Phenol adsorption on scoria stone as adsorbent - Application of response surface method and artificial neural networks

π-π conjugated PDI supramolecular regulating the photoluminescence of imine-COFs for sensitive smartphone visual detection of levofloxacin

As the contamination and enrichment in food chain of levofloxacin (LV) antibiotics have caused a significant threat to life safety, the instant detection of LV has become an urgent need. Here, a PDI-functionalized imine-based covalent organic framework (PDI-COF300) was prepared by the electrostatic self-assembly method as fluorescent probe for smartphone visual detection of LV, which exhibited excellent fluorescence quantum yield (82.68%), greater stability, high sensitivity with detection limit of 0.303 μM. Based on the results of molecular docking and Stern-Volmer equation, the LV detection by PDI-COF300 was mainly a static quenching process through π-π stacked hydrophobic interactions and fluorescence resonance energy transfer. Besides, PDI-COF300 was applied to LV detection in environmental medium and milk samples with recoveries from 85.56% to 108.34% and relative standard deviations <2.70%. This work also provided a new general strategy for using PDI-COF in smartphone devices and fluorescent papers for LV fluorescence detection and microanalysis.

Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks

Numerous harmful phenolic contaminants are discharged into water that pose a serious threat to environment where two of the most important purification methodologies for the mitigation of phenolic contaminants are adsorption and photocatalysis. Besides cost, each process has drawbacks in terms of productivity, environmental impact, sludge creation, and the development of harmful by-products. To overcome these limitations, the modeling and optimization of water treatment methods is required. Artificial Intelligence (AI) is employed for the interpretation of treatment-based processes due to powerful learning, simplicity, high estimation accuracy, effectiveness, and improvement of process efficiency where artificial neural networks (ANNs) are most frequently employed for predicting and analyzing the efficiency of processes applied for the mitigation of these phenolic contaminants from water. ANNs are superior to conventional linear regression models because the latter are incapable of dealing with non-linear systems. ANNs can also reduce the operational cost of treating phenol-contaminated water. A correlation coefficient of >0.99 can be achieved using ANN with enhanced phenol mitigation percentage accuracy generally ranging from 80 % to 99.99 %. Using ANN optimization, the maximum phenol mitigation efficiencies achieved were 99.99 % for phenol, 99.93 % for bisphenol A, 99.6 % for nonylphenol, 97.1 % for 2-nitrophenol, 96.6 % for 4-chlorophenol and 90 % for 2,6-dichlorophenol. In numerous ANN models, Levenberg-Marquardt backpropagation algorithm for training was employed using MATLAB software. This study overviews their employment and application for optimization and modeling of removal processes and explicitly discusses the important input and output parameters necessary for better performance of the system. The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. Furthermore, several challenges and future prospects have also been discussed.

Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

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.

Biomass - metal oxide nano composite for the decontamination of phenol from polluted environment - parametric, kinetics and isotherm studies

The contamination of aqueous environment by phenol poses a major threat due to its hyper toxic effects and removal of phenol is challenging due to its hydrophilic properties. This research study examines the surface encapsulation of iron oxide (IO) with bio-derived carbon-based date palm (DP) to make date palm-iron oxide (DP-IO) nanocomposite to potentially remediate phenol in aqueous environment. Phenol removal percentage is predominantly influenced by environmental factors, namely pH, nano sorbent loading, temperature, agitation speed, and initial phenol concentration. Under optimum conditions of 30 °C and pH 7.8, 80.30% of phenol was removed using a 0.75 g/L sorbent load with 100 mg/L initial phenol concentration. Langmuir isotherm fitted well (R2 > 0.997), supporting single-layer phenol attachment with maximum bio-sorption capacity of 72.46 mg/g. A pseudo-2nd-order (PSO) kinetic model is identified to be the most appropriate for the DP-IO sorption experiment (R2>0.999). Scanning electron microscopic images, X-ray diffraction observations, FT-IR plots, and thermogravimetric analysis have been used to characterize. The removal mechanism involves unimolecular layer and chemisorption is identified as a rate determining step. The reuse potential proved that the synthesized nanocomposite as a sustainable solution for phenolic wastewater treatment.

