Investigating micellar-enhanced ultrafiltration (MEUF) of mercury and arsenic from aqueous solution using response surface methodology and gene expression programming

Mechanistic Insights into Micelle-Enhanced Nanofiltration for Heavy Metal Removal: Transformation of Ion Transport and Fouling Phenomena

Toxic heavy metals are widely present in typical scenarios, such as mines and electroplating wastewater, presenting significant risks to biological and environmental safety. Membrane processes encounter a challenge in effectively intercepting heavy metals due to their small hydration radius. This research showcases the high efficiency of micelle-enhanced nanofiltration (MENF) in removing heavy metals. At the critical micelle concentration, sodium dodecyl sulfate demonstrated a high removal of Cu2+, Ni2+, Zn2+, and Cd2+ while maintaining substantial potential for complexation of heavy metals. The formation of micelles and the bonding of heavy metals with surfactants bolstered the resistance of heavy metal ions to transmembrane transport. The presence of heavy metals in ionic form in wastewater facilitated their complexation with surfactants or micelles. Notably, the valence state and concentration of interfering ions in the environment could slightly influence the removal of heavy metals by MENF. Additionally, MENF displayed remarkable antifouling properties. The loose gel layer created by surfactant molecules and the micelle enhanced the membrane permeability and reduced the scaling tendency of heavy metals. This study contributes to an improved understanding of the mechanisms involved in heavy metal rejection by using MENF.

Artificial intelligence models for predicting calcium and magnesium removal by polyfunctional ketone using ensemble machine learners

Calcium (Ca2+) and magnesium (Mg2+) are the major scaling ions of reverse osmosis concentrate in zero-liquid discharge systems, causing performance decline. In this study, we predicted the removal of Ca2+ and Mg2+ from simulated reverse osmosis concentrate by functional polyketones (FPKs). Four amines, including 1,2-diaminopropane (DAP), 1-(2-aminoethyl) piperazine (AEP), 1-(3-aminopropyl) imidazole (API), and butyl amine (BA) used to synthesize FPKs. The effects of various factors such as the amount of adsorbent, feed water concentration, and pH were investigated for process optimization. In this study, ensemble learner artificial intelligence models, decision tree (DT), extreme gradient boost (XGB), and random forest (RF) were used to predict Ca2+ and Mg2+ removal by the FPKs. Datasets were collected experimentally using FPKs to remove Ca2+ and Mg2+ from the simulated reverse osmosis concentrate. The predictions were made by XGB, DT, and RF models for the first chosen amine for Ca2+ and then for Mg2+, subsequently, this process was repeated with each amine. The developed DT, RF, and XGB models demonstrated higher coefficients of determination for predicting Mg2+ removal by AEP and DAP (R2 = 0.841-0.935) than by API and BA (R2 = 0.774-0.801) except in the RF and XGB model results (R2 = 0.801-0.846). Overall, the XGB model displayed good results for both Ca2+ and Mg2+ removal but slight changes were observed in the AEP and BA predictions by DT and RF. Therefore, artificial intelligence models may be a viable alternative for further insight in predicting Ca2+ and Mg2+ removal by FPKs from simulated reverse osmosis concentrate.

Human health risk assessment due to mercury use in gold mining areas in the Ecuadorian Andean region

Mining activity drives economic development and has established itself as one of the main industrial spheres globally. However, illegal, and artisanal gold mining, which uses mercury (Hg), is a major source of global pollution. Hg is highly toxic and persistent in the environment, affecting human health and the ecosystem. The objective of this research is to; (a) analyze Hg concentrations in surface waters of nine provinces of the Andean region of Ecuador and compare them with the maximum permissible limits of Ecuadorian regulations, and (b) evaluate the health risk of people exposed to waters with high Hg content through residential and recreational scenarios. In this study, 147 water samples from rivers and streams were analyzed. The results revealed worrying levels of Hg, especially in the provinces of Azuay and Loja where Hg values of up to 0.0913 mg/L and 0.0387 mg/L, respectively, were detected. In addition, it was found that 45% of the samples did not meet the water quality criteria for the preservation of aquatic life, which represents a severe risk to the ecosystem. The probabilistic risk analysis yielded values that exceeded the acceptable exposure limit for adults and children in residential settings in Azuay and Loja, while in the recreational scenario the safe exposure limit was exceeded for both receptors only in the province of Azuay. The elevated presence of Hg in the provinces, mainly in Azuay and Loja, possibly related to illegal gold mining activity, represents a threat to water quality and aquatic life in the Andean region of Ecuador. Children are especially vulnerable, and effective regulation is required to ensure the safety of the population. This study provides valuable information for decision makers regarding the risk associated with Hg exposure in areas of mining activity in the Ecuadorian Andean region. In addition, it can contribute to the development of policies and strategies to control contamination in mining environments and protect human and environmental health in the region.

