Single and competitive dye adsorption onto chitosan–based hybrid hydrogels using artificial neural network modeling

Chitosan-based adsorbents for remediation of toxic dyes from wastewater: A review on adsorption mechanism, reusability, machine learning based modeling and future perspectives

The disposal of recalcitrant dyes in aquatic environments from various industrial sectors is a threat to both the plant and animal kingdom. The presence of dyes in various water bodies undermines the availability of uncontaminated drinking water and may result in serious health-related issues and diseases. Therefore, it is of utmost importance to get rid of these harmful dyes from the aquatic environment. Hence various treatment techniques came to the fore, but they have their corresponding advantages and disadvantages. Several researchers have reported the adsorption of dyes with carbon-based composites, polymeric materials, and metal-based nanoparticles. However, the one with biocompatible materials or biopolymers deserves special attention as they are benign from an environmental viewpoint. We have chosen chitosan as our material of interest and elaborated on the positive aspects of chitosan as an excellent candidate for dye adsorption. Although a plethora of review articles has been disseminated in the past to underscore the utilization of chitosan-based adsorbents in the extraction of dyes, this manuscript endeavors to furnish a thorough examination of the complete adsorption process, encompassing its parameters and kinetics, thus facilitating a reader lacking foundational knowledge in this field to attain a more comprehensible understanding of the subject matter. This review also integrates a comprehensive overview of optimization methodologies for dye adsorption and examines relevant patents-an area that, to the best of our knowledge, has not been thoroughly addressed in previous review articles. Conclusively, it can be stated that chitosan can efficiently adsorb dye from wastewater showing good performance even after five cycles of adsorption/desorption. Moreover, several software programs can be used for optimizing maximum dye adsorption capacity of chitosan which shows well alignment with experimental results, thus making it suitable for real-life applications.

Experimental and modeling of potassium diclofenac uptake on activated carbon

The presence of pharmaceuticals in wastewater resulting from human activities has driven researchers to explore effective treatment methods such as adsorption using activated carbon (AC). While AC shows promise as an adsorbent, further studies are essential to comprehend its entire interaction with pharmaceuticals. This article investigates the adsorption of potassium diclofenac (PD) onto AC using experimental and modeling approaches. Batch adsorption studies coupled with Fourier transform infrared spectroscopy (FTIR) were employed to clarify the adsorption mechanism of PD on AC. Various kinetic and isotherm adsorption models were applied to analyze the adsorbent-adsorbate interaction. The kinetics were best described by Avrami's fractional order (AFO) nonlinear model. Also, the intraparticle diffusion (IP) model reveals a three-stage adsorption process. The experimental equilibrium data fitted well with the three-parameter nonlinear Liu model, indicating a maximum adsorption capacity (Qmax) of 88.45 mg g-1 and suggesting monolayer or multilayer adsorption. Thermodynamic analysis showed favorable adsorption (ΔG° < 0), with an enthalpy change (ΔH° = -30.85 kJ mol-1) characteristic of physisorption involving hydrogen bonds and π-π interactions. The adsorption mechanism was attributed to forming a double layer (adsorbate-adsorbent and adsorbate-adsorbate).

The use of artificial neural network for modelling adsorption of Congo red onto activated hazelnut shell

Activated hazelnut shell (HSAC), an organic waste, was utilized for the adsorptive removal of Congo red (CR) dye from aqueous solutions, and a modelling study was conducted using artificial neural networks (ANNs). The structure and characteristic functional groups of the material were examined by the FTIR method. The BET surface area of the synthesized material, named HSAC, was 812 m2/g. Conducted in a batch system, the adsorption experiments resulted in a notable removal efficiency of 87% under optimal conditions. The kinetic data for hazelnut shell activated carbon (HSAC) removal of CR were most accurately represented by the pseudo-second-order kinetic model (R2 = 0.998). Furthermore, the equilibrium data demonstrated a strong agreement with the Freundlich model. The maximum adsorption capacity of HSAC for CR was determined to be 34.8 mg/g. The optimum adsorption parameters were determined to be pH 6, contact time of 60 min, 10 g/L of HSAC, and a concentration of 400 mg/L for CR. Considering the various experimental parameters influencing CR adsorption, an artificial neural network (ANN) model was constructed. The analysis of the ANN model revealed a correlation of 98%, indicating that the output parameter could be reliably predicted. Thus, it was concluded that ANN could be employed for the removal of CR from water using HSAC.

