Modelling dye removal by adsorption onto water treatment residuals using combined response surface methodology-artificial neural network approach

Valorization of tomato processing by-products: Predictive modeling and optimization for ultrasound-assisted lycopene extraction

Lycopene is a carotenoid highly valuable to the food, pharmaceutical, dye, and cosmetic industries, present in ripe tomatoes and other fruits with a distinctive red color. The main source of lycopene is tomato crops. This bioactive component can be successfully isolated from tomato processing waste, commonly called tomato pomace, mostly made from tomato skins, seeds, and some residual tomato tissue. The main investigative focus in this work was the application of green engineering principles in each stage of the optimized ultrasound-assisted extraction (UAE) of enzymatically treated tomato skins to obtain functional extracts rich in lycopene. The experimental plan was designed to determine the influence of studied operating parameters: enzymatic reaction time (60, 120, and 180 min), extraction time (0, 5, 10, 15, 30, 60, and 120 min), and temperature (25, 35 and 45 ℃) on lycopene yield. Process optimization was performed based on the yield of lycopene [1018, 1067, and 1120 mg/kg] achieved at optimal operating conditions. An artificial neural network (ANN) model was developed and trained for predictive modeling of the closed extraction system, with operating parameters used as input neurons and experimentally obtained values for lycopene content defined as the output neural layer. Applied ANN architecture provided a high correlation of experimental output with ANN-generated data (R=0.99914) with a model deviation error for the entire data set of RMSE=5.3 mg/kg. The k-Nearest Neighbor algorithm was introduced to predict lycopene yield using experimental key features: operating temperature, extraction time, and time of enzymatic treatment, split into training and testing sets with an 85/15 ratio. The model interpretation was conducted through the SHAP (SHapley Additive exPlanations) methodology.

Accurate prediction and intelligent control of COD and other parameters removal from pharmaceutical wastewater using electrocoagulation coupled with catalytic ozonation process

In this study, we employed the response surface method (RSM) and the long short-term memory (LSTM) model to optimize operational parameters and predict chemical oxygen demand (COD) removal in the electrocoagulation-catalytic ozonation process (ECOP) for pharmaceutical wastewater treatment. Through RSM simulation, we quantified the effects of reaction time, ozone dose, current density, and catalyst packed rate on COD removal. Then, the optimal conditions for achieving a COD removal efficiency exceeding 50% were identified. After evaluating ECOP performance under optimized conditions, LSTM predicted COD removal (56.4%), close to real results (54.6%) with a 0.2% error. LSTM outperformed RSM in predictive capacity for COD removal. In response to the initial COD concentration and effluent discharge standards, intelligent adjustment of operating parameters becomes feasible, facilitating precise control of the ECOP performance based on this LSTM model. This intelligent control strategy holds promise for enhancing the efficiency of ECOP in real pharmaceutical wastewater treatment scenarios. PRACTITIONER POINTS: This study utilized the response surface method (RSM) and the long short-term memory (LSTM) model for pharmaceutical wastewater treatment optimization. LSTM predicted COD removal (56.4%) closely matched experimental results (54.6%), with a minimal error of 0.2%. LSTM demonstrated superior predictive capacity, enabling intelligent parameter adjustments for enhanced process control. Intelligent control strategy based on LSTM holds promise for improving electrocoagulation-catalytic ozonation process efficiency in pharmaceutical wastewater treatment.

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%.

Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm

Given the necessity and urgency in removing organic pollutants such as malachite green (MG) from the environment, it is vital to screen high-capacity adsorbents using artificial neural network (ANN) methods quickly and accurately. In this study, a series of ZIF-67 were synthesized, which adsorption properties for organic pollutants, especially MG, were systematically evaluated and determined as 241.720 mg g-1 (25 ℃, 2 h). The adsorption process was more consistent with pseudo-second-order kinetics and Langmuir adsorption isotherm, which correlation coefficients were 0.995 and 0.997, respectively. The chemisorption mechanism was considered to be π-π stacking interaction between imidazole and aromatic ring. Then, a Python-based neural network model using the Limited-memory BFGS algorithm was constructed by collecting the crucial structural parameters of ZIF-67 and the experimental data of batch adsorption. The model, optimized extensively, outperformed similar Matlab-based ANN with a coefficient of determination of 0.9882 and mean square error of 0.0009 in predicting ZIF-67 adsorption of MG. Furthermore, the model demonstrated a good generalization ability in the predictive training of other organic pollutants. In brief, ANN was successfully separated from the Matlab platform, providing a robust framework for high-precision prediction of organic pollutants and guiding the synthesis of adsorbents.

Research on the kinetics and degradation pathways of gaseous acetic acid ester organics

ABSTRACTDesigned to meet the specific needs of the printing industry exhaust gas emissions, this paper proposes a method for the degradation of gaseous acetic acid ester organics that is environmentally friendly, safe, and simple to use: micro-nano cavitation technology. In the process of using micro-nano cavitation technology to degrade acetic acid ester organics, the products in the degradation process were analyzed by gas chromatography-mass (GC-MS) spectrometry, and the degradation pathways of acetic acid ester organics were identified. Under high temperatures and high pressure caused by cavitation collapse, the C-C bond and C-O bond on the main chain of organic matter are cleaved to form low molecular products. Low-molecular intermediate products are continuously produced as the reaction advances, and these intermediate products are further oxidized and decomposed into carbon dioxide and water. Besides, the factors that influence the degradation rate of acetic acid ester organics were investigated. Based on the experimental data, acetic acid esters can degrade with the greatest efficiency when their initial concentration is 200 ± 50 mg/m3 and their treatment time is 20∼30 min. Moreover, the experiment was optimized using the response surface method. The results suggested that for an initial concentration of 155.544 mg/m3 and a reaction time of 21.961 min, the best degradation rate was 0.251 min-1. Micro-nano cavitation technology is a novel and promising technology for the degradation of volatile organic compounds, with a wide range of practical applications.

Response surface methodology for process optimization in livestock wastewater treatment: A review

With increasing demand for meat and dairy products, the volume of wastewater generated from the livestock industry has become a significant environmental concern. The treatment of livestock wastewater (LWW) is a challenging process that involves removing nutrients, organic matter, pathogens, and other pollutants from livestock manure and urine. In response to this challenge, researchers have developed and investigated different biological, physical, and chemical treatment technologies that perform better upon optimization. Optimization of LWW handling processes can help improve the efficacy and sustainability of treatment systems as well as minimize environmental impacts and associated costs. Response surface methodology (RSM) as an optimization approach can effectively optimize operational parameters that affect process performance. This review article summarizes the main steps of RSM, recent applications of RSM in LWW treatment, highlights the advantages and limitations of this technique, and provides recommendations for future research and practice, including its cost-effectiveness, accuracy, and ability to improve treatment efficiency.

