Modeling and optimization of heterogeneous photo-Fenton process with response surface methodology and artificial neural networks

Removal of V(V) from a Mixed Solution Containing Vanadium and Chromium Using a Micropocrous Resin in a Column: Migration Regularity of the Mass Transfer Zone and Analysis of Dynamic Properties

In China, both vanadium(V) and chromium(VI) are present in wastewater resulting from vanadate precipitation (AVP wastewater) and from leaching vanadium-chromium reduction slag. Addressing environmental preservation and the comprehensive utilization of metal resources necessitates the extraction and separation of V(V) and Cr(VI) from these mixed solutions. However, their separation is complicated by very similar physicochemical properties. This study establishes a method for the dynamic selective adsorption of V(V) from such mixtures. It evaluates the impact of various operating conditions in columns on dynamic adsorption behavior. This study examines the migration patterns of the mass transfer zone (MTZ) and forecasts its effective adsorption capacity through multivariate polynomial regression and a neural network (NN) model. The NN model's outcomes are notably more precise. Its analysis reveals that C 0 is the most critical factor, with Q and H following in importance. Furthermore, the dynamic properties were analyzed using two established models, Thomas and Klinkenberg, revealing that both intraparticle and liquid film diffusion influence the rates of exchange adsorption, with intraparticle diffusion being the more significant factor. Using 3 wt % sodium hydroxide as the eluent to elute V(V)-loaded resin at a flow rate of 4 mL/min resulted in a chromium concentration of less than 3 mg/L in the V(V) eluate, indicating high vanadium-chromium separation efficiency in this method. These findings offer theoretical insights and economic analysis data that are crucial for optimizing column operation processes.

Thermally activated persulfate (TAP)-enhanced tris(2-chloroethyl) phosphate removal in real-world waters based on a response-surface approach as well as toxicological evaluation on its degradation products

As a typical organophosphorus flame retardant, tris(2-chloroethyl) phosphate (TCEP) is refractory in aqueous environment. The application of TAP is a promising method for removing pollutants. Herein, the removal of TCEP using TAP was rigorously investigated, and the effects of some key variables were optimized by the one-factor-at-a-time approach. To further evaluate the interactions among variables, the response surface methodology (RSM) based on central composite design was employed. Under optimized conditions (pH 5, [PS]0: [TCEP]0 = 500:1), the maximum removal efficiency (RE) of TCEP reached up to 90.6%. In real-world waters, the RE of TCEP spanned the range of 56%- 65% in river water, pond water, lake water and sanitary sewage. The low-concentration Cl- (0.1 mM) promoted TCEP degradation, but the contrary case occurred when the high-concentration Cl-, NO3-, CO32-, HCO3-, HPO42-, H2PO4-, NH4+ and humic acid were present owing to their prominently quenching effects on SO4•-. Both EPR and scavenger experiments revealed that the main radicals in the TAP system were SO4•- and •OH, in which SO4•- played the most crucial role in TCEP degradation. GC-MS/MS analysis disclosed that two degradation products appeared, sourcing from the replacement, oxidation, hydroxylation and water-molecule elimination reactions. The other two products were inferred from the comprehensive literature. As for acute toxicity to fish, daphnid and green algae, product A displayed the slightly higher toxicity, whereas other three products exhibited the declining toxicity as compared to their parent molecule. These findings offer a theoretical/practical reference for high-efficiency removal of TCEP and its ecotoxicological risk evaluation.

Muscovite clay for methylene blue removal: advanced optimization and Al-guided breakthroughs-an independent application from prior antibiotic removal investigation

This study evaluates the efficacy of muscovite mineral clay as an adsorbent for removing Methylene Blue (MB) from water-based solutions. The research examined the impact of initial MB concentration, adsorbent mass, and time on the MB removal process. Two modeling techniques, namely Box-Behnken design with response surface methodology (BBD-RSM) and Artificial Neural Network (ANN), were employed to accurately predict the MB removal efficiency. The RSM and ANN models yielded satisfactory results in estimating MB removal efficiency. To further enhance the optimization process, conventional and techno-economic methods were implemented. The conventional method aimed to maximize dye removal efficiency (R), while the techno-economic approach incorporated multiple objectives. The comparative analysis demonstrated that the techno-economic optimization method outperformed the conventional method. This study emphasizes the significance of considering multiple objectives and integrating techno-economic factors in optimizing clay adsorption processes. The successful application of the techno-economic optimization approach highlights its potential as a robust optimization method, particularly in the field of wastewater treatment. The findings provide valuable insights for optimizing adsorption and advancing environmental remediation practices.

