Evaluation of a newly developed biosorbent using packed bed column for possible application in the treatment of industrial effluents for removal of cadmium ions

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Magnetic adsorbent developed with alkali-thermal pretreated biogas slurry solids for the removal of heavy metals: optimization, kinetic, and equilibrium study

Discharge of effluents containing heavy metal without adequate treatment causes contamination of water resources and creates environmental and health issues. Adsorption could be applied to remediate heavy metals from wastewater effectively. In this study, a low-cost adsorbent was prepared by magnetic modification of pretreated biogas slurry solids (BSS) to remove heavy metals such as Cu²⁺, Cd²⁺, and Pb²⁺. The temperature (423 K) and time (1.5 h) of pretreatment, the BSS to KOH ratio (1:10 w/v), and the ratio of magnetic iron nanoparticle (MIN) to pretreated BSS (PSS) (1:2 w/w) were optimized for the preparation of adsorbent. The magnetically modified pretreated biogas slurry solid (MMPSS) adsorbent was characterized by BET isotherm, FTIR, XRD, FESEM, VSM, and EDX analysis. MMPSS attained equilibrium at 60 min and showed an adsorption capacity of 26.84 mg/g, 24.79 mg/g, and 23.86 mg/g with removal percentages 89.46%, 82.63%, and 79.54% for Cu²⁺, Cd²⁺, and Pb²⁺, respectively, at 310 K and pH 6 with an initial concentration of 150 mg/L. The adsorption process followed a pseudo second-order model with an R² value above 0.9 for all metals with a well-approaching equilibrium pattern. The good fit of experimental data by the Langmuir isotherm model implied monolayer adsorption. The metal ions adsorbed onto MMPSS were able to desorb effectively in the presence of HCl and retained 83.01%, 84.66%, and 81.83% of the initial adsorption capacity for Cu²⁺, Cd²⁺, and Pb²⁺ respectively after 5 consecutive cycles.

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.

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.

Sustainable removal of cadmium from contaminated water using green alga - Optimization, characterization and modeling studies

This research study reported the feasibility of cadmium removal using green algae, Caulerpa scalpelliformis, under controlled environmental conditions. The algal biosorbent could effectively remove cadmium under broad range of test conditions, namely, initial pH (3-6), adsorbent mass (0.5-2.5 gL^-1) and shaking speed (60-100 rpm). The best operating conditions were identified using Central Composite Design under Response Surface methodology and found to be pH - 4.9, adsorbent mass - 2.1 gL^-1 and shaking speed - 90 rpm. Equilibrium studies were conducted and monolayer sorption was identified as the mechanism, confirmed by Langmuir isotherm (R² = 0.9920). The maximum Cd uptake achieved at optimal conditions was 111.11 mg g^-1. The kinetic constants of the best fit model (pseudo second order) were determined. The thermodynamic feasibility was verified (ΔG ^ͦ < 0) and the biosorption process was found to be endothermic (ΔH ^ͦ > 0). The mass transfer studies shows that the mass transfer coefficient was inversely related to the temperature. Presence of favorable surface functional groups and enhanced surface area confirmed the suitability of the synthesized biosorbent for effective removal of cadmium.

Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia

Lead (Pb) is a primary toxic heavy metal (HM) which present throughout the entire ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial intelligence (AI) models include overfitting, normalization, validation against classical AI models, and lack in learning/technology transfer. This study explores the extreme gradient boosting (XGBoost) model as a superior SuperLearning (SL) algorithms for Pb prediction. The proposed model was examined using historical data at the Bramble and Deception Bay (BB and DB) stations, Australia. The model was trained at one of the stations and transferred to a cross-station and vice versa. XGBoost showed higher reliability with less declination in (R²: coefficient of determination), i.e., 0.97 % over the testing phase, among others models at BB. At the cross-station (DB), the performance of the XGBoost model was decreased by 2.74 % (R²) against random forests (RF). The mean absolute error (MAE) observed 40 % (XGBoost) and 47 % (RF) less than artificial neural network (ANN). The XGBoost model performance declined by 3.44 % (R²) over testing (DB), which is minor among validated models. At the cross-station (BB), the XGBoost model showed the least decrement in terms of R², i.e., 7.99 % against the ANN (8.31 %), RF (10.26 %), and support vector machine (SVM, 36.19 %).