Modeling and optimization of triclosan biodegradation by the newly isolated Bacillus sp. DL4: kinetics and pathway speculation

Triclosan is a widely used antibacterial agent and disinfectant, and its overuse endangered ecological safety and human health. Therefore, reducing residual TCS concentrations in the environment is an urgent issue. Bacillus sp. DL4, an aerobic bacterium with TCS biodegradability, was isolated from pharmaceutical wastewater samples. Response surface methodology (RSM) and artificial neural network (ANN) were carried out to optimize and verify the different condition variables, and the optimal growth conditions of strain DL4 were obtained (35 °C, initial pH 7.31, and 5% v/v). After 48 h of cultivation under the optimal conditions, the removal efficiency of strain DL4 on TCS was 95.89 ± 0.68%, which was consistent with the predicted values from RSM and ANN models. In addition, higher R2 value and lower MSE and ADD values indicated that the ANN model had a stronger predictive capability than the RSM model. Whole genome sequencing results showed that many functional genes were annotated in metabolic pathways related to TCS degradation (e.g., amino acid metabolism, xenobiotics biodegradation and metabolism, carbohydrate metabolism). Main intermediate metabolites were identified during the biodegradation process by liquid chromatography-mass spectrometry (LC-MS), and a possible pathway was hypothesized based on the metabolites. Overall, this study provides a theoretical foundation for the characterization and mechanism of TCS biodegradation in the environment by Bacillus sp. DL4.

Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction

Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.

Enhanced photocatalytic degradation of methylene blue using a novel counter-rotating disc reactor

Introduction: This research introduces an innovative photocatalytic reactor designed to address challenges in wastewater treatment, with a focus on enhancing dye degradation and reducing Chemical Oxygen Demand (COD). Methods: The reactor is designed with counter-rotational movements of discs to enhance hydrodynamics and mass transfer, along with a 3D-printed, interchangeable component system to boost efficacy. TiO2 nanoparticles, composed of 80% anatase and 20% rutile, are thermally immobilized on glass discs. The effectiveness of various treatment variables was assessed through a Central Composite Design (CCD), guided by a Response Surface Methodology (RSM) model. Results: The RSM analysis reveals that the linear, quadratic, and interactive effects of the counter-rotational movements significantly influence the efficiency of dye and COD removal. The RSM model yields coefficients of determination (R2) values of 0.9758 and 0.9765 for the predictive models of dye and COD removal, respectively. Optimized parameters for dye removal include a pH of 6.05, disc rotation speed of 22.35 rpm, initial dye concentration of 3.15 × 10-5 M, residence time of 7.98 h, and the number of nanoparticle layers set at 3.99, resulting in 96.63% dye removal and 65.81% COD removal under optimal conditions. Discussion: Notably, the reactor demonstrates potential for efficient treatment within a near-neutral pH range, which could reduce costs and resource use by eliminating the need for pH adjustments. The implementation of discs rotating in opposite directions marks a significant advancement in the process of dye removal.

Optimization of radioprotective dose of Juglans nigra leaf extract using response surface methodology

J. nigra leaf contains mixture of various pharmacologically active compounds and it is assumed that they may have the potential radioprotective effect. The purpose of this work was to predict radioprotective doses by correlating changes in organ distribution of radiopharmaceuticals with extract dose levels and rat body weight using response surface methodology (RSM) based on a second-order polynomial equation. The extract was administered daily by oral gavage to rats at dose of 6.9, 10.3, or 13.7 mg kg-1 body weight (bw) day-1 for 10 days. On the eleventh day, 0.1 ml of the one radiopharmaceutical (Na99mTcO4, 99mTc-dimercaptosuccinic acid and 99mTc-tin colloid) was injected into the tail vein. The statistical parameters: the coefficient of determination (0.81-0.95), the coefficient of variation (3.05-11.1), the adequate precision (8.82-19.12) and the mean relative percentage deviation (± 2.3-8.2) were indicated the precision and reliability of RSM. Using RSM, the predicted daily dose of leaf extract ranging from 11.19 to 11.99 mg kg-1 bw may be considered as an optimal daily radiopotective dose for protection of organs from radiation in cases of planned radiation exposures.

Proficient sonophotocatalytic degradation of organic pollutants using Co3O4/TiO2 nanocomposite immobilized on zeolite: Optimization, and artificial neural network modeling

The health of all living organisms is greatly influenced by the quality of the water. Therefore, developing cost-effective, eco-friendly, and easily accessible methods is desperately needed to meet the high global demand for clean water. Recently, nanozyme-based dye degradation methods have been promising for the remediation of water pollution. In this work, peroxidase-mimic Co3O4/TiO2 nanocomposite was synthesized and characterized for its size, morphology, and crystalline structure. Colorimetric assay results showed that the peroxidase-like activity of the Co3O4/TiO2 nanocomposite was considerably enhanced compared to the pure Co3O4 NPs and TiO2 NPs. Besides excellent enzyme-mimic activity, the higher sonophotocatalytic dye degradation capability of the nanocomposite after immobilization on zeolite (Co3O4/TiO2@Ze) was also demonstrated. Under optimal conditions (pH = 5.0, 25 °C), 0.1 g/L of catalyst was able to degrade 100 % of methylene blue (MB) with 600 μM in the presence of 30 μM H2O2 within 12 min. GC/MS analysis and toxicity studies revealed less toxic metabolite production after treatment of MB with sonophotocatalytic Co3O4/TiO2@Ze. Modeling of MB degradation using artificial neural networks (ANN) with a 5:6:1 topology was successfully performed, and the results confirmed the fitness of theoretical and experimental outputs according to the calculated correlation coefficient values. The prepared nanocomposite could thus be used as a promising and highly effective catalyst for the removal of organic dyes from polluted water.

Decolorization of Textile Azo Dye via Solid-State Fermented Wheat Bran by Lasiodiplodia sp. YZH1

Textile dyes are one of the major water pollutants released into water in various ways, posing serious hazards for both aquatic organisms and human beings. Bioremediation is a significantly promising technique for dye decolorization. In the present study, the fungal strain Lasiodiplodia sp. was isolated from the fruiting bodies of Schizophyllum for the first time. The isolated fungal strain was examined for laccase enzyme production under solid-state fermentation conditions with wheat bran (WB) using ABTS and 2,6-Dimethoxyphenol (DMP) as substrates, then the fermented wheat bran (FWB) was evaluated as a biosorbent for Congo red dye adsorption from aqueous solutions in comparison with unfermented wheat bran. A Box-Behnken design was used to optimize the dye removal by FWB and to analyze the interaction effects between three factors: fermentation duration, pH, and dye concentration. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) were applied to study the changes in the physical and chemical characteristics of wheat bran before and after fermentation. An additional experiment was conducted to investigate the ability of the Lasiodiplodia sp. YZH1 to remove Congo red in the dye-containing liquid culture. The results showed that laccase was produced throughout the cultivation, reaching peak activities of ∼6.2 and 22.3 U/mL for ABTS and DMP, respectively, on the fourth day of cultivation. FWB removed 89.8% of the dye (100 mg L-1) from the aqueous solution after 12 h of contact, whereas WB removed only 77.5%. Based on the Box-Behnken design results, FWB achieved 93.08% dye removal percentage under the conditions of 6 days of fermentation, pH 8.5, and 150 mg L-1 of the dye concentration after 24 h. The fungal strain removed 95.3% of 150 mg L-1 of the dye concentration after 8 days of inoculation in the dye-containing liquid culture. These findings indicate that this strain is a worthy candidate for dye removal from environmental effluents.