Continuous treatment of highly concentrated tannery wastewater using novel porous composite beads: Central composite design optimization study

This present study depicts the successful employment of fixed-bed column for total chromium removal from tannery wastewater in dynamic mode using sodium alginate-powdered marble beads (SA-Marble) as adsorbent. The SA-Marble composite beads prepared were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and Brunauer, Emmett and Teller (BET) method. The adsorption process performance of this bio-sorbent was examined in batches and columns for real effluent (tannery wastewater). After 90 min, the total chromium removal efficiency could be kept above 90% in the batch experiment. The adsorption kinetics fit better with the pseudo-second-order model, indicating the chemisorption process and the adsorption capacity of about 67.74 mg g-1 at 293 K (C0 = 7100 mg L-1) was obtained. Additionally, dynamic experiments indicate that the total chromium removal efficiency could be maintained above 90% after 120 min at 293 K and 60 min at 318 and 333 K; it's an endothermic but rapid process. The effects of two adsorption variables (Temperature and time) were investigated using central composite design (CCD), which is a subset of response surface methodology (total Cr, COD, sulfate, and total phosphorus percentage removal). This work paves a new avenue for synthesizing SA-Marble composite beads and provides an adsorption efficiency of total chromium removal from tannery wastewater.

Graphical abstract

Adsorption of phenol using adsorbent derived from Saccharum officinarum biomass: optimization, isotherms, kinetics, and thermodynamic study

The present research shows the application of Taguchi's design of experiment approach to optimize the process parameters for the removal of phenol onto surface of Saccharum officinarum biomass activated carbon (SBAC) from an aqueous solution to maximize adsorption capacity of SBAC. The effect of adsorption parameters viz. adsorbent dose (m), temperature (T), initial concentration (C0) and mixing time (t) on response characteristics i.e., adsorption capacity (qt) has been studied at three levels by using L9 orthogonal array (OA) which further analyzed by variance analysis (ANOVA) for adsorption data and signal/noise (S/N) ratio data by using 'larger the better' characteristics. Using ANOVA, the optimum parameters are found to be m = 2 g/L, C0 = 150 mg/L, T = 313 K and t = 90 min, resulting in a maximum adsorption capacity of 64.59 mg/g. Adopting ANOVA, the percentage contribution of each process parameter in descending order of sequence is adsorbent dose 59.97% > initial phenol concentration 31.70% > contact time 4.28% > temperature 4.04%. The phenol adsorption onto SBAC was best fitted with the pseudo-second-order kinetic model and follows the Radke-Prausnitz isotherm model. Thermodynamic parameters suggested a spontaneous, exothermic nature and the adsorption process approaches physisorption followed by chemisorption. Hence the application of Taguchi orthogonal array design is a cost-effective and time-efficient approach for carrying out experiments and optimizing procedures for adsorption of phenol and improve the adsorption capacity of SBAC.

Modeling of adsorptive removal of azithromycin from aquatic media by CoFe2O4/NiO anchored microalgae-derived nitrogen-doped porous activated carbon adsorbent and colorimetric quantifying of azithromycin in pharmaceutical products

Herein, it was aimed to optimize the removal process of Azithromycin (Azi) from the aquatic environment via CoFe2O4/NiO nanoparticles anchored onto the microalgae-derived nitrogen-doped porous activated carbon (N-PAC), besides developing a colorimetric method for the swift monitoring of Azi in pharmaceutical products. In this study, the Spirulina platensis (Sp) was used as a biomass resource for fabricating CoFe2O4/NiO@N-PAC adsorbent. The pores of N-PAC mainly entail mesoporous structures with a mean pore diameter of 21.546 nm and total cavity volume (Vtotal) of 0.033578 cm3. g-1. The adsorption studies offered that 98.5% of Azi in aqueous media could remove by CoFe2O4/NiO@N-PAC. For the cyclic stability analysis, the adsorbent was separated magnetically and assessed at the end of five adsorption-desorption cycles with a negligible decrease in adsorption. The kinetic modeling revealed that the adsorption of Azi onto the CoFe2O4/NiO@N-PAC was well-fitted to the second-order reaction kinetics, and the highest adsorption capacity was found as 2000 mg. g-1 at 25 °C based on the Langmuir adsorption isotherm model at 0.8 g. L-1 adsorbent concentration. The Freundlich isotherm model had the best agreement with the experimental data. Thermodynamic modeling indicated the spontaneous and exothermic nature of the adsorption process. Moreover, the effects of pH, temperature, and operating time were also optimized in the colorimetric Azi detection. The blue ion-pair complexes between Azi and Coomassie Brilliant Blue G-250 (CBBG-250) reagent followed Beer's law at wavelengths of 640 nm in the concentration range of 1.0 μM to 1.0 mM with a 0.94 μM limit of detection (LOD). In addition, the selectivity of Azi determination was verified in presence of various species. Furthermore, the applicability of CBBG-250 dye for quantifying Azi was evaluated in Azi capsules as real samples, which revealed the acceptable recovery percentage (98.72-101.27%). This work paves the way for engineering advanced nanomaterials for the removal and monitoring of Azi and assures the sustainability of environmental protection and public health.