Synthesis of zeolite from industrial wastes: a review on characterization and heavy metal and dye removal

Increasing world population, urbanization, and industrialization have led to an increase in demand in production and consumption, resulting in an increase in industrial solid wastes and pollutant levels in water. These two main consequences have become global problems. The high Si and Al content of solid wastes suggests that they can be used as raw materials for the synthesis of zeolites. In this context, when the literature studies conducted to obtain synthetic zeolites are evaluated, it is seen that hydrothermal synthesis method is generally used. In order to improve the performance of the hydrothermal synthesis method in terms of energy cost, synthesis time, and even product quality, additional methods such as alkaline fusion, ultrasonic effect, and microwave support have been developed. The zeolites synthesized by different techniques exhibit superior properties such as high surface area and well-defined pore sizes, thermal stability, high cation exchange capacity, high regeneration ability, and catalytic activity. Due to these specific properties, zeolites are recognized as one of the most effective methods for the removal of pollutants. The toxic properties of heavy metals and dyes in water and their carcinogenic effects in long-term exposure pose a serious risk to living organisms. Therefore, they should be treated at specified levels before discharge to the environment. In this review study, processes including different methods developed for the production of zeolites from industrial solid wastes were evaluated. Studies using synthetic zeolites for the removal of high levels of health and environmental risks such as heavy metals and dyes are reviewed. In addition, EPMA, SEM, EDX, FTIR, BET, AFM, and 29Si and 27Al NMR techniques, which are characterization methods of synthetic zeolites, are presented and the cation exchange capacity, thermodynamics of adsorption, effect of temperature, and pH are investigated. It is expected that energy consumption can be reduced by large-scale applications of alternative techniques developed for zeolite synthesis and their introduction into the industry. It is envisaged that zeolites synthesized by utilizing wastes will be effective in obtaining a green technology. The use of synthesized zeolites in a wide variety of applications, especially in environmental problems, holds great promise.

Enhanced dye sequestration with natural polysaccharides-based hydrogels: A review

Due to the expansion of industrial activities, the concentration of dyes in water has been increasing. The dire need to remove these pollutants from water has been heavily discussed. This study focuses on the reproducible and sustainable solution for wastewater treatment and dye annihilation challenges. Adsorption has been rated the most practical way of the several decolorization procedures due to its minimal initial investment, convenient utility, and high-performance caliber. Hydrogels, which are three-dimensional polymer networks, are notable because of their potential to regenerate, biodegrade, absorb bulky amounts of water, respond to stimuli, and have unique morphologies. Natural polysaccharide hydrogels are chosen over synthetic ones because they are robust, bioresorbable, non-toxic, and cheaply accessible. This study has covered six biopolymers, including chitosan, cellulose, pectin, sodium alginate, guar gum, and starch, consisting of their chemical architecture, origins, characteristics, and uses. The next part describes these polysaccharide-based hydrogels, including their manufacturing techniques, chemical alterations, and adsorption effectiveness. It is deeply evaluated how size and shape affect the adsorption rate, which has not been addressed in any prior research. To assist the readers in identifying areas for further research in this subject, limitations of these hydrogels and future views are provided in the conclusion.

Modeling competitive biosorption for methylene blue removal on rape straw powders using response surface methodology in a ternary dye aqueous solution

The improvement of biosorption efficiency for selective dye removal in a multi-dye aqueous system has become an increasingly significant research topic. However, the competitive effects of coexisting dyes and the target dye in such systems remain uncertain due to complex interactions between adsorbent and coexisting dyes. Therefore, in this research, response surface methodology (RSM) model was effectively employed to investigate the competitive effects of allura red (AR) and malachite green (MG) on methylene blue (MB) removal in a ternary dye aqueous system using three different parts of rape straw powders. In the current design of RSM, the initial concentrations of AR and MG dyes ranging from 0 mg·L-1 to 500 mg·L-1 were considered as influencing factors, while the removal rates of MB on adsorbents at an initial concentration of 500 mg·L-1 were established as response values. The RSM models exhibited high correlation coefficients with adjusted R2 values of 0.9908 (pith core), 0.9870 (seedpods), and 0.9902 (shells), respectively, indicating a close fitted between predicted and actual values. The proposed models indicated that the perturbation effects of initial AR and MG concentrations were observed on the removal rates of MB by three types of rape straw powders in a ternary dye aqueous system, resulting in a decrease in MB removal rates, particularly at higher initial AR concentration due to stronger competitive effects compared to initial MG concentration. The structures of rape straw powders, including pith core, seedpods and shell, were analyzed using scanning eletron microscoe (SEM), energy dispersive spectroscopy (EDS), N2 physisorption isotherm, frourier transform infared spectroscopy (FTIR), Zeta potential classes and fluorescence spectrum before and after adsorption of MB in various dye aqueous systems. The characteristics of rape straw powders suggested that similar adsorption mechanisms, such as electrostatic attraction, pore diffusion, and group complex formation for MB, AR, and MG, respectively, occurred on the surfaces of adsorbents during their respective adsorption processes. This leads to significant competitive effects on the removal rates of MB in a ternary dye aqueous system, which are particularly influenced by initial AR concentrations as confirmed through fluorescence spectrum analysis.