Preparation of Bamboo-Based Activated Carbon via Steam Activation for Efficient Methylene Blue Dye Adsorption: Modeling and Mechanism Studies

Dye pollution has long been an ecological and human health issue. Activated carbon is considered to be the most promising material for dye adsorption. In this study, bamboo was used as a biomass precursor to produce activated carbon with a high specific surface area by the steam activation method. The physical activation reaction between water vapor and bamboo promotes the carbonization product to have a rich porous structure. The prepared activated carbon was investigated from the perspectives of surface morphology, elemental change, surface porosity, and surface functional groups using a variety of techniques. According to the Brunauer-Emmett-Teller analysis, the activated carbon has a high surface area (1273 m2/g) and a mesoporous structure (average pore size 3.1 nm). On this basis, the effect of activated carbon on the removal of methylene blue (MB) dye from aqueous environments was evaluated and optimized by response surface methodology (RSM). Key adsorption parameters include initial MB concentration (150-200 mg/L), adsorption time (5-120 min), adsorbent dosage (30-50 mg), adsorption temperature (5-50 °C), and solution pH (3-11). Box-Behnken design (BBD) was used for modeling and analysis. Kinetic and isotherm model studies show that pseudo-second-order model kinetics and Langmuir isotherm can better describe the process of MB dye adsorption. This study will provide new ideas for the preparation of bamboo-activated carbon and provide a model prediction basis for dye adsorption research.

Black carbon derived PET plastic bottle waste and rice straw for sorption of Acid Red 27 dye: Machine learning approaches, kinetics, isotherm and thermodynamic studies

This study focuses on the probable use of PET waste black carbon (PETWBC) and rice straw black carbon (RSBC) as an adsorbent for Acid Red 27 (AR 27) adsorption. The prepared adsorbent is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1-180 min), AR 27 concentration (5-70 mg/L), adsorbent dose (0.5-20 g/L), pH (2-10), and temperature (25-60°C). High coefficient value [PETWBC (R2 = 0.94), and RSBC (R2 = 0.97)] of process optimization model suggesting that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 99.88, and 99.89% for PETWBC, and RSBC at pH (2). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected value. Pseudo-second-order was the well-suited kinetics, where Freundlich isotherm could explain better equilibrium adsorption data. Thermodynamic study shows AR 27 adsorption is favourable, endothermic, and spontaneous. Environmental friendliness properties are confirmed by desorption studies and satisfactory results also attain from real wastewater experiments. Finally, this study indicates that PETWBC and RSBC could be potential candidates for the adsorption of AR 27 from wastewater.

Removal of hazardous textile dye from simulated wastewater by municipal organic solid waste charcoal using machine learning approaches: Kinetics, isotherm, and thermodynamics

This study focuses on the probable use of municipal organic solid waste charcoal (MOSWC) as an adsorbent for Methyl orange (MO) adsorption. The prepared MOSWC is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1-180 min), adsorbate concentration (60-140 mg/L), adsorbent dose (1-5 g/L), pH (3-11), and temperature (25-60 °C). The high coefficient value (R2 = 0.96) of the process optimization model suggests that this model was significant, where pH and adsorbent dose expressively stimulus adsorption efficiency including 40.11 mg/g at pH (3), MO concentration (100 mg/L), and MOSWC dose (1 g/L). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected values. The highest monolayer adsorption capacity of MO was 90.909 mg/g. Pseudo-second-order was the well-suited kinetics, where Langmuir isotherm could explain better for equilibrium adsorption data. Thermodynamic study shows MO adsorption is favourable, exothermic, and spontaneous. Finally, this study indicates that MOSWC could be a potential candidate for the adsorption of MO from wastewater.

Valorization of Glucose-Derived Humin as a Low-Cost, Green, Reusable Adsorbent for Dye Removal, and Modeling the Process

Glucose can be isomerized into fructose and dehydrated into key platform biochemicals, following the "bio-refinery concept". However, this process generates black and intractable substances called humin, which possess a polymeric furanic-type structure. In this study, glucose-derived humin (GDH) was obtained by reacting D-glucose with an allylamine catalyst in a deep eutectic solvent medium, followed by a carbonization step. GDH was used as a low-cost, green, and reusable adsorbent for removing cationic methylene blue (MB) dye from water. The morphology of carbonized GDH differs from pristine GDH. The removal efficiencies of MB dye using pristine GDH and carbonized GDH were 52% and 97%, respectively. Temperature measurements indicated an exothermic process following pseudo-first-order kinetics, with adsorption behavior described by the Langmuir isotherm. The optimum parameters were predicted using the response surface methodology and found to be a reaction time of 600 min, an initial dye concentration of 50 ppm, and a GDH weight of 0.11 g with 98.7% desirability. The MB dye removal rate optimized through this model was 96.85%, which was in good agreement with the experimentally obtained value (92.49%). After 10 cycles, the MB removal rate remained above 80%, showcasing the potential for GDH reuse and cost-effective wastewater treatment.

Modelling coagulant dosage in drinking water treatment plant using advance machine learning model: Hybrid extreme learning machine optimized by Bat algorithm

Despite the high importance of coagulation process in drinking water treatment plant (DWTP), challenge remains in effectively linking raw water quality measured at the inlet of the DWTP with coagulant dosage rate. This study proposes an integral modelling framework using hybrid extreme learning machine and Bat metaheuristic algorithm (ELM-Bat) for modelling coagulant dosage rate using water temperature, pH, specific conductance, dissolved oxygen, and water turbidity. The aluminum sulphate (Al₂ (SO₄)₃.18H₂O) coagulant is determined using conventional Jar-Test procedure. Results obtained using the hybrid ELM-Bat were compared to those obtained using standalone ELM, outlier robust extreme learning machine (ORELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), and kernel extreme learning machine (KELM). First, the models have been calibrated during the training stage and in a second stage; they are validated using various statistical metrics, i.e., RMSE, MAE, the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). We found that the hybrid ELM-Bat was significantly more accurate and it has yielded accuracy higher than all other models. During the validation stage, the R and NSE values calculated using the ELM-Bat were ≈0.959 and ≈0.918 exhibiting an improvement rates of approximately (≈15.26% and ≈33.82%), (≈10.35% and ≈21.92%), (≈14.98% and ≈31.89%), (≈7.63% and ≈16.35%), (≈10.99% and ≈23.05%), compared to the values obtained using the ELM, OPELM, OSELM, KELM and ORELM, respectively. Besides, the new ELM-Bat model has shown to have high predictive capabilities, which can be used optimally for calculating the optimal coagulant dosage with high accuracy.