Modeling and optimization study on degradation of organic contaminants using nZVI activated persulfate based on response surface methodology and artificial neural network: a case study of benzene as the model pollutant

Due to the complicated transport and reactive behavior of organic contamination in groundwater, the development of mathematical models to aid field remediation planning and implementation attracts increasing attentions. In this study, the approach coupling response surface methodology (RSM), artificial neural networks (ANN), and kinetic models was implemented to model the degradation effects of nano-zero-valent iron (nZVI) activated persulfate (PS) systems on benzene, a common organic pollutant in groundwater. The proposed model was applied to optimize the process parameters in order to help predict the effects of multiple factors on benzene degradation rate. Meanwhile, the chemical oxidation kinetics was developed based on batch experiments under the optimized reaction conditions to predict the temporal degradation of benzene. The results indicated that benzene (0.25 mmol) would be theoretically completely oxidized in 1.45 mM PS with the PS/nZVI molar ratio of 4:1 at pH 3.9°C and 21.9 C. The RSM model predicted well the effects of the four factors on benzene degradation rate (R2 = 0.948), and the ANN with a hidden layer structure of [8-8] performed better compared to the RSM (R2 = 0.980). In addition, the involved benzene degradation systems fit well with the Type-2 and Type-3 pseudo-second order (PSO) kinetic models with R2 > 0.999. It suggested that the proposed statistical and kinetic-based modeling approach is promising support for predicting the chemical oxidation performance of organic contaminants in groundwater under the influence of multiple factors.

An experimental design study of photocatalytic activity of the Z-scheme silver iodide/tungstate binary nano photocatalyst

A binary AgI/ Ag2WO4 photocatalyst was fabricated and characterized by SEM, XRD, UV-Vis DRS, and FT-IR. It was then used to photodegrade sodium ceftriaxone (CTX) in an aqueous solution. The band gap energies of 2.95, 2.78, and 2.62 eV were obtained by the Kubelka-Munk model for Ag2WO4, AgI, and AgI/Ag2WO4 catalysts. The samples have pHPZC values of 6.9, 4.2, and 6.6, respectively. The synergistic photocatalytic activity of the coupled system depended on the AgI:Ag2WO4 mole ratio and grinding time (optimums:mole ratio of 4:1 and time 30 min). The experimental design was used for optimizing the conditions and a quadratic model well-processed the data based on the model F value of 131.87 > F0.05,14,13 = 2.55 and LOF F value of 0.78 < F0.05,10,3 = 8.78. The optimized RSM run included the irradiation time of 85 min, 3.5 mg/L of CTX sample at pH 9, and a catalyst dose of 1.0 g/L. Under the optimized conditions, about 63% of CTX molecules were photodegraded. In the study of the scavenging agents, the direct Z-scheme mechanism accumulated electrons in the CB-AgI and the holes in the VB-Ag2WO4 level, as stronger reducing and oxidizing centers than the accumulated electrons and holes of the type (II) heterojunction mechanism. Compared to a CTX oxidation potential of about 0.06 V, the direct Z-scheme mechanism is more favorable to reduce or oxidize it.

Taguchi L16 (44) orthogonal array-based study and thermodynamics analysis for electro-Fenton process treatment of textile industrial dye

Reactive orange 16 (RO16) is the most widely used azo dye in Textile industry. Complex aromatic structures and resistivity to biological decay caused the dye pollutants incompletely treated by the conventional oxidative methods. The current study presents the electro-Fenton-based advanced oxidation treatment of RO16 dye and the process optimization by Taguchi-based design of experiment (DOE). Using a 500 mL volume lab-scale experimental setup, the process was first studied for the principal operational parameters (initial dye concentration (q); [H2O2]/[Fe+2] (R); current density (ρ); and temperature (T)) effect on decolourization (D R ) and COD removal (C R ). Then, by means of the L16 (44) orthogonal array (OA) formation, standard mean and signal-to-noise (S/N) ratio, the process was optimized for the response variables. The result showed the optimized result at q = 100 mg/L, R = 100, ρ = 8 mA/cm2, and T = 32 °C; with D R and C R as 90.023 and 84.344%, respectively. It was found that the current density affects the process most, followed by [H2O2]/[Fe+2] ratio, initial dye concentration, and temperature i.e., ρ > R > q > T. Also, with the analysis of variance (ANOVA), model equations for D R and C R were developed and its accuracy was verified for experimental results. At optimized conditions, the first order removal rate constants (k a ) were found from batch results. Additionally, the thermodynamic constants (ΔH e , ΔS e , and ΔG b ) were also calculated for the nature of heat-energy involved and temperature effect study on dye degradation. The results showed that the process was thermodynamically feasible, endothermic, and non-spontaneous with a lower energy barrier (E A  = 46.7 kJ mol−1).

