Optimal artificial neural network design for simultaneous modeling of multicomponent adsorption

Green synthesis of carbon nanotubes functionalized with iron nanoparticles and coffee husk biomass for efficient removal of losartan and diclofenac: Adsorption kinetics and ANN modeling studies

The presence of emerging contaminants in wastewater poses a global environmental challenge, requiring the development of innovative materials or methods for their treatment. This study focused on the production of green functionalized carbon nanotubes (CNTs) and using them in the adsorption of the pharmaceuticals Losartan (LOS) and Diclofenac (DIC). The efficiency of the methodology was verified by characterization techniques. Elemental composition analysis indicated a significant increase in the iron content after the green functionalization, proving the effectiveness of the method. Thermogravimetric analysis showed similar thermal degradation profiles for pristine CNTs and functionalized CNTs, indicating better post-functionalization thermal stability. BET analysis revealed mesoporous characteristics of CNTs, with increased surface area and pore volumes after functionalization. X-Ray diffraction confirmed the preservation of the lattice structure of the CNTs post-functionalization and post-adsorption, with changes in peak broadening suggesting surface modifications. LOS and DIC adsorption were evaluated via kinetic studies at four different concentrations (0.1-0.4 mmol/L) that were best represented by the pseudo-second order model, suggesting chemisorption mechanisms, with faster and higher uptakes for DIC (0.084-0.261 mmol/g; teq = 5 min) when compared to LOS (0.058-0.235 mmol/g; teq = 20 min). The curves were also studied via artificial neural networks (ANN) and revealed that the best ANN architecture for representing the experimental data is a network with [3 5 5 2] neurons trained using the Bayesian-Regularization algorithm and the Log-sigmoid (hidden layers) and Linear (output layer) transfer functions. The desorption study showed that CaCl2 had better performance in CNT regeneration, reaching its removal capacity above 50% up to 3 cycles, for both pharmaceuticals. These findings reveal the potential of the developed material as a promising adsorbent for targeted removal of pollutants, contributing to advances in the remediation of emerging contaminants and the application of artificial intelligence in adsorption research.

Pirarucu hydroxyapatite applied to ternary competitive adsorption of synthetic basic dyes as contaminants of emerging concern: kinetic, equilibrium, and ANN studies

This study investigated the single and multicomponent adsorption of three emerging pollutants, the basic dyes Rhodamine 6G (R6G), Auramine-O (AO), and Brilliant Green (BG) by using hydroxyapatite synthesized from Pirarucu scales as adsorbent (HAP). The adsorption process was studied using seven different systems: AO-single, R6G-single, BG-single, R6G + AO, BG + AO, BG + R6G, and R6G + AO + BG. For kinetics, the initial concentration of each adsorbate per system was 50 mg/L, the results showed that the singular adsorption of these dyes was best-represented by the pseudo-second-order model (qAO = 62.54 mg/g, qR6G = 7.91 mg/g, qBG = 62.40 mg/g), however, the multicomponent adsorption was well-fitted by a pseudo-first-order model (ternary system: qAO = 56.21 mg/g, qR6G = 14.95 mg/g, qBG = 60.62 mg/g). For equilibrium, the initial concentration of each adsorbate per system was 10-300 mg/L, and the single adsorption systems were best represented by the Langmuir model. Nonetheless, the results displayed in the multicomponent mixture showed the presence of inflection points of AO and R6G whenever BG was present in solution with C0 > 150 mg/L, thus indicating that BG has greater affinity with HAP. The presence of inflection points in the curves represented a limitation for applying traditional equilibrium models, thus, an artificial neural network (ANN) was applied to non-linear curve fit this process and satisfactorily predicted the kinetics and equilibrium data. Finally, the analysis of thermodynamics for the ternary mixture revealed that the adsorption process is spontaneous (ΔG < 0), endothermic (ΔH > 0), and increases to a disorganized state as the temperature rises (ΔS > 0).

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.