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO2 Capture Processes

CO2 emission reduction is an essential step to achieve the climate change targets. Solvent-based post-combustion CO2 capture (PCC) processes are efficient to be retrofitted to the existing industrial operations/installations. Solvent degradation (and/or loss) is one of the main concerns in the PCC processes. In this study, the thermal degradation of monoethanolamine (MEA) is investigated through the utilization of hybrid connectionist strategies, including an artificial neural network-particle swarm optimization (ANN-PSO), a coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and an adaptive neuro-fuzzy inference system (ANFIS). Moreover, gene expression programming (GEP) is employed to generate a correlation that relates the solvent concentration to the operating variables involved in the adverse phenomenon of solvent thermal degradation. The input variables are the MEA initial concentration, CO2 loading, temperature, and time, and the output variable is the remaining/final MEA concentration after the degradation phenomenon. According to the training and testing phases, the most accurate model is ANFIS, and the reliability/performance of its optimal network is assessed by the coefficient of determination (R2), mean squared error, and average absolute relative error percentage, which are 0.992, 0.066, and 2.745, respectively. This study reveals that the solvent initial concentration has the most significant impact, and temperature plays the second most influential effect on solvent degradation. The developed models can be used to predict the thermal degradation of any solvent in a solvent-based PCC process regardless of the complicated reactions involved in the degradation phenomenon. The models introduced in this study can be employed for the development of more accurate hybrid models to optimize the proposed systems in terms of cost, energy, and environmental prospects.

Synthesis of heated aluminum oxide particles impregnated with Prussian blue for cesium and natural organic matter adsorption: Experimental and machine learning modeling

Heated aluminum oxide particles impregnated with Prussian blue (HAOPs-PB) are synthesized for the first time using different molar ratios of aluminum sulfate and PB to improve the adsorption of cesium (¹³³Cs⁺) and natural organic matter (NOM) from an aqueous solution. The Cs⁺ adsorption from various aqueous solutions, including surface, tap and deionized water by synthesized HAOPs-PB, is investigated. The influencing factors such as HAOPs-PB mixing ratio, pH and dosage are studied. In addition, pseudo 1st and 2nd order is tested for adsorption kinetics study. A machine learning model is developed using gene expression programming (GEP) to evaluate and optimize the adsorption process for Cs⁺ and NOM removal. Synthesized adsorbent showed maximum adsorption at a 1:1 M ratio of aluminum sulfate and PB in DI, tap, and surface water. The pseudo 2nd order kinetics model described the Cs ⁺ adsorption by HAOPs-PB more accurately that indicating physiochemical adsorption. Adsorption of Cs⁺ showed an increasing trend with higher HAOPs-PB concentration, while high pH also favored the adsorption. Maximum NOM adsorption is found at a higher HAOPs-PB dosage and a neutral pH value. Furthermore, the proposed GEP model shows outstanding performance for Cs⁺ adsorption modeling, whereas a modified-GEP model presents promising results for NOM adsorption prediction for testing dataset by learning the relationship between inputs and output with R² values of 0.9348 and 0.889, respectively.

Recovery of Metals from Wastewater-State-of-the-Art Solutions with the Support of Membrane Technology

This paper discusses the most important research trends in the recovery of metals from industrial wastewater using membrane techniques in recent years. Particular attention is paid to the preparation of new membranes with the required filtration and separation properties. At the same time, possible future applications are highlighted. The aspects discussed are divided into metals in order to clearly and comprehensibly list the most optimal solutions depending on the composition of the wastewater and the possibility of recovering valuable components (metalloids, heavy metals, and platinum group metals). It is shown that it is possible to effectively remove metals from industrial wastewater by appropriate membrane preparation (up to ~100%), including the incorporation of functional groups, nanoparticles on the membrane surface. However, it is also worth noting the development of hybrid techniques, in which membrane techniques are one of the elements of an effective purification procedure.

Design and fabrication of chitosan cross-linked bismuth sulfide nanoparticles for sequestration of mercury in river water samples

The existence of heavy metals in ecological systems poses great threats to living organisms due to their toxicant and bio-accumulating properties. Mercury is a known toxicant with notable malignant impacts. It has long been known to cause toxic threats to the health of living organisms since the break out of Minamata disease. The turbulent expulsion of mercury-based pollutants from the industrial sector, requires a proper solution. Many attempts have been made to design a greener and more efficient route for a satisfactory removal of mercury. In the current study, bismuth sulfide nanoparticles (BiSNPs) have been synthesized via the co-precipitation method. The BiSNPs were supported with crosslinked chitosan to enhance their sorption capacity and avoid leaching. The average size of the BiSNPs was 42 nm based on SEM micrographs. The SEM analysis of the bismuth sulfide chitosan-crosslinked beads (BiS-CB) showed that the beads possessed a spherical and smooth morphology with a size of 1.02 mm. The FTIR analysis showed that the beads possessed the characteristics bands of imine groups of chitosan, bismuth, sulfur, and glycosidic linkages present in the molecules. The XRD analysis confirmed the phase crystallinity of the BiS-CB with an average crystallite size of 11 nm. The BiS-CB was employed for the sorption of mercury from water samples. The maximum sorption capacity of 65.51 mg/g was achieved at optimized conditions of pH 5, concentration 80 ppm, in 45 min at 30 °C. The mechanism studied for mercury removal showed that sorption followed the complexation mechanism according to the SHAB concept. In conclusion, the results showed that the BiS-CB sorbent exhibited an excellent sorption capacity to remove mercury.

