Prediction Of Removal Efficiency Research Articles

Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose.

This study explores nitrate reduction in aqueous solutions using carboxymethyl cellulose loaded with zero-valent iron nanoparticles (Fe0-CMC). The structures of this nano-composite were characterized using various techniques. Based on the characterization results, the specific surface area of Fe0-CMC measured by the Brunauer-Emmett-Teller analysis were 39.6m2/g. In addition, Scanning Electron Microscopy images displayed that spherical nano zero-valent iron particles (nZVI) with an average particle diameter of 80nm are surrounded by carboxymethyl cellulose and no noticeable aggregates were detected. Batch experiments assessed Fe0-CMC's effectiveness in nitrate removal under diverse conditions including different adsorbent dosages (Cs, 2-10mg/L), contact time (t, 10-1440min), initial pH (pHi, 2-10), temperature (T, 10-55°C), and initial concentration of nitrate (C0, 10-500mg/L). Results indicated decreased removal with higher initial pHi and C0, while increased Cs and T enhanced removal. The study of nitrate removal mechanism by Fe0-CMC revealed that the redox reaction between immobilized nZVI on the CMC surface and nitrate ions was responsible for nitrate removal, and the main product of this reaction was ammonium, which was subsequently completely removed by the synthesized nanocomposite. In addition, a stable deviation quantum particle swarm optimization algorithm (SD-QPSO) and a least square error method were employed to train the ANFIS parameters. To demonstrate model performance, a quadratic polynomial function was proposed to display the performance of the SD-QPSO algorithm in which the constant parameters were optimized through the SD-QPSO algorithm. Sensitivity analysis was conducted on the proposed quadratic polynomial function by adding a constant deviation and removing each input using two different strategies. According to the sensitivity analysis, the predicted removal efficiency was most sensitive to changes in pHi, followed by Cs, T, C0, and t. The obtained results underscore the potential of the ANFIS model (R2 = 0.99803, RMSE = 0.9888), and polynomial function (R2 = 0.998256, RMSE = 1.7532) as accurate and efficient alternatives to time-consuming laboratory measurements for assessing nitrate removal efficiency. These models can offer rapid insights and predictions regarding the impact of various factors on the process, saving both time and resources.

Predicting removal efficiency of organic pollutants by soil vapor extraction based on an optimized machine learning method

Soil vapor extraction (SVE) was a cost-effective technology for remediating volatile and semi-volatile organic contaminated soils. Many factors, including SVE parameters, soil properties, and contaminant characteristics, significantly influenced the remediation efficiency of SVE. The optimal conditions for organic pollutants removal efficiency were site-specific and varied among studies. Therefore, a generalized model was needed to predict the remediation efficiency of SVE in organic contaminated soils. This study employed machine learning to predict the removal efficiency of organic pollutants by SVE. The model's development was based on a trainset, and its predictive capabilities were evaluated using a testset. An XGBoost (XGB) model was derived from literature data (R2 = 0.9728). Time, pollutant type, and temperature were identified as the three most important features affecting SVE remediation efficiency. The accuracy (R2 = 0.9799) and universality of the model were enhanced through an optimization scheme. The developed XGB model demonstrated the ability to predict the removal efficiency of organic pollutants by considering all collected influential factors. The mechanism of multi-factor interaction on remediation efficiency was clarified. Overall, this study would contribute to evaluating the remediation potential of SVE for specific organic contaminated soils, aiding in maximizing the removal efficiency of organic pollutants under optimal conditions.

Custom and definitive screening designs for evaluating multiple adsorbents: bisphenol-A adsorption onto villi-structured polyaniline/carboxymethyl cellulose composites