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.

Machine learning modelling of removal of reactive orange RO16 by chemical activated carbon in textile wastewater

This study develops machine learning model of removal of reactive orange dye (Azo) RO16 from textile wastewater by chemical activated carbon CAC. The study addresses the contamination removal efficiency with respect to changing dynamics of concentration, temperature, time, pH and dose, respectively. Machine learning based learning multiple polynomial regression is implemented to fit a model on the experimental observed data. The machine learns from the data and fit the multiple polynomial regression model for the data. The observed and predicted data are in close agreement with the R-squared value of 92%. The results show that the baseline efficiency of using chemical activated carbon adsorbent for removing RO16 is 76.5%. The most significant input parameter increasing the efficiency by a constant value of 35 units out of 100 is the second order response of the dose. Moreover, four input parameters can considerably increase the efficiency. Furthermore, six input parameters can considerably decrease the efficiency. It is investigated, that the second order response with respect to time has the minute decreasing effect on the removal efficiency. The superior abilities of the modeling are two fold. Firstly, the contamination removal of reactive orange dye (Azo) RO16 with chemical activated carbon adsorbent is studied with respect to five multiple parameters. Secondly, the model exploits the machine learning capability of the renowned Python machine learning module sklearn to fit a multiple polynomial regression model. Thus a robust model is fitted giving twenty-one inputs/output interactions and responses. From the input-target correlation analysis it is clear that the removal efficiency has a strong correlation with the time. It has considerably significant relationship with dose of the CAC and the temperature with values of 18% and 17%, respectively. Moreover, the removal efficiency has inverse relations with pH and Ci, with values of 15% and 12%, respectively.

Modeling and optimization approach for phytoremediation of formaldehyde from polluted indoor air by Nephrolepis obliterata plant

This study aimed to model the removal of formaldehyde as an indoor air pollutant by Nephrolepis obliterata (R.Br.) J.Sm. plant using response surface methodology (RSM) and artificial neural network (ANN) models, and optimization of the models by particle swarm optimization algorithm (PSO). The data obtained in pilot-scale experiments under a controlled environment were used in this study. The effects of parameters on the removal efficiency such as formaldehyde concentration, relative humidity, light intensity, and leaf surface area were empirically investigated and considered as model parameters. The results of the RSM model, with power transformation, were in meaningful compromise with the experiments. A multilayer perceptron (MLP) neural network was also designed, and the mean of squared error (MSE), mean absolute error (MAE), and R² were used to evaluate the network. Several training algorithms were assessed and the best one, the Levenberg Marquardt (LM), was selected. The PSO algorithm proved that the highest removal efficiency of formaldehyde was obtained in the presence of light, maximum leaf surface area and relative humidity, and at the lowest inlet concentration. The empirical system breakthrough occurred at 15 mg/m³ of formaldehyde, and the maximum elimination capacity was about 0.96 mg per m² of leaves. The findings indicated that the ANN model predicted the removal efficiency more accurately compared to the RSM model.

Optimization and modeling of ammonia nitrogen removal from anaerobically digested liquid dairy manure using vacuum thermal stripping process

During anaerobic digestion (AD) of liquid dairy manure, organic nitrogen converts to ammonia nitrogen (NH₃-N) and subsequently escalates the NH₃-N concentrations in manure. Among different available NH₃-N removal processes treating anaerobically digested liquid dairy manure (ADLDM), vacuum thermal stripping is reported to be an effective technique. However, none of the studies have performed multi-parameter optimization, which is of utmost significance in maximizing process efficiency. In this study, critical operational parameters for vacuum thermal stripping of NH₃-N from ADLDM were optimized and modeled for the first time via integrating grey relational analysis (GRA)-based Taguchi design, response surface methodology (RSM), and RSM-artificial neural network (ANN). The initial experimental trials conducted using the GRA coupled with Taguchi L₁₆ orthogonal array revealed the order of influence of the process parameters on NH₃-N removal as vacuum pressure (kPa) > temperature (°C) > treatment time (min) > mixing speed (rpm) > pH. The values of the first three most influential operating parameters were then further optimized and modeled using RSM and RSM-ANN models. Under the optimized conditions (temperature: 69.6 °C, vacuum pressure: 43.5 kPa, and treatment time: 87.65 min), the NH₃-N removal efficiency of 93.58 ± 0.59 % was experimentally observed and was in line with the RSM and RSM-ANN models' predicted values. While the RSM-ANN model showed a better prediction potential than did the RSM model when compared statistically. Moreover, the nutrient contents (nitrogen, N and sulfur, S) of the recovered NH₃-N as ammonium sulfate ((NH₄)₂SO₄) were in reasonable agreement with the market-available (NH₄)₂SO₄ fertilizer. The results presented in this study provide important insights into improving the treatment process performance and will help design and operate future pilot- and full-scale vacuum thermal stripping processes in dairy farms.

Efficient removal of levofloxacin by a magnetic NiFe-LDH/N-MWCNTs nanocomposite: Characterization, response surface methodology, and mechanism

Antibiotic pollutants in water bodies, was studied to remove using an oxidized, nitrogen-doped, and Fe₃O₄ and NiFe-LDH decorated MWCNT (magnetic NiFe-LDH/N-MWCNTs) nanocomposite (NC). The novel, engineered NC was characterized by different techniques of SEM, XRD, TEM, EDX, and XPS and then examined under different main effective parameters of NC dose, levofloxacin (LVX) concentration, pH, time, and temprature. The experimentally obtained data then evaluated using the modeling approaches of RSM, GRNN, and ANFIS. The as prepared adsorbent showed an excellent adsorption performance (removal efficiency = 95.28% and adsorption capacity = 344.83-454.55 mg/g) under the respective values of the mentioned parameters of 0.152 g, 23.01 mg/L, 12.00 min, and 37.5 °C, respectively. The comparison of the models showed that although all of them accurately predicted the removal efficiency, ANFIS presented the best capability with R², RMSE, MSE, MAE, as well as AAD of 0.9998, 0.0082, -0.0004, 0.0069, 0.1322, respectively. The adsorption by the NC followed Freundlich isotherm (R² = 0.9993) and PSO kinetic (>0.998) models, confirming a heterogenous chemisorption process. The thermodynamic parameters showed an endothermic and spontaneous nature for LVX removal by magnetic NiFe-LDH/N-MWCNTs NC. A high-performance efficiency, appropriate reusability (five times without loss of efficiency), as well as easy separation due to magnetic properties, makes the NC to a promising option in removing LVX from water.