4-Chlorophenol adsorption from water solutions by activated carbon functionalized with amine groups: response surface method and artificial neural networks

4-Chlorophenol pollution is a significant environmental concern. In this study, powdered activated carbon modified with amine groups is synthesized and investigated its efficiency in removing 4-chlorophenols from aqueous environments. Response surface methodology (RSM) and central composite design (CCD) were used to investigate the effect of different parameters, including pH, contact time, adsorbent dosage, and initial 4-chlorophenol concentration, on 4-chlorophenol removal efficiency. The RSM-CCD approach was implemented in R software to design and analyze the experiments. The statistical analysis of variance (ANOVA) was used to describe the roles of effecting parameters on response. Isotherm and kinetic studies were done with three Langmuir, Freundlich, and Temkin isotherm models and four pseudo-first-order, pseudo-second-order, Elovich, and intraparticle kinetic models in both linear and non-linear forms. The synthesized adsorbent was characterized using X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) analyses. The results showed that the synthesized modified activated carbon had a maximum adsorption capacity of 316.1 mg/g and exhibited high efficiency in removing 4-chlorophenols. The optimal conditions for the highest removal efficiency were an adsorbent dosage of 0.55 g/L, contact time of 35 min, initial concentration of 4-chlorophenol of 110 mg/L, and pH of 3. The thermodynamic study indicated that the adsorption process was exothermic and spontaneous. The synthesized adsorbent also showed excellent reusability even after five successive cycles. These findings demonstrate the potential of modified activated carbon as an effective method for removing 4-chlorophenols from aqueous environments and contributing to developing sustainable and efficient water treatment technologies.

Acidic modification of natural stone for Remazol Black B dye adsorption from aqueous solution- central composite design (CCD) and response surface methodology (RSM)

This study investigated the adsorption capacity of Remazol Black B (RBB) from aqueous solutions using a pumice stone as a cheap, high-frequent, and available adsorbent. The raw pumice was modified using five acids: Acetic, Sulfuric, Phosphoric, Nitric, and Hydrochloric acid. Fourier transform infrared spectrograph (FTIR), x-ray fluorescence (XRF), and scanning electron microscopy (SEM) were used to analyze the morphological and chemical properties of raw and modified adsorbents. The adsorption capacity equilibrium was investigated using the Langmuir, Freundlich, Temkin, and Dubinin - Radushkevich isotherms. The results indicated that the data are well-fitted with Langmuir isotherm. The maximum adsorption capacity was observed when pumice modified with H₂SO₄ (q_m = 10.00 mg/g) was used, and the RBB removal efficiency was higher than that for raw pumice (q_m = 5.26 mg/g). Also, the results were best fitted with pseudo-second-order kinetic. The experiments indicated that increasing the RBB concentration reduces the efficiency of adsorbents while increasing the contact time and adsorbent doses improved the RBB removal efficiency. Accordingly, it can be concluded that pumice stone modified with various acids can be considered a cheap adsorbent with high efficiency in removing RBB from industry effluent.

Modeling of methylene blue removal on Fe3O4 modified activated carbon with artificial neural network (ANN)