Cr(VI) and doxorubicin adsorptive capture by a novel bionanocomposite of Ti-MOF@TiO2 incorporated with watermelon biochar and chitosan hydrogel

Biodegradable polymers, biochars and metal organic frameworks (MOFs) have manifested as top prospects for elimination of harmful pollutants. In the current study, Ti-MOF was synthesized and decorated with TiO2 nanoparticles, then embedded into watermelon peel biochar and functionalized with chitosan hydrogel to produce Ti-MOF@TiO2@WMPB@CTH. Various instruments were employed to assure the effective production of the bionanocomposite. The HR-TEM and SEM studies referred to excellent surface porosity and homogeneity of Ti-MOF@TiO2@WMPB@CTH bionanocomposite, with 51.02-74.23 nm. Based on the BET analysis, the mesoporous structure has a significant surface area of 366.04 m2 g-1 and a considerable total pore volume of 11.38 × 10-2 cm3 g-1, with a mean pore size of 12.434 nm. Removal of doxorubicin (DOX) and hexavalent chromium (Cr(VI)) was examined under various experimentations. Pseudo-second order kinetic models in addition to Langmuir isotherm offered the best fitting. Thermodynamic experiments of the two contaminants demonstrated spontaneous and endothermic interactions. After five subsequent adsorption and desorption cycles, Ti-MOF@TiO2@WMPB@CTH bionanocomposite demonstrated an exceptional recyclability for the elimination of DOX and Cr(VI) ions, reaching 97.96 % and 95.28 %, respectively. Finally, the newly designed Ti-MOF@TiO2@WMPB@CTH bionanocomposite demonstrated a high removing efficiency of Cr(VI) ions and DOX from samples of real water.

Carboxymethyl chitosan/polyvinyl alcohol double network hydrogels prepared by freeze-thawing and calcium chloride cross-linking for efficient dye adsorption

The discharge of dye wastewater resulting from rapid industrial development has become a serious environmental concern. Therefore, there is a pressing need to develop efficient methods and technologies for the removal of dye pollutants. This study introduced a double network hydrogel, with varying carboxymethyl chitosan (CMCS) contents and polyvinyl alcohol (PVA), employing a combination of freeze- thawing and calcium chloride cross-linking. The investigation focused on the rheological properties of the hydrogels and their removal ability of acidic blue 93 (AB). The results showed that the strength and viscoelastic modulus of composite hydrogels were positively correlated with the CMCS content, and all composite hydrogels exhibited the typical weak strain overshoot behavior. The pore size of the gel initially decreased and then increased, with the densest pores observed at 4 wt% CMCS, showing the optimal removal ability for AB. The adsorption process followed pseudo second-order kinetic model, dominated by external diffusion, and exhibited inhomogeneous multilayer adsorption. This study unveils the potential of CMCS/PVA gels as adsorbents, offering inspirations for the design and development of polyvinyl alcohol-based gels for applications in the food industry.

Applications of covalent organic frameworks for the elimination of dyes from wastewater: A state-of-the-arts review

Covalent organic frameworks (COFs) are class of porous coordination polymers made up of organic building blocks joined together by covalent bonding through thermodynamic and controlled reversible polymerization reactions. This review discussed versatile applications of COFs for remediation of wastewater containing dyes, emphasizing the advantages of both pristine and modified materials in adsorption, membrane separation, and advanced oxidations processes. The excellent performance of COFs towards adsorption and membrane filtration has been centered to their higher crystallinity and porosity, exhibiting exceptionally high surface area, pore size and pore volumes. Thus, they provide more active sites for trapping the dye molecules. On one hand, the photocatalytic performance of the COFs was attributed to their semiconducting properties, and when coupled with other functional semiconducting materials, they achieve good mechanical and thermal stabilities, positive light response, and narrow band gap, a typical characteristic of excellent photocatalysts. As such, COFs and their composites have demonstrated excellent potentialities for the elimination of the dyes.

