Removal of sarafloxacin from aqueous solution by a magnetized metal-organic framework; Artificial neural network modeling

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.

Magnetite Metal-Organic Frameworks: Applications in Environmental Remediation of Heavy Metals, Organic Contaminants, and Other Pollutants

Due to the increasing environmental pollution caused by human activities, environmental remediation has become an important subject for humans and environmental safety. The quest for beneficial pathways to remove organic and inorganic contaminants has been the theme of considerable investigations in the past decade. The easy and quick separation made magnetic solid-phase extraction (MSPE) a popular method for the removal of different pollutants from the environment. Metal-organic frameworks (MOFs) are a class of porous materials best known for their ultrahigh porosity. Moreover, these materials can be easily modified with useful ligands and form various composites with varying characteristics, thus rendering them an ideal candidate as adsorbing agents for MSPE. Herein, research on MSPE, encompassing MOFs as sorbents and Fe₃O₄ as a magnetic component, is surveyed for environmental applications. Initially, assorted pollutants and their threats to human and environmental safety are introduced with a brief introduction to MOFs and MSPE. Subsequently, the deployment of magnetic MOFs (MMOFs) as sorbents for the removal of various organic and inorganic pollutants from the environment is deliberated, encompassing the outlooks and perspectives of this field.

Sustainable adsorptive removal of antibiotics from aqueous streams using Fe3O4-functionalized MIL101(Fe) chitosan composite beads

In this study, we synthesized recyclable Fe₃O₄-functionalized MIL101(Fe) chitosan composite beads for the removal of tetracycline (TC), doxycycline (DC) and ciprofloxacin (CFX) antibiotics from aqueous streams. More than 99% removal efficiency for each antibiotic was achieved at optimum pH, dosage, concentration and contact time. Langmuir adsorption isotherms and pseudo-second-ord er kinetic model were suitable with correlation coefficient values close to 1 for all the antibiotics. Adsorption capacities of 45.33, 33.20 and 31.30 mg g^-1 for TC, DC and CFX, respectively, were reported by the synthesized Fe₃O₄-functionalized MIL101(Fe) chitosan composite beads. The Fe₃O₄-functionalized MIL101(Fe) chitosan composite beads were also tested for their regeneration ability, and a remarkable regeneration ability over up to 5 cycles was observed. The adsorption of TC, DC and CFX on the surface of Fe₃O₄-functionalized MIL101(Fe) chitosan composite beads was governed by the π-π interaction, H-bonding and electrostatic interaction between the antibiotics and adsorbent due to protonation, deprotonation and cation exchange in the aqueous solution. These results showed a good prospect for applying the reported beads towards removing antibiotics from pharmaceutical industry wastewater.

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.

Metal-organic framework MIL-100(Fe) for dye removal in aqueous solutions: Prediction by artificial neural network and response surface methodology modeling

In this study, a metal organic framework MIL-100(Fe) was synthesized for rhodamine B (RB) removal from aqueous solutions. An experimental design was conducted using a central composite design (CCD) method to obtain the RB adsorption data (n = 30) from batch experiments. In the CCD approach, solution pH, adsorbent dose, and initial RB concentration were included as input variables, whereas RB removal rate was employed as an output variable. Response surface methodology (RSM) and artificial neural network (ANN) modeling were performed using the adsorption data. In RSM modeling, the cubic regression model was developed, which was adequate to describe the RB adsorption according to analysis of variance. Meanwhile, the ANN model with the topology of 3:8:1 (three input variables, eight neurons in one hidden layer, and one output variable) was developed. In order to further compare the performance between the RSM and ANN models, additional adsorption data (n = 8) were produced under experimental conditions, which were randomly selected in the range of the input variables employed in the CCD matrix. The analysis showed that the ANN model (R² = 0.821) had better predictability than the RSM model (R² = 0.733) for the RB removal rate. Based on the ANN model, the optimum RB removal rate (>99.9%) was predicted at pH 5.3, adsorbent dose 2.0 g L^-1, and initial RB concentration 73 mg L^-1. In addition, pH was determined to be the most important input variable affecting the RB removal rate. This study demonstrated that the ANN model could be successfully employed to model and optimize RB adsorption to the MIL-100(Fe).

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.

A Comparison between Different Agro-Wastes and Carbon Nanotubes for Removal of Sarafloxacin from Wastewater: Kinetics and Equilibrium Studies

In the current study, eco-structured and efficient removal of the veterinary fluoroquinolone antibiotic sarafloxacin (SARA) from wastewater has been explored. The adsorptive power of four agro-wastes (AWs) derived from pistachio nutshells (PNS) and Aloe vera leaves (AV) as well as the multi-walled carbon nanotubes (MWCNTs) has been assessed. Adsorbent derived from raw pistachio nutshells (RPNS) was the most efficient among the four tested AWs (%removal '%R' = 82.39%), while MWCNTs showed the best adsorptive power amongst the five adsorbents (%R = 96.20%). Plackett-Burman design (PBD) was used to optimize the adsorption process. Two responses ('%R' and adsorption capacity 'qe') were optimized as a function of four variables (pH, adsorbent dose 'AD' (dose of RPNS and MWCNTs), adsorbate concentration [SARA] and contact time 'CT'). The effect of pH was similar for both RPNS and MWCNTs. Morphological and textural characterization of the tested adsorbents was carried out using FT-IR spectroscopy, SEM and BET analyses. Conversion of waste-derived materials into carbonaceous material was investigated by Raman spectroscopy. Equilibrium studies showed that Freundlich isotherm is the most suitable isotherm to describe the adsorption of SARA onto RPNS. Kinetics' investigation shows that the adsorption of SARA onto RPNS follows a pseudo-second order (PSO) model.