In this study, AC/Fe3O4 adsorbent was first synthesized by modifying activated carbon with Fe3O4. The structure of the adsorbent was then characterized using analysis techniques specific surface area (BET), Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDX), and Fourier Transform Infrared Spectroscopy (FTIR). Equilibrium, thermodynamic and kinetic studies were carried out on the removal of methylene blue (MB) dyestuff from aqueous solutions AC/Fe3O4 adsorbent. The Langmuir maximum adsorption capacity of AC/Fe3O4 was 312.8 mg g-1, and the best fitness was observed with the pseudo-second-order kinetics model, with an endothermic adsorption process. In the final stage of the study, the adsorption process of MB on AC/Fe3O4 was modeled using artificial neural network modeling (ANN). Considering the smallest mean square error (MSE), The backpropagation neural network was configured as a three-layer ANN with a tangent sigmoid transfer function (Tansig) at the hidden layer with 10 neurons, linear transfer function (Purelin) the at output layer and Levenberg-Marquardt backpropagation training algorithm (LMA). Input parameters included initial solution pH (2.0-9.0), amount (0.05-0.5 g L-1), temperature (298-318 K), contact time (5-180 min), and concentration (50-500 mg L-1). The effect of each parameter on the removal and adsorption percentages was evaluated. The performance of the ANN model was adjusted by changing parameters such as the number of neurons in the middle layer, the number of inputs, and the learning coefficient. The mean absolute percentage error (MAPE) was used to evaluate the model's accuracy for the removal and adsorption percentage output parameters. The absolute fraction of variance (R2) values were 99.83, 99.36, and 98.26% for the dyestuff training, validation, and test sets, respectively.

Selective Removal of Boron from Aqueous Solutions Using ECH@NGM Aerogels with Excellent Hydrophilic and Mechanical Properties: Performance and Response Surface Methodology Analysis

The remediation of environmental boron contamination has received extensive research attention. The adsorbent ECH@NGM aerogel with high hydrophilic and mechanical properties was synthesized to remove boron. The ECH@NGM aerogel had a high adsorption capacity of 81.11 mg/g, which was 14.50% higher than that of commercial boron-selective resin Amberlite IRA743. The Freundlich model and pseudo-second-order model described the adsorption behavior well. In addition, the response surface methodology (RSM) could predict the experimental outcomes and optimize the reaction conditions, and X-ray photoelectron spectroscopy (XPS) and control tests were utilized to investigate probable adsorption mechanisms. These data showed that the B ← N coordination bond was the primary adsorption force. The adsorbent had good resistance to interference from coexisting salts, high reusability, good adsorption performance even after five reuse cycles, and a high desorption rate in a relatively short time. The adsorption performance in real brines could be maintained at 80%. Therefore, this work not only provided ECH@NGM aerogels for the removal of boron from brine but also elucidated the main adsorption processes between N-containing adsorbents and boron, facilitating the design of future adsorbents for boron removal.

Immobilization of lead(Ⅱ) and zinc(Ⅱ) onto glomalin-related soil protein (GRSP): Adsorption properties and interaction mechanisms

Glomalin-related soil protein (GRSP), a microbial product that can be used as a bioflocculant, is critical to metal sequestration in the ecosystem. However, the relationship between GRSP and heavy metal has not been well explored. In this study, the adsorption behaviors and mechanisms of Pb(II) and Zn(II) ions on GRSP were investigated. Results reveal that the Pb(II) and Zn(II) adsorption closely conform to the pseudo second-order model, which indicates that the chemisorption of GRSP occurred after intra-particle diffusion. The adsorption process is influenced by the degree of pollution, pH value, GRSP content in the environment. In addition, scanning electron microscopy coupled with microanalysis (SEM-EDX) reveals that the surface structure of GRSP is irregularly blocky or flaky and metal ions are uniformly distributed on the surface of GRSP. Fourier transform infrared (FT-IR), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) analysis show that the carboxyl and nitro groups on GRSP act as ligands to form complexes with two divalent metal ions. The interaction between GRSP and the metals is mainly surface complexation. This research further reveals the dynamic response of its structural components when GRSP sequestrates heavy metals in mangrove sediment and aqueous ecosystems, demonstrating a new perspective for the transport and transformation of heavy metals onto GRSP.

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.