A designed experiment for CdS-AgBr photocatalyst toward methylene blue

A boosted photocatalytic activity was observed for the CdS-AgBr nanocomposite in the degradation of methylene blue (MB). The experimental design method based on the response surface methodology (RSM) approach used to study the simultaneous interaction effects between the influencing variables. Analysis of variance (ANOVA) of the results confirmed a significant model for processing the data because an F value of 32.34 for the suggested model was higher than that of the critical value of F_{0.05, 14, 13} = 2.55 at 95% confidence interval. This analysis also showed a non-significant lack of fit (LOF) (as a measure of the randomness of the deviations around the obtained data) because the LOF F value of 8.27 was smaller than that of the critical value of F_{0.05, 10, 3} = 8.79. R² values near to unity were achieved (the multiple correlation coefficients R² (R² = 0.9627), adjusted R² (adj-R² = 0.9226), and predicted R² (pred-R² = 0.7423)). Six center points suggested by the model included the following conditions: pH, 6.1; C_MB, 3.5 mg/L; a dose of the catalyst, 0.68 g/L; and irradiation time, 40.5 min. During the center point runs, the degradation efficiencies were obtained in the range of 38 to 43%. The optimal run included pH, 9; catalyst dosage, 1 g/L; irradiation time, 60 min; and C_MB, 2 mg/L, and the best removal efficiency of 98% was achieved during these conditions.

The photocatalytic degradation kinetics of the anti-inflammatory drug ibuprofen in aqueous solution under UV/TiO2 system and neural networks modeling

In this study, the kinetic degradation of the anti-inflammatory drug Ibuprofen in aqueous solution by heterogeneous TiO2 photocatalytic was investigated. The data obtained were used for training an artificial neural network. Preliminary experiments of photolysis and adsorption were carried out to assess their contribution to the photocatalytic degradation. Both, direct photolysis and adsorption of Ibuprofen are very low-efficient processes (15,83% and 23,88%, respectively). The degradation efficiency was significantly elevated with the addition of TiO2 Catalyst (>94%). The photocatalytic degradation followed a pseudo-first-order reaction according to the L-H model. The hydroxyl radicals and photo-hole (h+‏) were found to contribute to the Ibuprofen removal. The higher the initial concentration of Ibuprofen resulted in the lower percentage of degradation. This can be credited to the fact that the created photon and radicals were constant. The higher the initial concentration of Ibuprofen the fewer radicals were shared for each Ibuprofen molecular and so the lower percentage of degradation. The maximum photoactivity from the available light is accomplished when the concentration of catalyst reaches to 1 g/L (0.8 g), which was adopted as the optimal amounts. Compared to the removal of ibuprofen, the mineralization was relatively lower. This decrease is due to the organic content of the treated solution, which is mainly composed of recalcitrant intermediate products. The network was planned as a Levenberg-Marquardt algorithm with three layer, four neurons in the input layer, fourteen neurons in the hidden layer and one neuron in the output layer (4:14:1). The artificial neural network was trained until the MSE value between the simulated data and the experimental results was 10−5. The best results (R 2 = 0.999 and MSE = 1.5 × 10−4) were obtained with a log sigmoid transfer function at hidden layer and a linear transfer function at output layer.

Oxidation of small aromatic compounds in rainwater by UV/H2O2: Optimization by response surface methodology

The presence of aromatic compounds in rainwater is a matter of concern, mainly when the use of rainwater in buildings is intended. The present work aimed to assess the oxidation of a mixture of small aromatic compounds (benzoic, 3,5-dihydroxybenzoic and syringic acids) in rainwater by the UV/H₂O₂ process, and the possibility of its optimization by the response surface methodology. The extent of oxidation was assessed by ultraviolet-visible and molecular fluorescence spectroscopies. During the oxidation of the mixture new chromophoric compounds were formed at an initial stage, but they were then degraded at a later stage. The increase of the H₂O₂ concentration, resulted in a higher extent of oxidation, while the initial pH value showed no influence in the oxidation of the mixture. The optimization of the oxidation was performed using the uniform design with the factors: initial H₂O₂ concentration, initial pH, and reaction time. The response surface model found, through the best subsets regression, described the extent of oxidation as function of the following variables: initial H₂O₂ concentration and reaction time, interaction between them, and also their respective quadratic forms. The optimal conditions, the lowest H₂O₂ concentration (3.1 mM) for a selected maximum reaction time (4 h), were applied to rainwater samples spiked with the mixture of contaminants and resulted in an extent of oxidation higher than 99.5%, validating the application of the model to real samples. Therefore, the UV/H₂O₂ process coupled to its optimization via response surface methodology may be an alternative for rainwater treatment in buildings.