Novel partially cross-linked nanoparticles graft co-polymer as pore former for polyethersulfone membranes for dyes removal

A newly developed water-soluble polymeric nano-additive termed "partially cross-linked nanoparticles graft copolymer (PCLNPG)" has been successfully synthesized and harnessed as a pore former for modifying a polyethersulfone ultrafiltration membrane for dyes removal. The PCLNPG content was varied in the PES polymeric matrix aiming to scrutinize its impact on membrane surface characteristics, morphological structure, and overall performance. Proposed interaction mechanism between methylene blue (MB), methyle orange (MO), and malachite green (MG) dyes with PES membrane was presented as well. Hydrophilicity and porosity of the novel membrane increased by 18 and 17 %, respectively, when manufactured with a 3 Wt. % PCLNPG, according to the findings. Besides this, the disclosed increased porosity, rather than the hydrophilic properties of the water-soluble PCLNPG, was the principal cause of the diminished contact angle. Meanwhile, raising the PCLNPG content in the prepared membrane made worthy shifts in its structure. A sponge-like region was materialized near the bottom surface as well. The membrane's pure water flux (PWF) synthesized with 3 Wt.% PCLNPG recorded 628 LMH, which is estimated 3.95 fold the pristine membrane. MG, MB, and MO dyes were rejected by 90.6, 96.3, and 97.87 %, respectively. These findings showed that the performance characteristics of the PES/PCLNPG membrane make it a potentially advantageous option to treat the textile wastewater.

A Comprehensive Review of Polysaccharide-Based Hydrogels as Promising Biomaterials

Polysaccharides have emerged as a promising material for hydrogel preparation due to their biocompatibility, biodegradability, and low cost. This review focuses on polysaccharide-based hydrogels' synthesis, characterization, and applications. The various synthetic methods used to prepare polysaccharide-based hydrogels are discussed. The characterization techniques are also highlighted to evaluate the physical and chemical properties of polysaccharide-based hydrogels. Finally, the applications of SAPs in various fields are discussed, along with their potential benefits and limitations. Due to environmental concerns, this review shows a growing interest in developing bio-sourced hydrogels made from natural materials such as polysaccharides. SAPs have many beneficial properties, including good mechanical and morphological properties, thermal stability, biocompatibility, biodegradability, non-toxicity, abundance, economic viability, and good swelling ability. However, some challenges remain to be overcome, such as limiting the formulation complexity of some SAPs and establishing a general protocol for calculating their water absorption and retention capacity. Furthermore, the development of SAPs requires a multidisciplinary approach and research should focus on improving their synthesis, modification, and characterization as well as exploring their potential applications. Biocompatibility, biodegradation, and the regulatory approval pathway of SAPs should be carefully evaluated to ensure their safety and efficacy.

The deconstruction and recombination of endogenous active units of carbon@chitosan@montmorillonite nanosheet microsphere adsorbent caused by cadmium and copper cations benefit for high adsorption performance

The highly efficient removal of heavy metals is one of the important factors to evaluate adsorbents. In our study, carbon@chitosan@montmorillonite nanosheet (C@CS@MTN) was successfully prepared via layer-by-layer assembly for the removal of Cu2+ and Cd2+ from solution. High-intensity ultrasound peeling technology was used to release Si-O tetrahedron and Al-O octahedron from montmorillonite in order to exert their optimal adsorption potential. Fourier transform infrared spectroscopy, an X-ray diffractometer, BET surface area measurement, and the inductively coupled plasma emission spectrometry were adopted to investigate the morphology, functional groups, and adsorption capacity of C@CS@MTN. Batch experiment results indicated that both Cu2+ and Cd2+ were effectively removed from solution with the range of pH from 2 to 6. The removal ratio of Cu2+ and Cd2+ onto C@CS@MTN increased with the rise of reaction temperature and their maximum adsorption capacities reached 1108.8 mg·g-1 and 237.4 mg·g-1, respectively, under the condition of the reaction temperature 40 °C, the reaction time 4 h, and the pH = 6. The molecular simulation calculation indicated that there was an obvious electron transfer between Si-O tetrahedron and metal cations, but not for Al-O octahedron. In comparison to Al-O octahedron, the bonding of Cu-O and Cd-O caused the Si-O bond to be broken, resulting in the deconstruction of Si-O tetrahedron and their recombination via the junction of O atoms. It was exactly the deconstruction and recombination of endogenous active units that provide more sites for metal ion adsorption.

