its overall performance and to provide an idea of how to control its operation. This model considered the adsorption process parameters (initial concentration, adsorbent dosage, pH, and residence time) as the input variables and arsenic removal as the only output. The ANN model predicted the adsorption efficiency with high accuracy for both training and testing datasets, when compared with the available response surface methodology (RSM) model. Based on the best model synaptic weights, user-friendly ANN software was created to predict and analyze arsenic removal as a function of adsorption process parameters. We developed various graphical user interfaces (GUI) for easy use of the developed model. Thus, a researcher can efficiently operate the software without an understanding of programming or artificial neural networks. Sensitivity analysis and quantitative estimation were carried out to study the function of adsorption process parameter variables on As(V) removal efficiency, using the GUI of the model. The model prediction shows that the adsorbent dosages, initial concentration, and pH are the most influential parameters. The efficiency was increased as the adsorbent dosages increased, decreasing with initial concentration and pH. The result show that the pH 2.0-5.0 is optimal for adsorbent efficiency (%).

Process modeling, characterization, optimization, and mechanisms of fluoride adsorption using magnetic agro-based adsorbent

In this study, fluoride removal from polluted potable water using magnetic carbon-based adsorbents derived from agricultural biomass was thoroughly investigated. An experimental matrix is designed considering the interactive effects of independent process variables (pH, adsorbent dose, contact time, and initial fluoride concentration) on the removal efficiency. Isotherms and kinetics studies, as well as anions interactions, were also investigated to understand the adsorption mechanisms further. The model parameters of isotherms and kinetics are estimated using nonlinear differential evolution optimization (DEO). Approaches like adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) are implemented to predict the fluoride removal and identify the optimal process values. The optimum removal efficiency of GAC-Fe₃O₄ (89.34%) was found to be higher than that of PAC-Fe₃O₄ (85.14%). Kinetics experiments indicated that they follow the intraparticle diffusion model, and adsorption isotherms indicated that they follow Langmuir and Freundlich models. Both PAC-Fe₃O₄ and GAC-Fe₃O₄ adsorbents have shown an adsorption capacity of 1.20 and 2.74 mg/g, respectively. The model predictions from ANFIS have a strong correlation with experimental results and superior to RSM predictions. The shape of the contours depicts the nonlinearity of the interactive effects and the mechanisms in the adsorption process.

Fe3O4@C Nanoparticles Synthesized by In Situ Solid-Phase Method for Removal of Methylene Blue

Fe3O4@C nanoparticles were prepared by an in situ, solid-phase reaction, without any precursor, using FeSO4, FeS2, and PVP K30 as raw materials. The nanoparticles were utilized to decolorize high concentrations methylene blue (MB). The results indicated that the maximum adsorption capacity of the Fe3O4@C nanoparticles was 18.52 mg/g, and that the adsorption process was exothermic. Additionally, by employing H2O2 as the initiator of a Fenton-like reaction, the removal efficiency of 100 mg/L MB reached ~99% with Fe3O4@C nanoparticles, while that of MB was only ~34% using pure Fe3O4 nanoparticles. The mechanism of H2O2 activated on the Fe3O4@C nanoparticles and the possible degradation pathways of MB are discussed. The Fe3O4@C nanoparticles retained high catalytic activity after five usage cycles. This work describes a facile method for producing Fe3O4@C nanoparticles with excellent catalytic reactivity, and therefore, represents a promising approach for the industrial production of Fe3O4@C nanoparticles for the treatment of high concentrations of dyes in wastewater.