Hemp fibers modified with graphite oxide as green and efficient solution for water remediation: Application to methylene blue

In this paper, the use of hemp fibers modified with graphite oxide for the removal of methylene blue (MB) from aqueous solutions was investigated. Parameters such as contact time, pH, temperature, initial concentration of dye and ionic strength were varied and their effects on the adsorption recovery were evaluated. The adsorption process attained the equilibrium within 30 min while the adsorption capacity was found to increase with increasing contact time. The experimental data were fitted through a pseudo-second order model. Maximum adsorption capacity slightly increases with temperature changing from 54 mg/g to 58 mg/g at pH = 7.5, from 37 mg/g to 45 mg/g at pH = 3 and from 44 mg/g to 49 mg/g at pH = 12, by increasing the temperature from 20 °C to 80 °C indicating that the process is slightly endothermic (ΔH = 3.43 kJ/mol). The thermodynamic parameters were even calculated demonstrating that the process is spontaneous (ΔG ≈ -4.4 J/mol K and ΔS = 3.16 J/mol K)). Finally, a mathematical algorithm was applied to forecast the response surface model. A second order model was chosen to fit the experimental data and the statistical effect of the process parameters were estimated. A numerical optimization was even performed to individuate the optimal set of process parameters (pH = 9.25, T = 53.8 °C and C₀ = 13.2 mg/L) which maximizes the removal capacity. A possible adsorption mechanism was even presented. So, it was proved the efficiency of the adsorption of a novel, inexpensive and sustainable composite material obtained from agro-waste resources by performing reusability cycles.

Support vector regression-based model for phenol adsorption in rotating packed bed adsorber

The excessive strength of phenol present in industrial wastewater is a major issue of concern to be looked upon. Among the pollutant removal techniques, a novel robust device, the rotating packed bed (RPB) adsorber, offers efficient adsorption of phenol due to its ability to magnify the mass transfer rate. In the present study, support vector regression (SVR) has been applied to predict adsorption of phenol on activated carbon in RPB by taking into account the independent parameters, namely, spray density, gravity factor, concentration, and contact time. The experimental data set of phenol adsorption sample has been randomized and normalized prior to constructing the models. The predictive ability of the SVR model has been compared with other data-driven models like artificial neural network (ANN) and multiple regression (MR) models. Both the SVR-based model and the ANN model have almost similar prediction efficacy; however, the ANN model was found to predict the outputs slightly better. The coefficient of determination (R²) and root mean square error (RMSE) values of test data set for the MR RPB adsorption model were found to be 0.934 and 0.149, while for the SVR and ANN-based models, these values were 0.996 and 0.045 and 0.998 and 0.027, respectively. Thus, it was concluded that the soft computing SVR and ANN models possessed tremendous potential to predict the adsorption process of RPB with remarkable accuracy and were greatly generalized.

Efficient anaerobic digestion of a mild wet air pretreated molasses ethanol distillery stillage: A comparative approach

The effect of a mild, wet air pretreatment and the subsequent anaerobic digestion (AD) was examined on the recovery of a complex and toxic molasses ethanol distillery stillage. The biogas yield and organics removal due to pretreatment were compared with the raw stillage AD. The application of a scoria support in this industrial residue AD process stability was also assessed. Consequently, a statistically significant cumulative specific methane recovery difference (p-value = 0.000) with an almost complete biological oxygen demand (BOD) removal and a significant chemical oxygen demand (COD) reduction, which were 100% and 92% respectively were achieved. Additionally, the biogas recovery rate was hastened due to pretreatment. The application of scoria, whose property has been instrumentally inspected, has helped stabilize the pH in the AD systems. In a comparative approach, this study suggests the energy benefit and an ecofriendly discharge of stillage by the ethanol industry towards sustainability.

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).

Response surface methodology and artificial neural network for remediation of acid orange 7 using TiO2-P25: optimization and modeling approach

The primary responsibility for continuously discharging toxic organic pollutants into water bodies and open environments is the increase in industrial and agricultural activities. Developing economical and suitable methods to continuously remove organic pollutants from wastewater is highly essential. The aim of the present research was to apply response surface methodology (RSM) and artificial neural networks (ANNs) for optimization and modeling of photocatalytic degradation of acid orange 7 (AO7) by commercial TiO2-P25 nanoparticles (TNPs). Dose of TNPs, pH, and AO7 concentration were selected as investigated parameters. RSM results reveal the reflective rate of AO7 removal of ~ 94.974% was obtained at pH 7.599, TNP dose of 0.748 g/L, and AO7 concentration of 28.483 mg/L. The resulting quadratic model is satisfactory with the highest coefficient of determination (R2) between the predicted and experimental data (R2 = 0.98 and adjusted R2 = 0.954). On the other hand, ANNs were successfully employed for modeling of AO7 degradation process. The proposed ANN model was absolutely fitted with experimental results producing the highest R2. Furthermore, root mean square error (RMSE), mean average deviation (MAD), absolute average relative error (AARE), and mean square error (MSE) were examined more to compare the predictive capabilities of ANN and RSM models. The experimental data was well fitted into pseudo-first-order and pseudo-second-order kinetics with more accuracy. Thermodynamic parameters, namely enthalpy, entropy, Gibbs' free energy, and activation energy, were also evaluated to suggest the nature of the degradation process. The increase of temperature was analyzed to be more suitable for the fast removal of AO7 over TNPs. Graphical abstract.