Investigating the effect of textural properties on CO2 adsorption in porous carbons via deep neural networks using various training algorithms

The adsorption of carbon dioxide (CO2) on porous carbon materials offers a promising avenue for cost-effective CO2 emissions mitigation. This study investigates the impact of textural properties, particularly micropores, on CO2 adsorption capacity. Multilayer perceptron (MLP) neural networks were employed and trained with various algorithms to simulate CO2 adsorption. Study findings reveal that the Levenberg-Marquardt (LM) algorithm excels with a remarkable mean squared error (MSE) of 2.6293E-5, indicating its superior accuracy. Efficiency analysis demonstrates that the scaled conjugate gradient (SCG) algorithm boasts the shortest runtime, while the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm requires the longest. The LM algorithm also converges with the fewest epochs, highlighting its efficiency. Furthermore, optimization identifies an optimal radial basis function (RBF) network configuration with nine neurons in the hidden layer and an MSE of 9.840E-5. Evaluation with new data points shows that the MLP network using the LM and bayesian regularization (BR) algorithms achieves the highest accuracy. This research underscores the potential of MLP deep neural networks with the LM and BR training algorithms for process simulation and provides insights into the pressure-dependent behavior of CO2 adsorption. These findings contribute to our understanding of CO2 adsorption processes and offer valuable insights for predicting gas adsorption behavior, especially in scenarios where micropores dominate at lower pressures and mesopores at higher pressures.

Global interpretation and generalizability of boosted regression models for the prediction of methylene blue adsorption by different clay minerals and alkali activated materials

In this study, Gradient Boosted Regression Trees is applied, for the first time, to predict governing factors for methylene blue (MB) adsorption on a variety of adsorbents involving clay minerals, such as kaolinite and sepiolite together with industrial wastes red mud and fly ash, and alkali activated materials synthesized from aforementioned raw materials. Dataset was constructed using electronic databases, such as ScienceDirect, Scopus, Elsevier, and Google, experimental studies published between 2005 and 2022 were covered. The final dataset included experimental conditions, such as adsorbent type, adsorbent properties (surface characteristics, density, and chemical modifications), pH of the medium, adsorbent dosage, and temperature; and it involved 914 datapoints, which were extracted out of 75 papers (out of ∼1360 initially screened). Among distinct parameters, initial adsorbate concentration was found to be the most dominant factor affecting the MB uptake. Concordantly, pH of the solution medium, raw material selection, and modification types were also found to be significant in MB adsorption. Results showed that in terms of raw material and modification types, sepiolite and chemical (acid and/or alkaline modification) and thermal treatments, respectively, come forward as the most powerful candidates for enhanced MB adsorption performance. Modifications applied on adsorbents should be evaluated separately, as there is no general rule applicable for all experimental conditions, and the strength of the contribution of modification type also depends on initial adsorbate concentration. Implementation of various imputation methods showed the importance of reporting experimental factors, such as surface area, in the literature. Range of applicability of the suggested modeling procedure was assessed to help experimenters in testing MB uptake under novel experimental conditions.

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.

Utilization of a double-cross-linked amino-functionalized three-dimensional graphene networks as a monolithic adsorbent for methyl orange removal: Equilibrium, kinetics, thermodynamics and artificial neural network modeling

Herein, it is aimed to develop a high-performance monolithic adsorbent to be utilized in methyl orange (MO) adsorption. Therefore, amino-functionalized three-dimensional graphene networks (3D-GN_f) fulfilling the requirements of reusability and high capacity have been fabricated via hydrothermal self-assembly approach followed by a double-crosslinking strategy. The potential utilization of 3D-GN_f as an adsorbent for removal MO has been assessed using both batch-adsorption studies and an artificial neural network (ANN) approach. Graphene oxide sheets have been amino-functionalized and cross-linked, by ethylenediamine (EDA) during hydrothermal treatment, following the glutaraldehyde has used as a double-crosslinking agent to facilitate the crosslinking of architecture. The successful fabrication of 3D-GN_f has been confirmed by field-emission scanning electron microscopy (FESEM), Fourier transform infrared (FT-IR), Raman and X-ray photoelectron spectroscopy (XPS). Moreover, N₂ adsorption/desorption isotherms have revealed the high specific surface area (1015 m² g^-1) with high pore volume (1.054 cm³ g^-1) and hierarchical porous structure of 3D-GN_f. The effect of initial concentration, contact time, and temperature on adsorption capacity have been thoroughly studied, and the kinetics, isotherms, and thermodynamics of MO adsorption have been modelled. The MO adsorption has been well defined by the pseudo-second-order kinetic model and Langmuir isotherm model with a monolayer adsorption capacity of 270.27 mg g^-1 at 25 °C. The thermodynamic findings have revealed MO adsorption has occurred spontaneously with an endothermic process. The Levenberg-Marquardt backpropagation algorithm has been implemented to train the ANN model, which has used the activation functions of tansig and purelin functions at the hidden and output layers, respectively. An optimum ANN model with high-performance metrics (coefficient of determination, R² = 0.9995; mean squared error, MSE = 0.0008) composed of three hidden layers with 5 neurons in each layer was constructed to forecast MO adsorption. The findings have shown that experimental results are consistent with ANN-based data, implying that the suggested ANN model may be used to forecast cationic dye adsorption.