A Study on removal of Methylene Blue dye by photo catalysis integrated with nanofiltration using statistical and experimental approaches

In this work, the removal of Methylene Blue dye from the synthetic textile effluent has been investigated using a hybrid system (photocatalysis and nanofiltration). The Commercial ZnO powder was used as a catalyst in the photocatalytic operation. Response surface methodology (RSM) was employed to optimize the various operating parameters such as pH, catalyst loading and time duration and this optimization has enhanced the decolorization efficiencies. The results were compared and contrasted with the individual as well as the combined systems at optimized conditions. The results indicate that the photocatalysis process alone has resulted in 33% of dye decolorization and 26.5% of total organic carbon (TOC) removal, while the individual ceramic nanoflitration system has yielded 43% of decolorization and 35.03% TOC removal. About 94% of the dye was decolorized, and 70% of TOC was removed in 94.23 minutes of operation by the hybrid system at optimized initial operating conditions.

Enhanced chemiluminescence determination of paracetamol

Due to the severe consequences of potential overdoses of paracetamol (PCM) on the human body, the measurement of PCM in pharmaceutical and biological samples is essential.

CrVI reductive transformation process by humic acid extracted from bog peat: Effect of variables and multi-response modeling

The present paper reports the efficiency of bog peat-derived humic acid (HA) in the reductive removal of hexavalent chromium (Cr^VI) from aqueous solution as affected by solution pH, the dose of Fe^III and reaction time (numeric variables) and light irradiation (categorical variable). A three-level Box-Behnken design (BBD) applied to design experimental matrix, model the effects and interactions of variables on four determined responses (residual concentration of dissolved Cr^VI, dissolved Cr^III, dissolved Fe^II and total Cr^VI) and optimize the experimental conditions for highest Cr^VI removal efficiency (Cr^VI RE). Reaction mechanisms are also well discussed. Regression models were developed and analyzed by the ANOVA test and models determination coefficient R². Obtained models were significant (F values > 13) and an excellent relationship between experimental and predicted responses (R²: 98.1-99.6%) was observed. The optimum conditions were established corresponding to the residual concentration of dissolved Cr^VI as an index for Cr^VI removal efficiency (RE). In the dark system, the highest Cr^VI RE (98.1%) was obtained under the following conditions: pH = 1, reaction time = 7 d and Fe^III dosage = 0.110 mM. In the light-irradiated system, the optimal Cr^VI RE of 98.3% was observed in pH = 1, reaction time = 5 d and Fe^III dosage = 0.075 mM. Almost all reduced Cr^III remained in the solution even at high pH value. No adsorption or precipitation of Cr^III on the HA surface at pH 5 was confirmed by surface analyses of HA using X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM).

Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles

In the present work a series of design rules are developed in order to tune the morphology of TiO₂ nanoparticles through hydrothermal process. Through a careful experimental design, the influence of relevant process parameters on the synthesis outcome are studied, reaching to the develop predictive models by using Machine Learning methods. The models, after the validation and training, are able to predict with high accuracy the synthesis outcome in terms of nanoparticle size, polydispersity and aspect ratio. Furthermore, they are implemented by reverse engineering approach to do the inverse process, i.e. obtain the optimal synthesis parameters given a specific product characteristic. For the first time, it is presented a synthesis method that allows continuous and precise control of NPs morphology with the possibility to tune the aspect ratio over a large range from 1.4 (perfect truncated bipyramids) to 6 (elongated nanoparticles) and the length from 20 to 140 nm.

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.

Fe3+-sulfite complexation enhanced persulfate Fenton-like process for antibiotic degradation based on response surface optimization

To improve the cycle between Fe³⁺ and Fe²⁺ in persulfate (PS) Fenton-like system, sulfite (Na₂SO₃) was used as the iron complexing agent to enhance the degradation of sulfamethoxazole (SMX) antibiotic in water. Response surface methodology (RSM) was applied to regulate the operation parameters for the Fe³⁺/Na₂SO₃/PS synergistic system. Based on the RSM, the SMX could be completely degraded when the concentration of Fe³⁺, Na₂SO₃, and PS were 0.4, 0.5, and 2.5 mM, respectively. The result showed that the synergistic process represented a high Fe³⁺ utilization rate and SMX degradation efficiency. After 1 h reaction, 100.00% of SMX and 27.80% of total organic carbon were removed under the ambient conditions containing the initial SMX concentration of 10 μM and initial pH of 5.96. Free radical masking and electron spin-resonance tests proved that hydroxyl radical (HO) and oxysulfur radicals (SO_x^-, x = 3, 4, 5) were all played the significant role in the antibiotic removal, and the primary active radical was HO. The SMX decomposition pathways based on the formed intermediates was proposed through the high-performance liquid chromatography and mass spectrum analyses. The toxicity assessment prediction indicated that the toxicities of decomposed SMX byproducts were reduced after the coupling treatment.