ABSTRACT Polyaniline composites consisting of carboxymethyl cellulose (CMC) have enhanced adsorption properties, but recent studies indicate that the oxidised species – dialdehyde carboxymethyl cellulose (DCMC) – outperforms CMC-based composites. However, these studies fail to study the effect of DCMC's aldehyde content and compare the composites with CMC-based composites; numerous experiments required to investigate each adsorbent for each factor limit such studies. We explored a way to study whether villi-structured polyaniline (VSPANI), its CMC composite (CMC/PANI), and its DCMC composites with 35% (DCMC(A)/PANI) and 77% (DCMC(B)/PANI) aldehyde content would be great adsorbents for removing bisphenol-A (BPA). We first customised a D-optimal screening design to alleviate the pitfalls of definitive screening design (DSD), hence estimating all the main effects: initial concentration, pH, flow rate, adsorbent amount, sample volume and type of adsorbent. We excluded CMC/PANI and DCMC(A)/PANI composites, both with low adsorption capacities of 56.57 and 57.27 mg/g from further investigation. The DSD followed to estimate all second-order effects through which we projected a response surface method (RSM) to optimise and model the active factors. Increasing the aldehyde content on the composites favoured adsorption, but there lacked evidence to suggest VSPANI and DCMC(B)/PANI differed significantly in performance. The models were numerically and graphically proven adequate, explaining 80% and 99% of the variation when predicting removal efficiency and adsorption capacity. VSPANI showed potential as an adsorbent for BPA removal with 85% removal efficiency and 129 mg/g adsorption capacity. This comprehensive approach, combining both designs, allows for sustainable investigation of multiple adsorbents and factors, minimising experimental waste.

Smart Strategic Management for the Cold Plasma Process Using ORP Monitoring and Total Organic Carbon Correlation

Assessing oxidation–reduction potential (ORP) is of paramount importance in the efficient management of wastewater within both chemical and biological treatment processes. However, despite its critical role, insufficient information exists about how reactive chemical species generated by cold plasma (CP) in chemical treatment are associated with ORP and air flow rate. Therefore, we aim to identify the correlation between ORP and the removal of organic pollutants when using CP treatment. Additionally, we introduce a machine-learning-based operation to predict removal efficiency in the CP process. Results reveal a significant correlation of over 0.9 between real-time ORP and total organic carbon (TOC), which underscores the efficacy of ORP as a key parameter. This approach made it possible to control OH radical generation by regulating the air flow rate of the CP. This study posits that smart management facilitated by machine learning has the potential to enhance the economic viability of CP feasibility while maintaining overall treatment performance.

Age-related differences in the removal of information from working memory

Removal has been assumed to be a core mechanism in working memory. However, it remains unclear whether children can actively remove outdated information from working memory and how this ability develops as children age. The current study aimed to examine age-related differences in removal ability and its relations with cognitive control and working memory capacity. Children aged 7, 9, and 11 years performed a modified working memory updating task assessing removal efficiency. In addition, a battery of cognitive control and working memory capacity tasks was administered. Results indicated that updating response times decreased considerably when a longer time was given for removal, suggesting that children aged 7 to 11 years can actively remove outdated items from working memory prior to encoding the new ones and that removal efficiency increased with age. More important, age-related increases in removal efficiency occurred concurrently with the development of working memory capacity. Proactive control predicted removal efficiency over and beyond age and working memory capacity. The findings shed new light on the mechanisms underlying the development of working memory updating.

Prediction of doxycycline removal by photo-fenton process using an artificial neural network - multilayer perceptron model

This paper presents a study on the effectiveness of the Photo-Fenton Process (PF) for removing the doxycycline hyclate (DXC) antibiotic. The experiment showed that the best removal efficiency was achieved (79%) at pH 3 for 2.5 mg/L of DXC, 76.53 mg/L of H2O2, and 86.8 mg/L of Fe2+. The degradation mechanism of DXC by hydroxyl radicals was confirmed by FTIR and HPLC. To model the oxidation reaction of DXC by PF, an multilayer perceptron (MLP) based optimized artificial neural network (OANN) was used, taking into account experimental data such as pH and initial concentrations of DXC, H2O2, and Fe2+. The OANNN predicted removal efficiency results were in close agreement with experimental results, with an RMSE of 0.0661 and an R2 value of 0.99998. The sensitivity analysis revealed that all studied inputs significantly impacted the transformation of DXC.

Removal of Reactive Black 5 from simulated textile effluents by an electrocoagulation process: optimization by response surface methodology

Abstract The textile industry creates one-fifth of the world's industrial water pollution, thus, the electrocoagulation (EC) process was proposed and investigated as an alternative eco-friendly treatment for water reuse. This study aimed to assess the removal efficiency of the Reactive Black 5 (RB5) from synthetic textile effluent by EC. Key operating parameters on EC process efficiency were optimized using response surface methodology (RSM). The main independent variables studied were the current density, EC time, concentration, and pH while the RB5 dye removal was studied as the dependent variable. The range of the studied parameters affecting the EC process ranged from 10 to 60 mA/cm2, 5 to 30 min, 4 to 10 pH and 10 to 40 ppm for current density, EC time, pH, and RB5 concentration, respectively. The optimal operating parameters turned out to be 5.5 pH, 47.5 mA/cm2, 23.75 min, 17.5 ppm, and the predicted RB5 dye removal was 96.33%. The experimental dye removal with optimum operating conditions was in good agreement with the predicted removal efficiency. Therefore, the experiment results revealed the high potential of the EC process to effectively treat textile industry effluents and the RB5 dye removal was successfully optimized using Response Surface Methodology (RSM).