Ag nanoparticles immobilized sulfonated polyethersulfone/polyethersulfone electrospun nanofiber membrane for the removal of heavy metals

In this work, Eucommia ulmoides leaf extract (EUOLstabilized silver nanoparticles (EUOL@AgNPs) incorporated sulfonated polyether sulfone (SPES)/polyethersulfone (PES) electrospun nanofiber membranes (SP ENMs) were prepared by electrospinning, and they were studied for the removal of lead (Pb(II)) and cadmium (Cd(II)) ions from aqueous solutions. The SP ENMs with various EUOL@AgNPs loadings were characterized by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscope, thermo-gravimetric analysis (TGA), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and contact angle (CA) measurements. The adsorption studies showed that the adsorption of Cd(II) and Pb(II) was rapid, achieved equilibrium within 40 min and 60 min, respectively and fitted with non-linear pseudo-second-order (PSO) kinetics model. For Cd(II) and Pb(II), the Freundlich model described the adsorption isotherm better than the Langmuir isotherm model. The maximum adsorption capacity for Cd(II) and Pb(II) was 625 and 370.37 mg g⁻¹ respectively at neutral pH. Coexisting anions of fluoride, chloride, and nitrate had a negligible influence on Cd(II) removal than the Pb(II). On the other hand, the presence of silicate and phosphate considerably affected Cd(II) and Pb(II) adsorption. The recyclability, regeneration, and reusability of the fabricated EUOL@AgNPs-SP ENMs were studied and they retained their high adsorption capacity up to five cycles. The DFT measurements revealed that SP-5 ENMs exhibited the highest adsorption selectivity for Cd(II) and the measured binding energies for Cd(II), Pb(II), are 219.35 and 206.26 kcal mol⁻¹, respectively. The developed ENM adsorbent may find application for the removal of heavy metals from water.

Congo red dye removal from aqueous environment by cationic surfactant modified-biomass derived carbon: Equilibrium, kinetic, and thermodynamic modeling, and forecasting via artificial neural network approach

Herein, it was aimed to optimize, model, and forecast the biosorption of Congo Red onto biomass-derived biosorbent. Therefore, the waste-orange-peels were processed to fabricate biomass-derived carbon, which was activated by ZnCl₂ and modified with cetyltrimethylammonium bromide. The physicochemical properties of the biosorbents were explored by scanning electron microscopy and N₂ adsorption/desorption isotherms. The effects of pH, initial dye concentration, temperature, and contact duration on the biosorption capacity were investigated and optimized by batch experimental process, followed by the kinetics, equilibrium, and thermodynamics of biosorption were modeled. Furthermore, various artificial neural network (ANN) architectures were applied to experimental data to optimize the ANN model. The kinetic modeling of the biosorption offered that biosorption was in accordance both with the pseudo-second-order and saturation-type kinetic model, and the monolayer biosorption capacity was calculated as 666.67 mg g^-1 at 25 °C according to Langmuir isotherm model. According to equilibrium modeling, the Freundlich isotherm model was better fitted to the experimental data than the Langmuir isotherm model. Moreover, the thermodynamic modeling revealed biosorption took place spontaneously as an exothermic process. The findings revealed that the best ANN architecture trained with trainlm as the backpropagation algorithm, with tansig-purelin transfer functions, and 14 neurons in the single hidden layer with the highest coefficient of determination (R² = 0.9996) and the lowest mean-squared-error (MSE = 0.0002). The well-agreement between the experimental and ANN-forecasted data demonstrated that the optimized ANN model can predict the behavior of the anionic dye biosorption onto biomass-derived modified carbon materials under various operation conditions.

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

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

Application of neural network in metal adsorption using biomaterials (BMs): a review

ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.