Micellar-Enhanced Ultrafiltration to Remove Nickel Ions: A Response Surface Method and Artificial Neural Network Optimization

Nickel ions from aqueous solutions were removed by micellar-enhanced ultrafiltration (MEUF), using the surfactant sodium dodecyl sulfate (SDS) as a chelating agent. Process variables and indicators were modeled and optimized by a response surface methodology (RSM), using the Box–Behnken design (BBD). The generated quadratic models described the relationship between a performance indicator (nickel rejection rate or permeate flux) and process variables (pressure, nickel concentration, SDS concentration, and molecular weight cut-off (MWCO)). The analysis of variance (ANOVA) showed that both models are statistically significant. To remove 1 mM of nickel ions, the optimal condition for maximum nickel removal and flux were: pressure = 30 psi, CSDS = 10.05 mM, and MWCO = 10 kDa, resulting in a rejection rate of 98.16% and a flux of 119.20 L/h∙m2. Experimental verification indicates that the RSM model could adequately describe the performance indicators within the examined ranges of the process variables. An artificial neural network (ANN) modelling followed to predict the MEUF performance and validate the RSM results. The obtained ANN models showed good fitness to the experimental data.

Tackling environmental challenges in pollution controls using artificial intelligence: A review

This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.

Design and Optimization of Flexible Polypyrrole/Bacterial Cellulose Conductive Nanocomposites Using Response Surface Methodology

Flexible conductive materials have greatly promoted the rapid development of intelligent and wearable textiles. This article reports the design of flexible polypyrrole/bacterial cellulose (PPy/BC) conductive nanocomposites by in situ chemical polymerization. Box-Behnken response surface methodology has been applied to optimize the process. The effects of the pyrrole amount, the molar ratio of HCl to pyrrole and polymerization time on conductivity were investigated. A flexible PPy/BC nanocomposite was obtained with an outstanding electrical conductivity as high as 7.34 S cm⁻¹. Morphological, thermal stability and electrochemical properties of the nanocomposite were also studied. The flexible PPy/BC composite with a core-sheath structure exhibited higher thermal stability than pure cellulose, possessed a high areal capacitance of 1001.26 mF cm⁻² at the discharge current density of 1 mA cm⁻², but its cycling stability could be further improved. The findings of this research demonstrate that the response surface methodology is one of the most effective approaches for optimizing the conditions of synthesis. It also indicates that the PPy/BC composite is a promising material for applications in intelligent and wearable textiles.

Degradation of sulfanilamide by Fenton-like reaction and optimization using response surface methodology

Excess sulfonamides are discharged into the environmental system due to the abuse of antibiotics, which threatens the ecological environment and human health. In this study, the ferric and ferrous as well as calcium peroxide (CP), sodium percarbonate (SPC) and sodium persulfate (SPS) have been used to build Fenton-like system for the sulfanilamide (SA) removal. Compared with other Fenton-like system, the Fe³⁺/CP system exhibited better degradation capacity and 94.65% SA was removed with 3.0 mM CP and 3.0 mM Fe³⁺. A response surface and corresponding quadratic regression equation were obtained by using a three-level Box-Behnken factorial design with the initial pH value and the dosage of Fe³⁺ and CP as the model parameters. Depended on the result of the response surface, the optimum conditions of the removal of SA in Fe³⁺/CP system could be obtained: [Fe³⁺] = 2.96 mM, [CaO₂] = 2.33 mM and [pH] = 6.45. Besides that, the influences of Na⁺, Mg²⁺, Cl^-, HCO₃^-, NO₃^- and HA on SA removal were also investigated under the optimum condition. The results revealed that the high concentration of HCO₃^- was able to inhibit degradation of SA while other ions and HA have little effect on SA degradation. These results provided a novel strategy to evaluate the catalyst/oxidant system by combining experiment and computer simulation in wastewater treatment.