The highly efficient simultaneous removal of Pb2+ and methylene blue induced by the release of endogenous active sites of montmorillonite

The inherent periodic structure of montmorillonite limits the adsorption capacity of its endogenous active units such as Si-O tetrahedron and Al-O octahedron for pollutants. The high-intensity ultrasound method was used to release these active units and the layer-by-layer assembly was adopted to prepare carbon@chitosan@montmorillointe microsphere adsorbent (C@CS@Mt) to give full play to the adsorption capacity of montmorillonite. The montmorillonite nanosheet exhibited good hole-making ability, resulting in high surface area, pore volume and pore diameter of microspheres. Benefitting from the release of active sites in Si-O tetrahedron and Al-O octahedron of montmorillonite nanosheets, the adsorption capacity of C@CS@Mt was significantly improved. The maximum adsorption capacities of Pb²⁺ and methylene blue (MB) reached 884.19 mg·g^-1 and 326.21 mg·g^-1, respectively. The simultaneous adsorption experiments indicated that the occupation of active sites by Pb²⁺ caused the observed decrease of MB adsorption capacity. The theoretical calculations indicated that Pb was preferentially adsorbed by active adsorption units due to strong electron donating ability in comparison to MB. As an active unit, Si-O tetrahedron exhibited stronger adsorption capacity for cationic dyes than Al-O octahedron due to both the large electronegativity and lower adsorption binding energy.

Optimization-based artificial neural networks to fit the isotherm models parameters of aqueous-phase adsorption systems

An artificial neural network (ANN) hybrid structure was proposed that, unlike the standard ANN structure optimization, allows the fit of several adsorption curves simultaneously by indirectly minimizing the real output error. To model a case study of 3-aminophenol adsorption phenomena onto avocado seed activated carbon, a hybrid ANN was applied to fit the parameters of the Langmuir and Sips isotherm models. Network weights and biases were optimized with two different methods: particle swarm optimization (PSO) and genetic algorithm (GA), due to their good convergence in large-scale problems. In addition, the data were also fitted with the Levenberg-Marquardt feedforward optimization method to compare the performance between a standard ANN model and the hybrid model proposed. Results showed that the ANN-isotherm hybrid models with both PSO and GA were able to accurately fit the experimental equilibrium adsorption capacity data using the Sips isotherm model, obtaining Pearson's correlation coefficient (R) of the order of 0.9999 and mean squared error (MSE) around 0.5, very similar to the performance of standard ANN using Levenberg-Marquardt optimization. On the other hand, the results with Langmuir isotherm models were quite inferior in the ANN-isotherm hybrid models with both PSO and GA, with R and MSE of around 0.944 and 4.04 × 10², respectively. The proposed ANN-isotherm hybrid structure was successfully applied to estimate the parameters of adsorption isotherms, reducing the computational demand and the exhausting task of estimating the parameters of each adsorption curve individually.

Biobased hydrogels and their composite containing MgMOF74 for the removal of textile dyes and wastewater treatment

In this work, we report the synthesis of a biobased hydrogel comprised of collagen, chitosan, and polyurethane for the removal of textile dyes. The adsorption capacity of this hydrogel was improved by adding a magnesium metal-organic framework to the semi-interpenetrating polymer matrix yielding a composite hydrogel. Removal of Bismarck Brown and Congo red was studied, and the experimental results fit Freundlich's model. Both hydrogel formulations were tested for the removal of textiles dyes from wastewaters. The magnesium-metal organic framework improved the efficiency of the biobased hydrogel for the removal of direct and mordant dyes reaching up to 89 ± 2%. The composite hydrogel was tested for the removal of Congo Red in a fixed bed column observing the breakthrough point after 168 min. Also, a flocculant material was prepared from collagen and chitosan and was tested for the removal of direct red dye from wastewater removing up to 80 ± 1%. The pretreated wastewater by coagulation-flocculation was treated by adsorption yielding a global removal efficiency of 99%. Finally, the studied hydrogels are potentially biodegradable being completely degraded by the proteolytic action after 22 days. PRACTITIONER POINTS: Composite hydrogels of collagen, chitosan, and MgMOF74 removed efficiently textile dyes from wastewater in batch systems and fixed bed columns. A biobased flocculant of collagen and chitosan significantly improved water quality after coagulation flocculation. Hydrogels were reusable for four cycles, and they can be proteolytically degraded after 22 days.

Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin

Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R² are 0.940 and 0.976) than the published NN-LFER model (R² are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log C_e, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation

The present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases the process’ self- sustainability. Adsorption experiments with varying fecal ash loadings, initial urine concentrations, and adsorption temperatures were conducted, and the acquired data were applied to determine the adsorption kinetics. These three process parameters and their interactions served as the input vectors for the artificial neural network model, with the percentage urea adsorption onto fecal ash serving as the output. The Levenberg–Marquardt (TRAINLM) and Bayesian regularization (TRAINBR) techniques with mean square error (MSE) were trained and tested for predicting percentage adsorption. TRAINBR was demonstrated in our study to be an ideal match for improving urea adsorption, with an accuracy of R = 0.9982 and a convergence time of seven seconds. The ideal conditions for maximum urea adsorption were determined to be a high starting concentration of 13.5 g.L−1; a low temperature of 30 °C, and a loading of 1.0 g of adsorbent. For urea, the improved settings resulted in maximum adsorption of 92.8%.

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.

Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System

This study investigates chromium removal onto modified maghemite nanoparticles in batch experiments based on a central composite design. The effect of modified maghemite nanoparticles on the adsorptive removal of chromium was quantitatively elucidated by fitting the experimental data using artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modeling approaches. The ANN and ANFIS models, relating the inputs, i.e., pH, adsorbent dose, and initial chromium concentration to the output, i.e., chromium removal efficiency (RE), were developed by comparing the predicted value with that of the experimental values. The RE of chromium ranged from 49.58% to 92.72% under the influence of varying pH (i.e., 2.6–9.4) and adsorbent dose, i.e., 0.8 g/L to 9.2 g/L. The developed ANN model fits the experimental data exceptionally well with correlation coefficients of 1.000 and 0.997 for training and testing, respectively. In addition, the Pearson’s Chi-square measure (χ2) of 0.0004 and 0.0673 for the ANN and ANFIS models, respectively, indicated the superiority of ANN over ANFIS. However, a small discrepancy in the predictability of the ANFIS model was observed owing to the fuzzy rule-based complexity and overtraining of data. Thus, the developed models can be used for the online prediction of RE onto synthesized maghemite nanoparticles with different sets of input parameters and it can also predict the operational errors in the system.

Recycling of Waste Cotton Sheets into Three-Dimensional Biodegradable Carriers for Removal of Methylene Blue

Waste cotton sheets (WCS) are promising cellulose sources due to their high content of cellulose and large amount of disposal every year, which could be recycled and employed as low-cost structural materials. The present work aims at investigating the efficacy of hydrogel adsorbents prepared from regenerated WCS as the carriers of activated carbon (AC) for treating the dye-contaminated water. Activated WCS was directly dissolved in lithium chloride/N,N-dimethylacetamide (LiCl/DMAc) solvent and then regenerated into cellulose hydrogels, which were employed as three-dimensional biodegradable matrices for loading an extremely high content of AC (up to 5000%). The morphology and properties of resultant adsorbents were studied in detail. The results showed that different washing methods and contents of AC and cellulose had obvious effects on water contents, mechanical properties, and adsorption capacities of AC/WCS hydrogels. Especially, the hydrogels containing high AC content washed by gradient ethanol solvent exhibited outstanding compressive strengths of up to 3.0 MPa at 60% strain, while the adsorption capacity of 5000%AC/0.3CS toward a model dye methylene blue (MB, initial concentration of 200 mg/L) reached 174.71 mg/g at pH 6.9 and 35 °C. This was comparable to the adsorption capacity of original AC powders, while no AC powders were released from hydrogels to water. The adsorption of MB followed the Dubinin-Astakhov model and pseudo-first-order mechanism. Thermodynamic studies showed the spontaneous and endothermic nature of the overall physical adsorption process. Therefore, this work demonstrates the feasibility to recycle WCS into biodegradable carriers of functional compounds, and the AC/regenerated cellulose hydrogels have a high potential as a promising adsorbent with low-cost and convenient separation for dye removal from wastewater.

Hydrogels Based on Poly([2-(acryloxy)ethyl] Trimethylammonium Chloride) and Nanocellulose Applied to Remove Methyl Orange Dye from Water

Bio-based hydrogels that adsorb contaminant dyes, such as methyl orange (MO), were synthesized and characterized in this study. The synthesis of poly([2-(acryloyloxy)ethyl] trimethylammonium chloride) and poly(ClAETA) hydrogels containing cellulose nanofibrillated (CNF) was carried out by free-radical polymerization based on a factorial experimental design. The hydrogels were characterized by Fourier transformed infrared spectroscopy, scanning electron microscopy, and thermogravimetry. Adsorption studies of MO were performed, varying time, pH, CNF concentration, initial dye concentration and reuse cycles, determining that when the hydrogels were reinforced with CNF, the dye removal values reached approximately 96%, and that the material was stable when the maximum swelling capacity was attained. The maximum amount of MO retained per gram of hydrogel (q = mg MO g⁻¹) was 1379.0 mg g⁻¹ for the hydrogel containing 1% (w w⁻¹) CNF. Furthermore, it was found that the absorption capacity of MO dye can be improved when the medium pH tends to be neutral (pH = 7.64). The obtained hydrogels can be applicable for the treatment of water containing anionic dyes.