Modeling and optimization of advanced oxidation treatment of dexamethasone from aquatic solutions using electro-peroxone/ultrasonic process: Application for real wastewater, electrical energy consumption and degradation pathway

Dexamethasone (DXM) is a widely used anti-inflammatory corticosteroid that is frequently detected in the water bodies due to its immense consumption and incomplete removal during wastewater treatment. The potential eco-toxicity of pharmaceutical pollutants and their insufficient removal in conventional wastewater treatments have provoked researchers to investigate and develop novel and highly efficient treatment systems. The present study aimed to develop a synergistic advanced oxidation system based on electro-peroxone coupled with ultrasonic irradiation (EP/US process) for the degradation of dexamethasone (DXM) from water medium and an EP/US process assisted with granular activated carbon (GAC) for the remediation of real pharmaceutical wastewater. The effects of various experimental parameters such as pH, operating time, US power, applied current, and supporting electrolyte (Na2SO4) were systematically investigated by using response surface methodology based on central composite design (RSM-CCD), and results established that the complete degradation of DEX was attained at pH 9, operating time 25 min, US power 300 W/L, applied current 420 mA, and supporting electrolyte 0.05 M. The results of analysis of variance and regression analysis revealed that the second-order polynomial equation is more suitable (R2 = 0.9981 and p-value = 0.0004) to predict removal efficiency and describe the oxidation process. Under optimized experimental condition, the integrated EP/US process provided synergy and lessenergyconsumption compared to individual EP and US processes. The removal efficiency of TOC and COD reached 44.2% and 70.8% after 25 min reaction and increased to 78.7 % and 94.4%, respectively, as contact time increased to 50 min. Also, average oxidation state (AOS) and carbon oxidation state (COS) values increased from 0.22 to 3.28 and 3.78, respectively. Scavenger tests disclosed that HO• radical is the predominant reactive free radical generated in the EP/US system, and LC-MS analysis indicated that different intermediates are produced during DEX decomposition. The EP/US system increased the BOD/COD ratio of real pharmaceutical wastewater from 0.27 to 0.5 during 100 min treatment time, while in the presence of GAC, the EP/US process showed better results. Overall, the present study highlights that the coupling of EP/US processes noticeably improves the DEX abatement rate from water medium, and GAC can improve the efficiency of this process for pharmaceutical wastewater treatment. The findings of the present study propose the EP/US and EP/US/GAC systems as promising efficient alternatives for treating persistentpharmaceutical wastewater.

Visible light-driven ZnO/Fe3O4 magnetic nanoparticles for detoxification of diazinon: the photocatalytic optimization process with RSM-BBD model.

Today, diazinon is one of the most widely used organophosphorus pesticides, whose widespread use can cause many ecological and biological risks. In this research, a magnetic ZnO/Fe3O4 nanoparticle was used to investigate the photocatalytic degradation of diazinon. Sol-gel synthesis was used to create the nanoparticle, which was then characterized using XRD, FTIR, FESEM, VSM, and XPS techniques. The design of photocatalytic degradation experiments was done using the response surface method and the Box-Behnken design model. The investigated parameters include pH, nanoparticle concentration, diazinon concentration, and irradiation time. The characterization of the ZnO/Fe3O4 nanoparticle showed well-formed crystalline phases and a cubic spinel structure. Additionally, the shape of the nanoparticle is almost uniform and spherical. The FT-IR spectrum also confirmed the presence of all functional groups related to ZnO and Fe3O4 in the ZnO/Fe3O4 nanoparticles structure. The synthesized nanocomposite has superparamagnetic properties and a very small coercive field, making it easily recyclable, according to a VSM analysis. XPS results also showed the presence of Fe (Fe 2p1/2 and Fe 2p3/2), Zn (Zn 2p1/2 and Zn 2p3/2), oxygen (O1s), and weak carbon (C1s) peaks in the ZnO/Fe3O4 structure. The results of the photocatalytic optimization experiments showed that the highest efficiency of diazinon toxin degradation is 99.3% under the conditions of pH 7, diazinon initial concentration of 10 mg/L, nanoparticle concentration of 1 g/L, and a contact time of 90 min. This result is very close to the BBD model's predicted removal efficiency under optimal conditions (100%). As a result, the ZnO/Fe3O4 nanocomposite can produce active free radicals through UV radiation, and these radicals can successfully remove diazinon under actual conditions.