Optimization of photo-Fenton process for the treatment of prednisolone

Prednisolone is a widely prescribed synthetic glucocorticoid and stated to be toxic to a number of non-target aquatic organisms. Its extensive consumption generates environmental concern due to its detection in wastewater samples at concentrations ranged from ng/L to μg/L that requests the application of suitable degradation processes. Regarding the actual treatment options, advanced oxidation processes (AOPs) are presented as a viable alternative. In this work, the comparison in terms of pollutant removal and energetic efficiencies, between different AOPs such as Fenton (F), photo-Fenton (UV/F), photolysis (UV), and hydrogen peroxide/photolysis (UV/H₂O₂), was carried out. Light diode emission (LED) was the selected source to provide the UV radiation. The UV/F process revealed the best performance, reaching high levels of both degradation and mineralization with low energy consumption. Its optimization was conducted and the operational parameters were iron and H₂O₂ concentrations and the working volume. Using the response surface methodology with the Box-Behnken design, the effect of independent variables and their interactions on the process response were effectively evaluated. Different responses were analyzed taking into account the prednisolone removal (TOC and drug abatements) and the energy consumptions associated. The obtained model showed an improvement of the UV/F process when treating smaller volumes and when adding high concentrations of H₂O₂ and Fe²⁺. The validation of this model was successfully carried out, having only 5% of discrepancy between the model and the experimental results. Finally, the performance of the process when having a real wastewater matrix was also tested, achieving complete mineralization and detoxification after 8 h. In addition, prednisolone degradation products were identified. Finally, the obtained low energy permitted to confirm the viability of the process.

Quantifying the effect of polyacrylamide dosage, Na+ and Ca2+ concentrations, and clay particle size on the flocculation of mature fine tailings with robust statistical methods

Polymer-driven flocculation and dewatering of mature fine tailings (MFT) is critical to improve their consolidation. MFT flocculation and dewatering depends on the size of the suspended clay particles, and on the composition and properties of the liquid in which they are dispersed. The effect of water chemistry on the polymer-particle dynamics is nontrivial, particularly for non-spherical, polydisperse particles such as natural clays. In this study, we used a response surface methodology to systematically assess the impact of Na⁺ and Ca²⁺ concentration and anionic polyacrylamide dosage in the flocculation and dewatering of MFT. We observed a beneficial synergistic effect between Ca²⁺ concentration and polyacrylamide dosage, although excess of Ca²⁺ may reduce polyacrylamide activity. In addition, we investigated the impact of clay particle size on MFT flocculation. Polyacrylamide did not flocculate MFT fractions where the fine clay particles (<2 μm) represented most of the population. Good settling, however, was observed when fine silt particles (from 2 to 44 μm) were present, indicating that the presence and accumulation of larger/heavier particles on the polymer-induced flocs is crucial to form aggregates that readily settle under gravity. The insights gained from this study can contribute to more efficient use of flocculants, more effective use of cations, and better understanding of the impact of particles size in MFT flocculation.

Understanding the coagulant activity of zirconium oxychloride to control THMs formation using response surface methodology

In the present study, impact of coagulant activity of zirconium oxychloride and aluminium sulphate on the kinetics of chlorine consumption and trihalomethanes (THMs) formation has been delineated. Zirconium Oxychloride showed rapid chlorine decay within the first 30 min, which further achieved steady rate after 60 min, but in case of aluminium sulphate chlorine consumption has been increased drastically throughout the chlorine decay. Zirconium oxychloride has effectively reduced significant amount of slow reducing agents (SRA) as well as fast reducing agents (FRA), which correspond to the rate of reduction in phenolic groups from water enriched with Natural Organic Matter (NOM) which eventually decreased trihalomethane mediated cancer risk by ~ 2.3 times among adults as compared to aluminium sulphate. Result depicts the outstanding coagulant activity of zirconium oxychloride as it tends to surpass aluminium sulphate in reducing NOM "measured as Absorbance Slope Index (ASI)" and phenol by 57.98% and 49.02% respectively from NOM enriched chlorinated water, which also resembles the THMs removal trend observed during cancer risk assessment.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection.

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms.

Preparation and characterization of monoliths HKUST-1 MOF via straightforward conversion of Cu(OH)2-based monoliths and its application for wastewater treatment: artificial neural network and central composite design modeling

Highly crystalline water stable monolithic HKUST-1 MOF by a straightforward conversion of Cu(OH)₂-based monoliths was prepared and characterized via FE-SEM, XRD and EDS analysis.

Treatment of real benzene dye intermediates wastewater by the Fenton method: characteristics and multi-response optimization

Benzene dye intermediates (BDI) wastewater has caused major environmental concern due to its potential carcinogenic, teratogenic, and mutagenic effects.

Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate

In this study, two modeling methods, namely response surface methodology (RSM) and artificial neural networks (ANN), were applied to investigate the Fenton process performance in landfill leachate treatment. For this purpose, three targets were used to cover different aspects of post-treatment products such as supernatant and sludge: mass content ratio (MCR) and mass removal efficiency (MRE). It was observed that coagulation was dominant mechanism in all responses. The proposed models were evaluated based on correlation coefficient (R²), root mean square error (RMSE) and average error (AE) and both models seemed satisfactory. However, the better results of 0.97-0.98 for R², 1.45-1.86 for RMSE and 2-4% for error, indicated relative superiority of ANN compared to RSM. In addition, it was revealed that [H₂O₂]/[Fe²⁺] mole ratio had the greatest effect in the targets, while Fe dosage and pH had lower ones. Finally, to investigate the predictive performance of both models, some additional experiments were conducted in expected optimum conditions that resulted to 27% sludge MCR, 14% effluent MCR, and 56% MRE. The results showed low deviation from predicted values with maximum errors of 8% and 9% for RSM and ANN, respectively. Though in most cases, ANN error values were lower than RSM values. Also, it was proved that setting RSM prior to ANN (as a feeding tool) improves the predictive capability of ANN significantly.

Novel RGO/α-FeOOH supported catalyst for Fenton oxidation of phenol at a wide pH range using solar-light-driven irradiation

A novel solar-light-driven (SLD) Fenton catalyst was developed by reducing the ferrous-ion onto graphene oxide (GO) and forming reduced graphene oxide/α-FeOOH composites (RF) via in-situ induced self-assembly process. The RF was supported on several mesoporous supports (i.e., Al-MCM-41, MCM-41 and γ-Al₂O₃). The activity, stability and energy use for phenol oxidation were systematically studied for a wide pH range. Furthermore, the catalytic mechanism at acid and alkaline aqueous conditions was also elucidated. The results showed that Fe(II) was reduced onto GO nanosheets and α-FeOOH crystals were formed during the self-assembly process. Compared with Fenton reaction without SLD irradiation, the visible light irradiation not only dramatically accelerated the rate of Fenton-based reactions, but also extended the operating pH for the Fenton reaction (from 4.0 to 8.0). The phenol oxidation on RF supported catalysts was fitting well with the pseudo-first-order kinetics, and needed low initiating energy, insensitive to the reacting temperature changes (273-318K). The Al-MCM-41 supported RF was a more highly energy-efficient catalyst with the prominent catalytic activity at wide operating pHs. During the reaction, OH radicals were generated by the SLD irradiation from H₂O₂ reduction and H₂O oxidation in the Fe^Ⅱ/Fe^Ⅲ and Fe^Ⅲ/Fe^Ⅳ cycling processes.

Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks

Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.

Slaughterhouse wastewater treatment using an advanced oxidation process: Optimization study

In this paper, a poultry slaughterhouse wastewater (PSW) was treated in terms of chemical oxygen demand (COD) and color reduction using electro-Fenton (EF) technique under response surface methodology (RSM). The effects of five significant independent variables such as reaction time, pH, H₂O₂/Fe²⁺ molar ratio, current density, volume ratio of H₂O₂/PSW (ml/l) were investigated on the COD and color removal. Experimental data were optimized by Box-Behnken design (BBD) and RSM. The optimum conditions were experimentally found at pH of 4.38, reaction time of 55.60 min, H₂O₂/Fe²⁺ molar ratio of 3.73, current density of 74.07 mA/cm², volume ratio of H₂O₂/PSW of 1.63 ml/l for 92.37%COD removal and at pH of 3.39, reaction time of 49.22 min, H₂O₂/Fe²⁺ molar ratio of 3.62, current density of 67.90 mA/cm², volume ratio of H₂O₂/PSW of 1.44 ml/l for 88.06% color removal.