Chitosan-based hybrid materials for adsorptive removal of dyes and underlying interaction mechanisms

Environmental pollution by dyes molecules has become a subject of intensive research in recent years due to their hazardous effects on human health, organisms, and animals. Effective treatment and removal of dye molecules from the environmental matrices and water sources are of supreme concern. The deployment of cheap, safe, green, sustainable, and eco-friendly materials to remove these pollutants from water is the main challenge during the last decades. Chitosan and its derivatives/composites, as a cheap, easily available, and environmentally friendly sorbent, have attracted increasing attention for the removal of dye molecules. This review article focuses on the application of chitosan and chitosan-based smart adsorbents for the removal of dyes. Recent methods for the preparation of chitosan-based composites and their application in the removal of dyes are discussed. Moreover, the possible mechanisms for the interaction of chitosan and chitosan-based adsorbents with dyes molecules were evaluated. Finally, future prospects of using chitosan as an adsorbent for the removal of dye molecules are directed.

Artificial neural network modeling for Congo red adsorption on microwave-synthesized akaganeite nanoparticles: optimization, kinetics, mechanism, and thermodynamics

This work aims to synthesize akaganeite nanoparticles (AKNPs) by using microwave and use them to adsorb Congo red dye (CR) from the aqueous solution. The AKNPs with an average particle size of about 50 nm in width and 100 nm in length could be fabricated in 20 min. The effects of pH, CR initial concentration, adsorption time, and adsorbent dosage on the adsorption process were investigated and the artificial neural network (ANN) was used to analyze the adsorption data. The various ANN structures were examined in training the data to find the optimal model. The structure with training function, TRAINLM; adaptation learning function, LARNGDM; transfer function, LOGSIG (in hidden layer) and PURELIN (in output layer); and 10 neutrons in hidden layer having the highest correlation (R² = 0.996) and the lowest MSE (4.405) is the optimal ANN structure. The consistency between the experimental data and the data predicted by the ANN model showed that the behavior of the adsorption process of CR onto AKNPs under different conditions can be estimated by the ANN model. The adsorption kinetics was studied by fitting the data into pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion models. The results showed that the adsorption kinetics obeyed the pseudo-second-order model and governed by several steps. The adsorption isotherms at the different temperatures were studied by fitting the data to Langmuir, Freundlich, and Temkin isotherm models. The R² obtained from the Langmuir model was above 0.9 and the highest value in three of four temperatures, suggesting that the adsorption isotherms were the best fit to the Langmuir model and the maximum adsorption capacity was estimated to be more than 150 mg/g. Thermodynamic studies suggested that the adsorption of CR onto AKNPs was a spontaneous and endothermic process and physicochemical adsorption. The obtained results indicated the potential application of microwave-synthesize AKNPs for removing organic dyes from aqueous solutions.

Synthesis and characterization of γ-MnO2/chitosan/Fe3O4 cross-linked with EDTA and the study of its efficiency for the elimination of zinc(II) and lead(II) from wastewater

In this research, a novel γ-MnO₂/chitosan/Fe₃O₄ nanocomposite was synthesized and modified by ethylenediaminetetraacetic acid (EDTA) for the separation and simultaneous elimination of Zn(II) and Pb(II) ions from aqueous solutions in a batch system. The magnetic nanocomposite was characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, and elemental analysis (EDAX). The results demonstrated that the magnetic nanocomposite was successfully synthesized and cross-linked. The predominant influential experimental parameters including pH, contact time, initial concentration, and temperature were analyzed in relation to the adsorption capacity. The experimental data were well converged with the double exponential kinetic model. Also, the results were well matched with the Langmuir isotherm, where the maximum adsorption values were 310.4 and 136 mg g^-1 for Pb(II) and Zn(II), respectively. On the other hand, in the binary-component system, the Langmuir-Freundlich model dominated the experimental data. The thermodynamic results (ΔG° < 0, ΔH° > 0, and ΔS° > 0) within the temperature range of 25-40 °C showed that the nature of adsorption by the nanocomposite for both ions was spontaneous and endothermic and was favored at higher temperatures. The simultaneous removal of two ions, the excellent magnetic separation, and the high efficiency in reuse (five effective recovery cycles) indicated the high capability of the EDTA-modified γ-MnO₂/chitosan/Fe₃O₄ nanocomposite in the treatment of industrial effluents from Pb(II) and Zn(II).

Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: response surface methodology and artificial neural network model studies

An aluminium-based metal–organic framework ((MOF), MIL-53(Al)), was hydrothermally synthesized, characterized and applied for the remediation of the herbicides dicamba (3,6-dichloro-2-methoxy benzoic acid) and 4-chloro-2-methylphenoxyacetic acid (MCPA) in aqueous medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to design, optimize and predict the non-linear relationships between the independent and dependent variables. The shared interaction of the effects of key response parameters on the adsorption capacity were assessed using the central composite design-RSM and ANN optimization models. The optimum adsorption capacities for dicamba and MCPA are 228.5 and 231.9 mg g⁻¹, respectively. The RSM ANOVA results showed significant p-values, with coefficients of determination (R²) = 0.988 and 0.987 and R² adjusted = 0.974 and 0.976 for dicamba and MCPA, respectively. The ANN prediction model gave R² = 0.999 and 0.999, R² adjusted = 0.997 and 0.995 and root mean square errors (RMSEs) of 0.001 and 0.004 for dicamba and MCPA, respectively. In each set of experimental conditions used for the study, the ANN gave better prediction than the RSM, with high accuracy and minimal error. The rapid removal (∼25 min), reusability (5 times) and good agreement between the experimental findings and simulation results suggest the great potential of MIL-53(Al) for the remediation of dicamba and MCPA from water matrices.

Rapid equilibration within a short time, high adsorption capacity, optimization, multivariate interaction of adsorption parameters and artificial neural network prediction model.

Recent Advancement of Molecular Structure and Biomaterial Function of Chitosan from Marine Organisms for Pharmaceutical and Nutraceutical Application

Chitosan is an innate cationic biological polysaccharide polymer, naturally obtained from chitin deacetylation, that possesses broad-spectrum properties such as antibacterial, biodegradability, biocompatibility, non-toxic, non-immunogenicity, and so on. Chitosan can be easily modified owing to its molecular chain that contains abundant active amino and hydroxyl groups, through various modifications. Not only does it possess excellent properties but it also greatly accelerates its solubility and endows it with additional special properties. It can be developed into bioactive materials with innovative properties, functions, and multiple uses, especially in the biomedical fields. In this paper, the unique properties and the relationship between the molecular structure of chitosan and its derivatives are emphasized, an overview of various excellent biomedical properties of chitosan and its current progress in the pharmaceutical and nutraceutical field have prospected, to provide the theoretical basis for better development and utilization of new biomedical materials of chitosan and its derivatives.

The tetracyclines removal by MgAl layered double oxide in the presence of phosphate or nitrate: Behaviors and mechanism exploration

Pollution of tetracyclines (TCs) in swine wastewater has been a critical concern worldwide. Notably, multiple anions (e.g. PO₄^3-, NO₃^-) coexist in the actual environments, which could significantly influence the TCs removal. In the current study, MgAl layered double oxide (MgAl-LDO) was adopted for investigating the TC removal performance with/without PO₄^3- or NO₃^-. In all systems, the adsorption performance exhibited two different approaches between low and high TC concentrations. In the single system, pseudo-second-order and the Freundlich model fitted well to the equilibrium adsorption data when TC concentration was below 125 mg·L^-1, while the pseudo-first-order and the linear model could describe the removal process at high TC concentration (>125 mg·L^-1). The maximum adsorption capacity was 83.56 mg·g^-1. In the co-existing system, the adsorption capacity was slightly enhanced when TC concentration below 150 mg·L^-1 however was inhibited at high concentration (>150 mg·L^-1). Combined with the characterization analyses, the interaction mechanism at low concentration was primarily surface adsorption on reconstructed LDH from LDO in the TC-alone system. It is worth mention that both PO₄^3- and NO₃^- facilitated the formation of LDH via rehydration of LDO which enhanced surface adsorption in the co-existing system. At high TC concentration, the formation of tetracycline-metal complexes played a dominant role in TC removal in the single system, whereas diminished complexation in the binary system led to the decreased TC removal. This study provides a theoretical and practical guidance for MgAl-LDO on the efficient remediation of actual tetracyclines wastewater.

Chitosan/poly(ε-caprolactone)-block-poly(ethylene glycol) copolymer electrospun membrane for the adsorption of dyes

The CS/PCL-(b-PEG) nanofibers with controllable core to shell ratio were fabricated as an adsorbent for dye adsorption.