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.

A Combined CFD-Response Surface Methodology Approach for Simulation and Optimization of Arsenic Removal in a Fixed Bed Adsorption Column

An experimentally validated CFD model was developed for lab-scale arsenic (As) fixed-bed columns using COMSOL Multiphysics. The effects of key factors such as the adsorbent bed depth, the feed flow rate, and the initial As concentration (conc.) on the overall As removal performance were investigated. Subsequently, the CFD was combined with response surface methodology (RSM) to optimize process conditions and examine main and interaction effects of these factors on model responses, i.e., the As removal efficiency and the bed saturation time. The ANOVA results suggested that quadratic regression models were highly significant for both responses. The established regression model equations predicted the response values closer to CFD measurements. It was found that, compared with the initial As conc. and the feed flow rate, the effect of the bed depth was more significant. Moreover, both the As removal efficiency and the bed saturation time were increased reasonably with the increasing bed depth and decreased with the increasing feed flow rate and initial As conc. The optimum conditions for the As removal process were obtained as the bed height of 80 cm, the initial As concentration of 2.7 mmol/m3, and the feed flow rate of 1 L/min. The present combined CFD−RSM approach is a useful guideline in overall design and optimization of various lab-scale and industrial applications for removal of As from wastewater.

Effective and reusable 3D CuxS nanocluster structured magnetic adsorbent for mercury extraction from wastewater

The elimination of mercury from polluted water using an effective, cost-economic, and sustainable method was investigated in this work. A modulated multilayer magnetic Hg²⁺ extractor was prepared with a self-assembly engineering that permitting robust anchoring and uniform distribution of the negatively charged 3D Cu_xS nanocluster onto a polydopamine (PDA) covered positively strengthened Fe₃O₄ surface. The developed PAD@Fe₃O₄ supported copper sulfide composite (Cu_xS/PAD@Fe₃O₄) presented an unparalleled Hg²⁺ uptake performance with adsorption capacity of 1394.61 mg/g (without saturation), and extraordinary selectivity with distribution coefficient value K_d of 17419.2 mL/g. A complexation reaction during Hg²⁺ affinity was taken place on Cu_xS/PAD@Fe₃O₄ surface, and almost no components losses occurring during the adsorption. Furthermore, the as-prepared Cu_xS/PAD@Fe₃O₄ micron-adsorbent can be easily magnetic recovery and recycled with hydrochloric acid elution. The purification of 50 L Hg²⁺ containing wastewater, initial concentration of 20 μg/L can be achieved with Cu_xS/PAD@Fe₃O₄ dosage of 0.1 g and treatment cost of 0.077 US $. The outlet Hg²⁺ concentration met drinking water standard of the United States Environmental Protection Agency. The Cu_xS/PAD@Fe₃O₄ magnetic adsorbent can be fabricated cheaply and holds promise for scale-up applications.

Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants

A plethora of technologies has been developed over decades of extensive research on arsenic remediation, although the technical and financial perspective of arsenic removal plants in the field requires critical evaluation. In the present study, focusing on some of the pronounced arsenic-affected areas in West Bengal, India, we assessed the implementation and operation of different arsenic removal technologies using a dataset of 4000 spatio-temporal data collected from an in-depth field survey of 136 arsenic removal plants engaged in the public water supply. Our statistical analysis of this dataset indicates a 120% rise in the average cumulative capacity of the plants during 2014–2021. The majorities of the plants are based on the activated alumina with FeCl3 technology and serve about 49% of the population in the study area. The average cost of water production for the activated alumina with FeCl3 technology was found to be ₹7.56/m3 (USD $1 ≈ INR ₹70), while the lowest was ₹0.39/m3 for granular ferric hydroxide technology. A machine learning-based framework was employed to analyze the impact of water quality and treatment plant parameters on the removal efficiency, capital, and operational cost of the plants. The artificial neural network model exhibited adequate statistical significance, with a high F-value and R2 of 5830.94 and 0.72 for the capital cost model, 136,954, and 0.98 for the operational cost model, respectively. The relative importance of the process variables was identified through random forest models. The models indicated that flow rate, media, and chemicals are the predominant costs, while contaminant loading in influent water and a coagulating agent was important for removal efficiency. The established framework may be instrumental as a decision-making tool for water providers to assess the expected performance and financial involvement for proposed or ongoing arsenic removal plants concerning various design and quality parameters.