Optimization of paeonol-loaded poly(butyl-2-cyanoacrylate) nanocapsules by central composite design with response surface methodology together with the antibacterial properties

With the aim to enhance dissolution rate and bioavailability of paeonol, paeonol-loaded poly(butyl-2-cyanoacrylate) nanocapsules (Pae@PNCs) were prepared by interfacial spontaneous polymerization for the first time. Herein, a rotatable central composite design (RCCD) with three-factor five-level was applied to evaluate the optimization experiments. To the maximum percentage encapsulation efficiency (EE%) and minimum particle size (nm) of the Pae@PNCs, a quadratic polynomial model was generated to predict and evaluate the independent variables with respect to the dependent variables. RSM model goodness fitting were confirmed by the ANOVA Table (P<0.05) through variance analysis, which predicted values of EE (%) and particle size (R² and adjusted R² were close to 1, respectively) in good agreement with experimental values. By solving the regression equation and analyzing the response surface, three-dimensional model graphs and plots, the optimal result for the preparation of Pae@PNCs were found to be: pH (2.34), Poloxamer F-68 (0.80% m/v) and ethyl acetate/α-BCA ratio (16.67 v/v) for the highest EE% (73.58±2.76%) and the smallest particle size (42.06±1.20nm). The release profiles and antibacterial activity in vitro from the optimal Pae@PNCs were performed. The results indicated that it has slow and well-controlled release, and has strong antibacterial activity in vitro than paeonol. This understanding can help to predict the conditions of optimization of poly(butyl-2-cyanoacrylate) nanoparticles formation and to improve paeonol bioavailability and pharmacological properties.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R² (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

Modeling and optimization of Newfoundland shrimp waste hydrolysis for microbial growth using response surface methodology and artificial neural networks

The hydrolyzed protein derived from seafood waste is regarded as a premium and low-cost nitrogen source for microbial growth. In this study, optimization of enzymatic shrimp waste hydrolyzing process was investigated. The degree of hydrolysis (DH) with four processing variables including enzyme/substrate ratio (E/S), hydrolysis time, initial pH value and temperature, were monitored. The DH values were used for response surface methodology (RSM) optimization through central composite design (CCD) and for training artificial neural network (ANN) to make a process prediction. Results indicated that the optimum levels of variables are: E/S ratio at 1.64%, hydrolysis time at 3.59h, initial pH at 9 and temperature at 52.57°C. Hydrocarbon-degrading bacteria Bacillus subtilis N3-1P was cultivated using different DHs of hydrolysate. The associated growth curves were generated. The research output facilitated effective shrimp waste utilization.

Degradation of aqueous methylene blue using an external loop airlift sonophotoreactor: Statistical analysis and optimization

Degradation and mineralization of aqueous methylene blue (MB) are investigated in a bench scale external loop airlift sonophotoreactor. A central composite design along with response surface methodology is employed to model and optimize the sonophotolytic process. A quadratic empirical expression between responses and independent variables (pH and initial concentrations of H2O2 and MB) is derived. The efficiencies of the system for the MB degradation after 10, 15, and 30 min, and total organic carbon reduction after 150 min are considered as responses. The analysis of variance performed high values for the coefficient of determination R(2) and adjusted R(2) for all four responses. Optimum values of process variables for the maximum degradation and mineralization efficiency are pH 6.6 and initial concentrations of H2O2 and MB are 1,280 and 10.56 mg/L, respectively. With optimal operating conditions, 99.93% and 55.32% MB removal (after 10 min) and TOC reduction (after 150 min) are achieved, respectively. Artificial neural networks are also used to model the experimental data. The respirometric study is conducted to compare the biodegradability of untreated and sonophotolytically pre-treated MB solutions at different reaction times. Pre-treated solutions at 180, 240, and 300 min performed higher biodegradability compared to those of untreated MB solutions.

Oxidation of AOX and organic compounds in pharmaceutical wastewater in RSM-optimized-Fenton system

Adsorbable organic halogens (AOX) and total organic carbon (TOC) removal efficiencies in pharmaceutical wastewater treated by Fenton process under response surface methodology (RSM) optimized conditions were studied. High regression coefficient value R(2) (R(2) = 0.9680, 0.9040 for AOX and TOC removal efficiency, respectively) and low value coefficient of variation (2.21%, 2.04% for AOX and TOC, respectively) of the quadratic model indicated that the model was accurate in predicting the experimental results. The desirability function was used to optimize AOX and TOC removal efficiencies simultaneously. The optimal pH, Fe(2+) concentration, molar ratio of H2O2/Fe(2+) and reaction time were found to be 3.3, 19.05 mM, 20.16 and 2.2 h, respectively, and 91.78% AOX and 75.01% TOC were removed under these conditions, which was validated. Furthermore, gas chromatography-mass spectrometer (GC-MS) results revealed that 28 out of 33 kinds of organic compounds, including 11 kinds of AOX were completely removed by the Fenton process while one new AOX compound, 4,5,6,7-tetrachlorophthalide, was produced which was the result of the carbonyl of 4,5,6,7-tetrachloro-1,3-isobenzofurandione being attacked in the Fenton reaction. These results indicated that analysis of organics was important since new AOX compounds could be produced in Fenton process despite the value of AOX decreasing.