Fractal-Like Kinetics of Adsorption Applied to the Solid/Solution Interface

In this paper, the fractal-like multiexponential (f-mexp) equation was modified by introducing the fractional fractal exponent to each stage of the adsorption process. The new equation was used for the analysis of kinetic adsorption of copper onto treated attapulgite. The modeling results show that the modified f-mexp equation fits properly the kinetic data in comparison with the classical and fractal-like kinetic models tested. The effect of varying the initial concentration of the adsorbate on the kinetic parameters was analyzed. Artificial neural networks were applied for the prediction of adsorption efficiency. Outcomes indicate that the multilayer perceptron neural network can predict the removal of copper from aqueous solutions more accurately under different experimental conditions than the single-layer feedforward neural network. Single-site and multisite occupancy adsorption models were used for the analysis of experimental adsorption equilibrium data of copper onto treated attapulgite. The modeling results show that there is no multisite occupancy effect and that the equilibrium data fit well the Langmuir-Freundlich isotherm.

Kinetic, isotherm and mechanism investigations of the removal of phenols from water by raw and calcined clays

In this work, the phenols removal of phenol from water by raw clay (RCG) and calcined one at 1000 °C (CCG) of Goulmima city (Morocco) was investigated. The kinetics and isotherms experiments were also studied at pH = 4. The results indicated that the phenol adsorption reached equilibrium within 3 h, and the removal of phenol was enhanced at the same temperature by CCG (2.932 mg/g) adsorbent, compared to RCG (1.640 mg/g) due to the removal of organic matter by heat treatment, and an increase in adsorption temperature, indicating the endothermic process. The adsorbents were characterized by means of X-ray fluorescence, FTIR, XRD, B.E.T, and TGA/DTA analysis and showed that the clay consists essentially of silica and alumina. The experimental data were examined by using linear and nonlinear forms of the kinetics and the isotherms models. Based on the errors of the calculated values of R² (Coefficient of determination), χ² (Chi-square) and standard deviation (Δq (%)), it was found that the nonlinear forms of second-order kinetic model and Freundlich and Redlich-Peterson (R-P) isotherm models are best fit the experimental data for both adsorbents. However, the enthalpy ΔH° is less than 20 kJ/mol and the free energy ΔG° has a negative value, which shows that the adsorption is done physically and spontaneously on heterogeneous sites. The interest of this study is the use of FTIR and XRD to determine the effect of calcination on the phenol adsorption mechanism. However, the analysis of both adsorbents, before and after adsorption of phenol, shows that the adsorption mechanism of phenol is provided by the hydrogen bonding of the water molecules.

Dataset for adsorptive removal of tetracycline (TC) from aqueous solution via natural light weight expanded clay aggregate (LECA) and LECA coated with manganese oxide nanoparticles in the presence of H2O2

In this data article, natural (NL) and manganese oxide-modified LECA (MML) adsorbents were applied for adsorptive removal of Tetracycline (TC) from aqueous solution. The used adsorbents was characterized using fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD) and X-ray fluorescence spectroscopy (XRF). The chemical analysis of XRF data revealed increased chemical composition of Mn as MnO to 8.96 wt%. The SEM patterns were illustrated the extent of surface and enhanced porosity in MML with Mn. In optimum operational conditions, maximum removal percentage of TC was achieved at 51.5 and 99.4% using NL and MML, respectively. The maximum adsorption capacities obtained from Langmuir modeling were 6.89 and 9.24 for NL and MML, respectively. The modeling of the adsorption kinetics revealed that TC adsorption by both NL and MML adsorbents was best-fitted with a pseudo-first-order model (R² = 0.978). The isotherm studies of TC adsorption by MML showed that the Freundlich isotherm was the most appropriate model, with a higher coefficient of determination. The obtained data was illustrated that high competitive capacity of chloride and hardness ions compared with other ions against TC adsorption.