Application of QSAR for investigation on coagulation mechanisms of textile wastewater

Coagulation is an effective preliminary treatment process for textile wastewater. In order to evaluate the effectiveness of the coagulation process, we performed quantitative structure activity relationship (QSAR) analyses with total organic carbon (TOC) removal rates (R_exp) as an index by three different coagulants (AlCl₃, FeCl₃, and MgCl₂). The experimental results showed that the average R_exp of the three coagulants was 39.12% ± 2.60%, 51.60% ± 2.88%, and 49.95% ± 3.17%. Subsequently, 42 molecular descriptors of dye molecules were calculated by quantitative calculation softwares Gaussian 09, Material Studio 7.0, and Multiwfn 3.7, and then QSAR models were constructed by a multiple linear regression (MLR) method for the three coagulation systems. The developed QSAR models demonstrated excellent stability, robustness, and predictability with values of R² = 0.7677, 0.8015, and 0.7035, Q²_INT = 0.6067, 0.7026, and 0.5898, Q²_EXT = 0.5505, 0.5076, and 0.5697, respectively. Based on the analysis of quantum parameters, the coagulation mechanism for AlCl₃, FeCl₃ is primarily electrostatic adsorption as well as hydrogen bonding, while MgCl₂ coagulates dyes mainly by electrostatic adsorption. This study provides an assessment of the removal rules and a feasible method for predicting dye removal rates in AlCl₃, FeCl₃, and MgCl₂ coagulation process.

A comprehensive review on the coagulant recovery and reuse from drinking water treatment sludge

The main treatment unit in conventional systems for surface water is coagulation-flocculation (CF) process, which consumes huge quantities of coagulant, and produces large volume of sludge. The produced sludge is known as one of the components of water treatment sludge (WTS), which is considered as a global issue and hot topic require careful attention from the plant operators and sludge managers to be managed sustainably with applying an ecofriendly method. Among the suggested technologies, recovery and reuse of coagulants from WTS show the potential to decrease the waste disposal and chemicals usage for drinking water treatment significantly. So, this comprehensive review provides a useful insight into environmental and health problems of WTS, reports the sources, physicochemical properties of sludge, describes different sludge management methods by more focus on coagulant recovery (CR), which significantly point out the different aspects of WTS recovery and reuse, and eventually, economic evaluation of the CR process was also discussed. The results of this review confirm that coagulants can be recovered from WTS by different methods and also will be reused for multiple times in the removal of pollutants from water and wastewater. Moreover, the recovered coagulants can be used as building and construction materials, constructed wetlands substrate and other aims.

Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA

Antibiotics are considered among the most non-biodegradable environmental contaminants due to their genetic resistance. Considering the importance of antibiotics removal, this study was aimed at multi-objective modeling and optimization of the Fenton-like process, homogeneous at initial circumneutral pH. Two main issues, including maximizing Ciprofloxacin (CIP) removal and minimizing sludge to iron ratio (SIR), were modeled by comparing central composite design (CCD) based on Response Surface Methodology (RSM) and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA). Results of simultaneous optimization using ethylene diamine tetraacetic acid (EDTA) revealed that at pH ≅ 7, optimal conditions for initial CIP concentration, Fe²⁺ concentration, [H₂O₂]/[Fe²⁺] molar ratio, initial EDTA concentration, and reaction time were 14.9 mg/L, 9.2 mM, 3.2, 0.6 mM, and 25 min, respectively. Under these optimal conditions, CIP removal and SIR were predicted at 85.2% and 2.24 (gr/M). In the next step, multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) were developed to model CIP and SIR. It was concluded that ANN, especially multilayer perceptron (MLP-ANN) has a decent performance in predicting response values. Additionally, multi-objective optimization of the process was performed using Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to maximize CIP removal efficiencies while minimizing SIR. NSGA-II optimization algorithm showed a reliable performance in the interaction between conflicting goals and yielded a better result than the GA algorithm. Finally, TOPSIS method with equal weights of the criteria was applied to choose the best alternative on the Pareto optimal solutions of the NSGA-II. Comparing the optimal values obtained by the multi-objective response surface optimization models (RSM-CCD) with the NSGA-II algorithm showed that the optimal variables in both models were close and, according to the absolute relative error criterion, possessed almost the same performance in the prediction of variables.

Performance evaluation of Fe-based water treatment sludge for dewatering of iron ore tailings slurry using coagulation-flocculation process: Optimization through response surface methodology

This research attempted to investigate the feasibility of using drinking water treatment sludge (ferric chloride sludge, FCS) as a coagulant for turbidity removal from iron ore tailings slurry. The evaluation was performed in two phases. In the first phase, the one factor at a time (OFAT) approach was used to study the effects of FCS dosage, initial pH, and initial turbidity on turbidity removal efficiency (TR%) and the volume of the sediment produced at the end of the process (SV). In the second phase, response surface methodology (RSM) was employed to assess the individual and interaction effects of the parameters on TR% and SV. Numerical multiple-response optimization was carried out using RSM to maximize TR% and minimize SV simultaneously. At optimum condition (FCS dose of 0.13 g dried FCS/L, initial pH of 10, and initial turbidity of 538 NTU), the removal of all particles in the range of 0.25-1 μm and 2-55 μm from slurry led to the TR% of 78.80% and SV of 0.74 mL (per 250 mL of tailings). Characterization tests indicated that at alkaline pH values, the higher presence of hydroxide compounds intensified the enmeshment in a precipitate or sweep-floc mechanism, which was the predominant removal mechanism in this work. This study demonstrated the remarkable performance of FCS as a coagulant in water reclamation from iron beneficiation wastewater.

Genetically modified metallothionein/cellulose composite material as an efficient and environmentally friendly biosorbent for Cd2+ removal

Metallothioneins (MTs) are a class of cysteine-rich metal-binding proteins. Cadmium (Cd) is one of the toxic heavy metal pollutants. In our previous research, the full-length cDNA of MT (Cd specificity) from freshwater crab (Sinopotamon henanense) (ShMT) was cloned and genetically modified to ShMT3 by site-directed mutagenesis to enhance the tolerance for Cd²⁺, however, it was limited in actual Cd²⁺ adsorption due to instability. Here, ShMT3-CBM, a novel recombinant fusion protein, was prepared. CBM is a carbohydrate binding module that can specifically bind cellulose while ShMT3 can effectively chelate Cd²⁺. The biosorbent Cellulose1-ShMT3-CBM was obtained by screening suitable cellulose materials. The selective adsorption experiments showed that Cellulose1-ShMT3-CBM had a preference for Cd²⁺. In low-concentration Cd²⁺ solutions, the removal efficiency was >99 %, and the adsorption equilibrium was reached within 15 min. The saturated adsorption capacity of Cellulose1-ShMT3-CBM for Cd²⁺ is 180.35 ± 4.67 mg/g (Dry Weight). Regeneration experiments showed that adsorption efficiency was maintained after six cycles. The MTT experiment showed that Cellulose1-ShMT3-CBM had low cytotoxicity. Meanwhile, Cellulose1-ShMT3-CBM can preferentially remove Cd²⁺ in actual water samples and boiler sewage. In this study, an environmentally friendly biosorbent which can adsorb Cd²⁺ efficiently and quickly was prepared for actual water treatment.