Preparation of sodium zinc phosphate/polyaniline nanocomposite for Cr(VI) and eriochrome black T adsorption from water

In this study, ultrasound physical mixing of polyaniline (PAni) and sodium zinc phosphate (NaZnP) materials resulted in the synthesis of a new nanocomposite (NaZnP/PAni). Samples were physicochemically characterized via different analytical techniques. The X-ray diffraction pattern indicated the synthesis of hydrated sodium zinc phosphate (Na6(ZnPO4)6.8 H2O) with an average crystallite size of 40.8 nm. After physical mixing with PAni, the crystallite size of the resulting composite was reduced to 36 nm. Scanning electron microscopy (SEM) revealed that the nanocomposite was formed by the deposition of NaZnP nanospheres over PAni nanorods. The adsorption efficiency of the composite was monitored using an azo dye (eriochrome black T (EB)) and hexavalent chromium (Cr(VI)). The optimization of the effects of operating parameters (pH, NaZnP/PAni dosage, starting pollutants concentration, and reaction time) on the removal efficiency were examined using the central composite design (CCD). The predicted removal efficiencies under the optimum conditions were 93 % and 98 % for EB and Cr(VI), respectively. The adsorption kinetics modeling reveals that the pseudo-second-order describes the uptake of Cr(VI) and EB. Equilibrium data fitted well with the Redlich-Peterson and Sips isotherm models. The highest retention capacity of NaZnP/PAni was 234 mg/g for EB and 231 mg/g for Cr(VI). Consequently, the developed NaZnP/PAni nanocomposite could be applied to remove heavy metals and dyes from wastewater with great efficiency.

Preparation and Characterization of nZVI, Bimetallic Fe0-Cu, and Fava Bean Activated Carbon-Supported Bimetallic AC-F e0-Cu for Anionic Methyl Orange Dye Removal

Nano zero-valent iron (nZVI), bimetallic Nano zero-valent iron-copper (Fe0- Cu), and fava bean activated carbon-supported with bimetallic Nano zero-valent iron-copper (AC-F e0-Cu) were prepared and characterized by DLS, FT-IR, XRD, and SEM. The influence of the synthesized adsorbents on the adsorption and removal of soluble anionic methyl orange (M.O) dye was investigated using UV-V spectroscopy. The influence of numerous operational parameters was studied at varied pH (3–9), time intervals (15–180 min), and dye concentrations (25–1000 ppm) to establish the best removal conditions. The maximum removal efficiency of M.O. using the prepared adsorbent materials reached about 99%. The removal efficiency is modeled using response surface methodology (RSM). The Bimetallic Fe0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F{e}^{0}$$\\end{document}-Cu, the best experimental and predicted removal efficiency is 96.8% RE. For the H2SO4 chemical AC-Fe0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F{e}^{0}$$\\end{document}-Cu, the best experimental and removal efficiency is 96.25% RE. The commercial AC-Fe0–Cu, the best experimental and predicted removal efficiency is 94.93%RE. This study aims to produce low-cost adsorbents such as Bimetallic Fe0-Cu, and Fava Bean Activated Carbon-Supported Bimetallic AC-Fe0-Cu to treat the industrial wastewater from the anionic methyl orange (M.O) dye and illustrate its ability to compete H2SO4 chemical AC-Fe0-Cu, and commercial AC-Fe0-Cu.