Lanthanide recovery by silica-Cyanex 272 material immobilized in alginate matrix

Mesoporous silica impregnate with Cyanex 272 (bis/2,4,4-trimethylpentyl/phosphinic acid) extractant was immobilized into an alginate matrix to obtain a composite sorbent easy to use and applicable in fixed-bed column continuous systems. The sorption efficiency of this material was tested for the recovery of Eu(III) ions from aqueous solutions in batch and continuous mode. The competition among rare earths ions (europium, lanthanum, and lutetium) and among rare earths and calcium or sodium ions was investigated. High calcium concentrations strongly reduce the sorption capacity of the alginate matrix that composes the hybrid material and the Cyanex 272 impregnated into silica powder improves the rare earths' sorption performance in this calcium charged media. The experimental breakthrough curves obtained were satisfactory fitted by Thomas model.

A Study on the Removal of Copper (II) from Aqueous Solution Using Lime Sand Bricks

Heavy metals such as Cu(II), if ubiquitous in the runoff, can have adverse effects on the environment and human health. Lime sand bricks, as low-cost adsorbents to be potentially applied in stormwater infiltration facilities, were systematically investigated for Cu(II) removal from water using batch and column experiments. In the batch experiment, the adsorption of Cu(II) to bricks reach an equilibrium within 7 h and the kinetic data fits well with the pseudo-second-order model. The sorption isotherm can be described by both the Freundlich and Langmuir model and the maximum adsorption capacity of the bricks is 7 ± 1 mg/g. In the column experiment, the best removal efficiency for Cu(II) was observed at a filler thickness of 20 cm, service time of 12 min with a Cu(II) concentration of 0.5 mg/L. The Cu(II) removal rate increases with the increasing bed depth and residence time. The inlet concentration and residence time had significant effects on the Cu(II) removal analyzed by the Box–Behnken design (BBD). The Adams-Bohart model was in good agreement with the experimental data in representing the breakthrough curve. Copper fractions in the bricks descend in the order of organic matter fraction > Fe-Mn oxides fraction > carbonates fraction > residual fraction > exchangeable fraction, indicating that the lime sand bricks after copper adsorption reduce the long-term ecotoxicity and bioavailability to the environment.

Modified grape stem as a renewable adsorbent for cadmium removal

In order to aggregate value to the grape stem (wastes), this research aim was to increase the adsorption capacity of Cd²⁺ by chemical modifications on grape stems. The grape stems were milled and sieved, resulting in the biosorbent, which was used for the chemical modifications resulting in E. H₂O₂, E. H₂SO₄ and E. NaOH. These were characterized by such means as its pH_PZC, Fourier transform-infrared (FTIR) spectroscopy, porosimetry, thermal stability and scanning electron microscopy. The ideal adsorption dose, the pH influence on adsorption, kinetics, equilibrium and thermodynamics studies were carried out. The FTIR spectroscopy suggests the occurrence of carboxyl, amine, and phenolic acting in Cd²⁺ sorption. The modification on grape biomass caused small increase in pore volume and specific surface area. The grape-based adsorbents have similar thermal stability, with irregular appearance and heterogeneity. 5.0 g kg^-1 is the best adsorption dose. The modified adsorbents exhibited increase in Cd²⁺ removal of 66% for E. NaOH, 33% for E. H₂O₂ and 8.3% for E. H₂SO₄. The use of grape stem as adsorbent is an attractive alternative, because its wastes have great availability, low cost and great potential for metal adsorption processes.

Optimization of copper, lead and cadmium biosorption onto newly isolated bacterium using a Box-Behnken design

Due to the progressive development of industrial and technological activities, heavy metal contamination is increasing each year and it poses a serious health and environmental risk. Microorganisms are capable of removing heavy metals from a contaminated environment. In this work, 51 microbial strains were isolated from heavy metal contaminated water and soil. The JAW1 strain, identified as Pseudomonas azotoformans, was selected and applied in bioremediation of the specific mixture of metals (Cd, Cu, and Pb) in an aqueous medium. The Box-Behnken design was used to optimize the biosorption process, with three factors: pH, initial metal concentration, concentration of the biosorbent. For the strain P. azotoformans JAW1, the optimal conditions were pH = 6.0, 25mg/L of each metal and 2g/L, following removal levels were achieved: Cd 44,67%; Cu 63,32%; Pb 78,23%. The possible interactions of cell-metal ions were evaluated using FT-IR analysis. The study indicated the presence of groups, which may be responsible for bonding of metal ions. The studies conducted on bioremediation mechanisms indicated that metal accumulation could occur on the cell surface (biosorption) where the amount of adsorbed metals reached: Cd 98,57%, Cu 69,76%, Pb 88,58%. P. azotoformans JAW1 exhibited a potential for application in the bioremediation of mining wastewater with complex metal contaminations.