Bioremediation potential of Dicentrarchus labrax fish scales for dye-based emerging contaminants by ANN-GA hybrid modeling

This work investigates the possibility of using scales of sea bass Dicentrarchus labrax as a low-cost material for the adsorptive removal of methylene blue (MB) cationic dye in aqueous solutions. The physical-chemical characterizations of fish scales in natura (FS-in natura) revealed through thermogravimetry that they are composed of inorganic (hydroxyapatite) and organic (collagen) phases in relatively similar amounts. Spectroscopy analyses show that the interactions of MB with FS-in natura occur mainly in the organic phase layer of the adsorbent. The effects of initial MB concentration (5.0 × 10^-4 and 5.0 × 10^-3 mol L^-1) and temperature (25-55 °C) on the adsorption efficiency of FS-in natura were evaluated. FS-in natura at MB concentration (5.0 × 10^-3 and 5.0 × 10^-4 mol L^-1) exhibited the maximum adsorption capacities of 2.2 × 10^-3 mol g^-1 at 25 °C and 2.8 × 10^-5 mol g^-1 at 55 °C, respectively. The pseudo-second-order model represented the adsorption kinetics well, and the equilibrium isotherm data were better correlated using the Langmuir equation. The newly developed neural model demonstrated a high predictive capacity with an R-value greater than 0.99 and reduced values for mean squared error, root mean squared error, and mean absolute error equal to 0.003, 0.055, and 0.0348, respectively. The genetic algorithm was used to optimize the experimental conditions of the process. In conclusion, the sea bass scales have promising prospects as a low-cost alternative material for removing cationic dyes from aqueous solutions.

Predictive analysis of water resource carrying capacity based on system dynamics and improved fuzzy comprehensive evaluation method in Henan Province

The water resource carrying capacity (WRCC) is a carrying capacity of natural resources. It affects the application and expansion of the carrying capacity of water resources. This subject involves various elements, such as water resources, the ecological environment system, humans and their economic and social systems, and a wider range of biological groups and their survival needs. Based on the objective recognition of the complex relationship between the water resource system, ecological environment system, and economic and social system, the support scale of water resources and the ecological environment for economic and social development is studied. Current research on the carrying capacity of water resources has mostly shifted from the previously limited support capacity of water resources to include factors such as the population, economy, and ecology, establishing the internal relationships between the economics, water resources, and ecological environment. This reflects the comprehensive carrying capacity of the entire region (or river basin) of water resources and the ecological environment system on an overall economic and social scale. Based on the conceptual connotation of the WRCC and the actual problems facing water resources in Henan Province, the paper uses a system dynamics method to develop information feedback between the four subsystems of Henan Province: economic, population, water resource, and water environment subsystems. The index system of the WRCC in Henan Province is also determined. The weight of each index is comprehensively determined by a combination weighting method of the analytic hierarchy process and an entropy weight method, and then a fuzzy comprehensive evaluation method is used to evaluate the WRCC of Henan Province under four different development models. The validation period of the model is 2010-2020, and the forecast period is 2021-2030. The results indicate that during the period 2021-2030, the WRCC of Henan Province showed a slight upward trend overall under the four models, but the increase rates were different under the different models. Among the four models, the comprehensive model's benefit was the best, which not only maintained the healthy and stable development of the economy and society but also improved the pressure on the water resources and the quality of the water environment.

Sensitivity Analysis with the Monte Carlo Method and Prediction of Atenolol Removal Using Modified Multiwalled Carbon Nanotubes Based on the Response Surface Method: Isotherm and Kinetics Studies

Atenolol (ATN) is a β-blocker drug extensively used to treat arrhythmias and high blood pressure. Because the human body cannot metabolize it completely, this drug has been commonly found in many environmental matrices. In the present study, the response surface method (RSM) was used for adsorption prediction of ATN on modified multiwalled carbon nanotubes (M-MWCNTs) by NaOCl and ultrasonic. The sensitivity analysis was done by the Monte Carlo method. Sensitivity analysis was performed to determine the effective parameter by the Monte Carlo simulator. Statistical analysis of variance (ANOVA) was performed by using the nonlinear second-order model of RSM. The influential parameters including contact time (min), adsorbent dosage (g/L), pH, and the initial concentration (mg/L) of ATN were investigated, and optimal conditions were determined. Kinetic of ATN adsorption on M-MWCNTs was evaluated using pseudo-first, pseudo-second-order, and intraparticle diffusion models. Equilibrium isotherms for this system were analyzed by the ISOFIT software. As per our result, optimum conditions in the adsorption experiments were pH 7, 60 min of contact time, 0.5 mg/L ATN initial concentration, and 150 mg/L adsorbent dose. In terms of ATN removal efficiency, coefficients of R2 and adjusted R2 were 0.999 and 0.998, respectively. Sensitivity analysis also showed that contact time has the greatest effect on increasing the removal of ATN. Pseudo-first-order (R2 value of 0.99) was the best kinetic model for the adsorption of ATN, and for isotherm, BET (AICC value of 3.27) was the best model that fit the experimental data. According to the obtained results from sensitive analysis, time was the most important parameter, and after that, the adsorbent dose and pH affect positively on ATN removal efficiency. It can be concluded that the modified multiwalled carbon nanotubes can be applied as one of the best adsorbents to remove ATN from the aqueous solution.

Optimized O3/Fe(II) Using Response Surface Methodology for Organic Phosphorus Removal in Tetrakis(hydroxymethyl)phosphonium Sulfate Wastewater

Tetrakis(hydroxymethyl)phosphonium sulfate (THPS) wastewater is a kind of industrial wastewater which is difficult to biodegrade. In this work, O3/Fe(II) was used to remove organic phosphorus from THPS wastewater. The operating conditions in this process were optimized using the Box-Behnken response surface method based on single-factor experimentation. A response model of the organic phosphorus removal rate considering the initial pH, reaction time, ozone concentration, and Fe(II) dosage was established. The results showed that the ozone concentration and initial pH had a significant effect on the removal rate of organic phosphorus, and the model fit well (R2 = 0.98). The maximum removal rate of organic phosphorus predicted by this model was 86.04%, while the deviation between the predicted and experimental values was 0.91%. We concluded that the quadratic model was an effective tool for optimizing the removal of organic phosphorus in the THPS wastewater by O3/Fe(II).