Low-Energy Electrodeposition of Nickel in Electroplating Wastewater Using a Water Hyacinth Separated Double Chamber Electrodeposition Cell

Nickel ions are highly present in wastewater from the electroplating industry because the galvanizing process only uses 30%–40% of nickel. The objectives of this study are to investigate the effect of electrolyte support, voltage, anolyte solution, and anode type on removal efficiency, current efficiency, and specific energy on water hyacinth-separated double chamber electrodeposition cell (DCEC) and to simulate wastewater treatment based on the optimized variables. We optimized the reaction conditions using artificial wastewater containing 2200 mg l−1 of nickel. We then simulated the electrodeposition using the optimized conditions and obtained the optimal operating conditions: 8 h of process time, a voltage of 3.48 V, and NaCl and H2SO4 concentrations of 1.5 and 1.4 M, respectively. At these optimum conditions, the predicted removal efficiency was found to be 68.73%. The current efficiency and specific energy were relatively low which was a similar efficiency of 28%. The reaction rate constants at reaction orders 1 and 2 in the electroplating industrial waste electrodeposition process were 0.1324 s-1 (R2 = 0.846) and 0.0001 s-1 (R2 = 0.913), respectively. This study contributes to further research and development of electrodeposition as a promising technology for the removal and recovery of toxic metal ions from wastewater with affordable m.

Experimental investigation of methyl-orange removal using eco-friendly cost-effective materials raw fava bean peels and their formulated physical, and chemically activated carbon

The discharge of effluents from dye industries into water streams poses a significant environmental and public health risk. In response, eco-friendly adsorbents derived from agricultural waste, such as Fava Bean Peels (R–FBP), have been investigated as potential materials for the removal of such pollutants. In this study, R–FBP and their corresponding physical and chemically activated carbon (P-RFB-AC and C-FBP-AC) were synthesized using H3PO4 acid and characterized using FT-IR, and SEM analyses. An optimization process was conducted to determine the optimum conditions for achieving high Methyl Orange (M. Orange) removal efficiencies using the prepared materials, namely R–FBP, P-RFB-AC, and C-FBP-AC. The adsorption mechanism was examined by analyzing the isotherm and kinetics. The results revealed that the physical raw-activated carbon exhibited the highest removal efficiency of 96.8% compared to other materials. This outcome was achieved through the use of ANN combined with Moth Search Algorithm (MSA), which was found to be the most effective model for achieving the highest M. Orange removal efficiency from Physical raw fava bean activated carbon. Under parameters of 1000 mg/l M. Orange concentration, 2 g/l dose, 15 min contact time, and 120 rpm shaking, the best experimental and predicted removal efficiencies for physical-activated carbon fava bean rind were 96.8 RE%, 96.01 indicated RSM RE%, and 95.75 predicted ANN RE%. The highest experimental and predicted removal efficiencies for the H3PO4 chemical activated carbon fava bean peel were 94%RE. This study aimed to develop an economical solution for treating industrial wastewater contaminated with anionic M. Orange dye using raw fava bean peel and their generated activated carbon, in both physical and chemical forms. The Temkin and Langmuir isotherm models were found to best fit the data for raw fava bean peel, while Temkin agreed well with the data from physical-activated carbon. Temkin and Freundlich's models were fitted with the H3PO4 chemical activated carbon. Pseudo-second-order kinetics was identified as the most suitable model for both physically and chemically activated carbons. Future research may explore the capacity of the produced activated carbon-based algae to extract a wider range of contaminants from contaminated wastewater. In summary, this work contributes to the development of eco-friendly and cost-effective methods for removing dyes, specifically M. Orange, from industrial effluents. By synthesizing and characterizing R–FBP and their relative activated carbon, the adsorption mechanism was studied, and the optimum conditions for achieving high M. Orange removal efficiencies were determined. The results showed that physical raw-activated carbon exhibited the highest removal efficiency, and pseudo-second-order kinetics was the most suitable model for both physically and chemically activated carbon.

Mathematical model evaluating for degradation of azo dye in contaminated water by UV/persulfate process

AbstractIn the present study, the photooxidative degradation of Acid Red 14 (AR 14) was investigated in the UV/persulfate process, and the results of the degradation rate of AR 14 were parametrically represented by ordinary differential equations to find mathematical model for the degradation rate of AR 14 in this process. Our experimental observations led to a model for AR 14 degradation in the UV/persulfate system that could be used to predict removal efficiency by changes of S2O82− (X1) and dye concentration (X2), pH (X3), temperature (X4) and also distance of UV lamp from solution (X5). It was found that persulfate and dye concentrations were the most important parameters for AR 14 degradation rate. Moreover, the results showed that the degradation rate was in good agreement with the first‐order kinetics for all the parameter values studied. Moreover, the results of the mathematical model agree well with the experimental values (R2 = 0.96). Our findings in this study showed that degradation efficiency of UV/S2O82− process for AR 14 was obtained as 98%. Therefore, this model could be applicable before scaling up the AR 14 degradation using UV/persulfate process.