Insight into ANN and RSM Models’ Predictive Performance for Mechanistic Aspects of Cr(VI) Uptake by Layered Double Hydroxide Nanocomposites from Water

Mathematical predictive models are vital tools for understanding of pollutant uptake during adsorptive water and wastewater treatment processes. In this study, applications of CoAl-LDH and its bentonite-CoAl intercalated LDH (bentonite-CoAl-LDH) for uptake of Cr(VI) from water were modeled using response surface methodology (RSM) and artificial neural network (ANN), and their performance for predicting equilibrium, thermodynamics and kinetics of the Cr(VI) uptake were assessed and compared based on coefficient of determination (R2) and root mean square error (RMSE). The uptake of Cr(VI) fits well quartic RSM polynomial models and ANN models based on Levenberg–Marquardt algorithms (ANN-LMA). Both models predicted a better fit for the Langmuir model compared to the Freundlich model for the Cr(VI) uptake. The predicted non-linear Langmuir model contestant (KL) values, for both the RSM and ANN-LMA models yielded better ΔG°, ΔH and ΔS predictions which supported the actual feasible, spontaneous and greater order of reaction as well as exothermic nature of Cr(VI) uptake onto the tested adsorbents. Employing the linear Langmuir model KL values dwindles the thermodynamic parameter predictions, especially for the RSM models. The excellent kinetic parameter predictions for the ANN-LMA models further indicate a mainly pseudo-second-order process, thus confirming the predominant chemisorption mechanism as established by the Cr(VI) speciation and surface charges for the Cr(VI) uptake by both CoAl-LDH and bentonite-CoAl-LDH. The ANN-LMA models showed consistent and insignificant decline in their predictions under different mechanistic studies carried out compared to the RSM models. This study demonstrates the high potential reliability of ANN-LMA models in capturing Cr(VI) adsorption data for LDHs nanocomposite heavy metal uptake in water and wastewater treatment.

Application of a combined response surface methodology (RSM)-artificial neural network (ANN) for multiple target optimization and prediction in a magnetic coagulation process for secondary effluent from municipal wastewater treatment plants

In this study, an enhanced coagulation-flocculant process incorporating magnetic powder was used to further treat the secondary effluent of domestic wastewater from a municipal wastewater treatment plant. The purpose of this work was to improve the discharged water quality to the surface water class IV standard of China. A novel approach using a combination of the response surface methodology and an artificial neural network (RSM-ANN) was used to optimize and predict the total phosphorus (TP) pollutant removal and turbidity. This work was first evaluated by RSM using the concentrations of coagulant, magnetic powder, and flocculant as the controllable operating variables to determine the optimal TP removal and turbidity. Next, an ANN model with a back-propagation algorithm was constructed from the RSM data along with the non-controllable variables, raw TP concentration, and raw water turbidity. Under the optimized experimental conditions (28.42 mg/L coagulant, 623 mg/L magnetic powder, and 0.18 mg/L flocculant), the TP and turbidity removal reached 88.79 ± 5.45% and 63.48 ± 9.60%, respectively, compared with 83.28% and 59.80%, predicted by the single RSM model, and 87.71 ± 5.74% and 64.62 ± 10.75%, predicted by the RSM-ANN model. The treated water were 0.17 ± 6.69% mg/L of TP and 2.46 ± 5.09% NTU of turbidity, respectively, which completely met the surface water class IV standard (TP < 0.3 mg/L; turbidity < 3 NTU). Therefore, this work demonstrated that the discharged water quality was completely improved using the magnetic coagulation process. In addition, the combined RSM-ANN approach could have potential application in municipal wastewater treatment plants.

Knowledge extraction of sonophotocatalytic treatment for acid blue 113 dye removal by artificial neural networks

Removing decolorizing acid blue 113 (AB113) dye from textile wastewater is challenging due to its high stability and resistance to removal. In this study, we used an artificial neural network (ANN) model to estimate the effect of five different variables on AB113 dye removal in the sonophotocatalytic process. The five variables considered were reaction time (5-25 min), pH (3-11), ZnO dosage (0.2-1.0 g/L), ultrasonic power (100-300 W/L), and persulphate dosage (0.2-3 mmol/L). The most effective model had a 5-7-1 architecture, with an average deviation of 0.44 and R² of 0.99. A sensitivity analysis was used to analyze the impact of different process variables on removal efficiency and to identify the most effective variable settings for maximum dye removal. Then, an imaginary sonophotocatalytic system was created to measure the quantitative impact of other process parameters on AB113 dye removal. The optimum process parameters for maximum AB 113 removal were identified as 6.2 pH, 25 min reaction time, 300 W/L ultrasonic power, 1.0 g/L ZnO dosage, and 2.54 mmol/L persulfate dosage. The model created was able to identify trends in dye removal and can contribute to future experiments.

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.

A highly efficient technique to simultaneously remove acidic and basic dyes using magnetic ion-exchange microbeads

The purpose of this study was to examine the combination of magnetic anion-exchange microbeads (MAM) and magnetic cation-exchange microbeads (MCM) to remove crystal violet (CV; a basic dye) and acid green 9 (AG9; an acidic dye) from their individual and combined solutions. Adsorption kinetics and isotherms experiments were performed in batch mode. CV and AG9 displayed superior affinity towards MCM and MAM, respectively, and their combined solution was efficiently adsorbed by combining MCM and MAM. The pseudo-first-order, pseudo-second-order, Elovich and intra-particle diffusion models well described the adsorption kinetic data, and the pseudo-second-order model appeared a better fit for the two-component CV/AG9 system. The better fit of the Langmuir isotherm for CV adsorption indicated that CV adsorption occurred on active sites with equal affinity in the monolayer. In contrast, AG9 adsorption onto the heterogeneous MAM surface appeared to be multilayered adsorption. The adsorption capacities of the two dyes decreased with the increase in the co-existing salt concentration and increased only slightly at the high salt level due to the salting-out effect. Moreover, these microbeads maintained most of their initial capacity during five reuse cycles, indicating the great potential of MCM and MAM to remove basic and acidic dyes in practical applications.

Deciphering the adsorption potential of a functionalized green hydrogel nanocomposite for aspartame from aqueous phase

Herein, a functionalized green hydrogel nanocomposite based on carboxymethylated gum tragacanth and nanobentonite (GTBCH) was designed via free-radical polymerization approach for the elimination of Aspartame (AS) from wastewater. The GTBCH fabrication was validated by Fourier Transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Transmission electron microscopy (TEM), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX) techniques. Central composite design (CCD) was efficaciously applied to determine the quadratic polynomial approach for predicting the adsorption capacity (q_e) of AS. The optimum sequestration conditions were dosage (0.8 g L^‒1), agitation time (35 min) initial AS concentration (60 mg L^-1), pH (6) and temperature (308 K). The CCD results revealed that dosage of GTBCH and initial concentration have greater impact on q_e followed by pH, time, and temperature. The significant adsorption capacity (392.04 mg g^-1), calculated from Langmuir model, could be attributed to the stronger interactions prevalent between AS and GTBCH. Diffusion investigations depicted the uptake of AS via surface adsorption, liquid film and intraparticle diffusion, respectively. Ionic strength and real water have minor effect on the adsorption capacity demonstrating electrostatic interaction has least impact in adsorption process. The pHzpc, FTIR and XPS investigations revealed hydrogen bonding, n-π and van der Waals interactions as the principal removal mechanisms. Robust design, high adsorption capacity, eco-friendly facets along with excellent reusability indicated the GTBCH as a competent adsorbent for AS decontamination from wastewater.

Applications of Computational and Statistical Models for Optimizing the Electrochemical Removal of Cephalexin Antibiotic from Water

One of the most serious effects of micropollutants in the environment is biological magnification, which causes adverse effects on humans and the ecosystem. Among all of the micro-pollutants, antibiotics are commonly present in the aquatic environment due to their wide use in treating or preventing various diseases and infections for humans, plants, and animals. Therefore, an aluminum-based electrocoagulation unit has been used in this study to remove cephalexin antibiotics, as a model of the antibiotics, from water. Computational and statistical models were used to optimize the effects of key parameters on the electrochemical removal of cephalexin, including the initial cephalexin concentration (15–55 mg/L), initial pH (3–11), electrolysis time (20–40 min), and electrode type (insulated and non-insulated). The response surface methodology-central composite design (RSM-CCD) was used to investigate the dependency of the studied variables, while the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied for predicting the experimental training data. The results showed that the best experimental and predicted removals of cephalexin (CEX) were 88.21% and 93.87%, respectively, which were obtained at a pH of 6.14 and electrolysis time of 34.26 min. The results also showed that the ANFIS model predicts and interprets the experimental results better than the ANN and RSM-CCD models. Sensitivity analysis using the Garson method showed the comparative significance of the variables as follows: pH (30%) > electrode type (27%) > initial CEX concentration (24%) > electrolysis time (19%).

Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping

Waste streams with high ammonia nitrogen (NH3-N) concentrations are very commonly produced due to human intervention and often end up in waterbodies with effluent discharge. The removal of NH3-N from wastewater is therefore of utmost importance to alleviate water quality issues including eutrophication and fouling. In the present study, vacuum thermal stripping of NH3-N from high strength synthetic wastewater was conducted using a rotary evaporator and the process was optimized and modeled using response surface methodology (RSM) and RSM–artificial neural network (ANN) approaches. RSM was first employed to evaluate the process performance using three independent variables, namely pH, temperature (°C) and stripping time (min), and the optimal conditions for NH3-N removal (response) were determined. Later, the obtained data from the designed experiments of RSM were used to train the ANN for predicting the responses. NH3-N removal was found to be 97.84 ± 1.86% under the optimal conditions (pH: 9.6, temperature: 65.5 °C, and stripping time: 59.6 min) and was in good agreement with the values predicted by RSM and RSM–ANN models. A statistical comparison between the models revealed the better predictability of RSM–ANN than that of the RSM. To the best of our knowledge, this is the first attempt comparing the RSM and RSM–ANN in vacuum thermal stripping of NH3-N from wastewater. The findings of this study can therefore be useful in designing and carrying out the vacuum thermal stripping process for efficient removal of NH3-N from wastewater under different operating conditions.

Optimization of the electrochemical oxidation of textile wastewater by graphite electrodes by response surface methodology and artificial neural network

In this study, electrochemical oxidation of combed fabric dyeing wastewater was investigated using graphite electrodes. The response surface methodology (RSM) was used to design the experiments via the central composite design (CCD). The planned experiments were done to track color changes and chemical oxygen demand (COD) removal. The experimental results were used to develop optimization models using RSM and the artificial neural network (ANN) and they were compared. The developed models by the two methods were in good agreement with the experimental results. The optimum conditions were found at 150 A/m², pH 5, and 120 min. The removal efficiencies for color and COD reached 96.6% and 77.69%, respectively. The operating cost at the optimum conditions was also estimated. The energy and the cost of 1 m³ of wastewater required 34.9 kWh and 2.58 US$, respectively. The graphite electrodes can be successfully utilized for treatment of combed fabric dyeing wastewater with reasonable cost.

A multilayer perceptron-based prediction of ammonium adsorption on zeolite from landfill leachate: Batch and column studies

In this study, multilayer perceptron (MLP) artificial neural network was used to predict the adsorption rate of ammonium on zeolite. pH, inlet ammonium concentration, contact time, temperature, dosage of adsorbent, agitation speed, and particle size in the batch experiments were used as independent variables while flow rate and particle size in column mode were investigated. In MLP application, different architecture structures were tried and the architecture structures with the highest predictive performance were determined. To comparatively evaluate the predictive capabilities of MLP based prediction tool, Response Surface Methodology (RSM) was utilized. When the results were evaluated with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values (<1%) for almost all experiments, it was seen that MLP-based prediction tool produces better predictions than RSM. The scatter plots showed that predictions and actual values were quite compatible. Both regression and determination coefficients were interpreted by creating a regression of the predictions against the actual values and these coefficients were obtained as pretty close to 1. The outstanding performance of MLP in out-of-sample data sets without the need for additional experiment demonstrate that MLP can be effectively and reliably used in cases where experimental setups are difficult or costly.

Adsorption of pyridine from aqueous solutions onto polyaluminium chloride and anionic polyacrylamide water treatment residuals

The adsorption performance of pyridine onto polyaluminium chloride (PAC) and anionic polyacrylamide (APAM) water treatment residuals (WTRs) was investigated by batch experiments. This study confirmed the assumption that PAC-APAM WTRs had the ability to remove pyridine. The non-linear Dubinin-Radushkevich model and non-linear Freundlich model better described the isotherms, indicating that the adsorption was a chemically controlled multilayer process. The pyridine adsorption rate was simultaneously controlled by external film diffusion and intraparticle diffusion. The adsorption of pyridine was an endothermic reaction with randomness increase. The pyridine adsorption decreased with pH increase. Pyridine removal was observed to be a linear increase from 6.16% to 96.18%, with the increase of dosage from 2.5 g/L to 15 g/L. The Langmuir maximum adsorption capacity was 3.605 mg/g while the theoretical isotherm saturation capacity was 9.823 mg/g. Therefore, PAC-APAM WTRs recycled into contaminated soils for remediation is expected to be an innovative alternative disposal method. More research is recommended in the future to identify detailed adsorption mechanisms and the most appropriate mixing ratio of PAC-APAM WTRs to contaminated soils under various climatic conditions.