Treatment of petroleum wastewater by electrocoagulation using scrap perforated (Fe-anode) and plate (Al and Fe-cathode) metals: Optimization of operating parameters by RSM

This study focused on removing chemical oxygen demand and phenol from petroleum wastewater by employing the electrocoagulation (EC) process using scrap electrodes. In the study, in a unique way different from those reported in the literature, scrap cylindrical perforated iron (PS) and plate metals discarded by different industries were used as electrodes by recovery for EC. PS was used the anode, and plate electrodes made of two different materials (Al and Fe) were used as the cathodes. The electrodes were placed at the center of the EC reactor and PS. The effects of the main parameters, namely, initial pH (4−10), initial exposure time (5–45 min), and current density (2–28 mA/cm2), on the EC process were assessed and optimized by employing response surface methodology (RSM). A reasonable correlation between the experimental and predicted data was found through analysis of variance (ANOVA). The ANOVA results revealed that the coefficients of determination (R2) for COD removal and phenol removal were 91.18% and 91.46%, respectively. The maximum predicted removal efficiency rates were 96.18% and 94.02% for respectively COD and phenol under the optimum conditions of pH= 6.30, current density= 22 mA/cm2, exposure time= 39 min, and the Fe cathode. Furthermore, the results showed that the initial current density and exposure time were significantly effective on COD and phenol removal from petroleum wastewater. The total cost was calculated as $3.1/m3. There was no electrode cost in the study due to the usage of scrap metals. The results indicated that the EC process is an effective treatment method for COD and phenol removal from petroleum wastewater using scrap metals as electrodes.

Response surface modeling for the treatment of methylene blue from aqueous media using electro-Fenton process before determination by UV-VIS spectrometer: Kinetic and degradation mechanism

In the present study, response surface methodology was employed to investigate the effects of main variables, including the initial MB concentration, hydrogen peroxide dosage, current density, and electrolysis time on the removal efficiency of MB using the electro-Fenton (EF) process. The MB concentration determination by UV-VIS spectrometer. The EF process degrades the MB contaminant molecules by the highly oxidizing species of the •OH. A quadratic regression model was developed to predict the removal of MB, where the R2 value was found to be 0.9970, which indicates the satisfactory accuracy of the proposed model. ANOVA analysis showed a non-significant lack of fit value (0.0840). Moreover, the predicted correlation coefficient values (R2=0.9915) were reasonably in agreement with the adjusted correlation coefficient value (R2=0.9958), demonstrating a highly significant model for MB dye removal. In addition, the obtained results showed 95.8% MB was removed in the optimum removal efficiency, including the initial MB concentration of 20 mg L-1, H2O2 dosage of 400 μL, and the current density of 7.0 mA cm-2, and electrolysis time of 10 min which was agreed with the predicted removal efficiency of 98.3%. Electrical energy consumption was found to be 0.163 kWh m-3.

Photocatalytic degradation of bisphenol a from aqueous solution using bismuth ferric magnetic nanoparticle: synthesis, characterization and response surface methodology-central composite design modeling.

Bisphenol A (BPA), as endocrine-disrupting compound (EDC), is extensively used as an important chemical in the synthesis of polycarbonate polymers and epoxy resins. BPA absorption into the body can result in the development of metabolic disorders such as low sex-specific neurodevelopment, immune toxicity, neurotoxicity and interference of cellular pathway. Therefore, the presence of BPA in the body and the environment can create hazards that must reach standards before being discharged into the environment. In this study, bismuth ferric nanomagnetic (BFO NMPs) were successfully synthesized via sol-gel method and developed as photocatalysts for BPA removal under visible light irradiation. FE-SEM, TEM, PL, XRD, UV-Vis DRS, VSM, EDX, and FTIR were used to characterize the BFO NMPs. RSM model (R2 = 0.9745) showed a good correlation between experimental and predicted removal efficiency of BPA. The investigation of four independent variables indicated that pH had the most significant positive effect on the degradation of BPA. Under optimal conditions (pH = 4.042, catalyst dose = 7.617mg, contact time = 122.742min and BPA concentration = 15.065mg/L), maximum degradation was calculated to be 98.7%. After five recycles, the removal of BPA remained >82%, which indicated the proper ability to reuse the catalyst. In conclusion, it can be stated like BPA, the prepared BFO NMPs is a promising photocatalyst for practical application in organic pollutant decomposition.

Modelling Metribuzin Removal Efficiency Through Adsorption Using Activated Carbon of Olive-waste Cake

A simple mathematical model is developed for the prediction of Metribuzin removal efficiency through adsorption using activated carbon of olive-waste cake for any combination of input conditions. Based on earlier experimental results, factors from three independent variables (pH, initial Metribuzin concentration and adsorbent dose concentration) were incorporated in the model. All the factors are multiplied to derive a combined diminishing factor, which is multiplied with maximum achievable removal efficiency. It is found that although the model results are having good correlation (0.92) with the experimental results, those are slightly away from the ideal line. Through the introduction of an adjustment factor, model predictions are closely matching with the measured values having a correlation coefficient of 0.96. The primary model predicted results are having standard errors as RMSE = 6.34, MAE = 5.99 and RAE = 0.07, whereas the same error statistics of the adjusted model are 1.97, 1.71 and 0.01, respectively. Such modelling technique will predict removal efficiency for any combination of input parameters, which at times are required to be changed for other constraints.

A green approach to remove acetamiprid insecticide using pistachio shell-based modified activated carbon; economical groundwater treatment

This study deals with an economical and green approach for the successful removal of acetamiprid (ATP) from groundwater samples using pistachio shell-based modified activated carbon (MAC). The prepared MAC has an apparent surface area (SBET) of 1158.7 m2 g−1, total pore (Vtotal) of 0.982 cm3 g−1, and micropore (Vmicro) volumes of 0.841 cm3 g−1, and pore diameter (Pdia) of 11.18 nm. The maximum removal efficiency of 98.6% was obtained at the optimal condition of the ATP concentration of 10 mg L−1, solution pH of 6.5, the contact time of 23 min, and adsorbent dosage of 300 mg L−1. It was in good accordance with the predicted removal efficiency of 99.3% by the developed model. According to the analysis of variance (ANOVA), the high F-value of 797.5 and the very low P-value of <0.0001 confirm that the proposed model is highly significant. The ATP concentration of the groundwater sample was reduced to zero after applying the proposed process. The obtained results confirmed that modifying the activated carbon by ferric chloride increased the surface area, total pore volume, and the adsorption capacity as compared to the commercial activated carbon (CAC) and raw activated carbon (RAC). ATP adsorption followed the pseudo-second-order kinetics model with the Akaike information criterion (AIC) of 0.101 and 0.114 for the synthetic and real samples that suggest the treatment process controlled under the chemisorption mechanism. The equilibrium studies were well fitted to the Langmuir isotherm with the AIC of 1.38, which indicates that the ATP adsorption is monolayer and homogeneous. The maximum adsorption capacity (qm) of 86.1 and 83.6 mg g−1, and RL of 0.0312 and 0.055 were found for synthetic and real samples, respectively.

Removal of Targeted Pharmaceuticals and Personal Care Products from Wastewater Treatment Plants using QSAR Model

Background: Because of their intrinsic ability to induce physiological effects in humans at low doses, pharmaceuticals and personal care products (PPCPs) are a unique group of emerging environmental pollutants. A number of studies have confirmed the occurrence of different PPCPs in the environment, which raises concerns about possible adverse effects on humans and wildlife. The removal of PPCPs from wastewaters has become a major activity to reduce pollution due to their adverse effects on humans and aquatic ecosystems. Methods: This study aimed to design a Quantitative Structure Activity Relationship (QSAR) model for the removal of 57 PPCPs from wastewater treatment plants (WWTPs) of historical data obtained from plants located in South Korea. The target compounds of PPCPs were optimised geometrically using a Forcite-Geometry code, assembled in Material Studio 2016. Results: The removal efficiency of PPCPs is dependent on several preliminary molecular descriptors including rotatable bonds (RBs), hydrogen bond donor (HBD), total molecular mass (TMM), binding energy (BE), atom count (AC), element count (EC), total energy (TE), total dipole (TD), highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). A Genetic Function Approximation (GFA) method was adopted to perform regression analysis and create correlation between experimental data (literature) and measured data (QSAR model). Conclusion: A QSAR model equation was established and used to predict removal efficiency of 57 PPCPs; the results obtained showed goodness of fit, R2 greater than 0.90 indicating that the internal and external validations were also performed on the model.