Prediction Of Removal Efficiency Research Articles

Food dye adsorption in single and ternary systems by the novel passion fruit peel biochar adsorbent

This study evaluated the passion fruit peel biochar (PFPB) as a novel adsorbent for synthetic food dyes indigotine blue (IB), tartrazine yellow (TY), and ponceau 4R (P4R) removal in single and ternary systems. A macroporous structure and a predominance of basic groups characterized PFPB. The pH study revealed better adsorption at pH 2.0. The response surface methodology optimization for adsorbent dosage and temperature predicted removal efficiencies of 100 % for IB, 79.8 % for TY, and 84.4 % for P4R. Elovich and Redlich-Peterson models better described kinetic and equilibrium, respectively, suggesting the contribution of chemical interactions. Thermodynamic data revealed endothermic, with an inordinate degree and spontaneous adsorption. In the ternary systems, antagonistic effects of interaction were noticed. The adsorption of synthetic effluents showed promising results with removal efficiencies of 99.6 % (IB), 60.2 % (TY), and 51.8 % (P4R). Therefore, we concluded that PFPB is a potential alternative low-cost synthetic food dye removal adsorbent.

Modelling the predictive analysis of turbidity removal efficiency in the in-line coagulation and flocculation process

Coagulation and flocculation processes are commonly used in conventional water treatment plants to remove suspended solids from water. However, these methods often suffer from drawbacks such as high chemical consumption, sludge generation, and the need for huge areas, leading to high construction costs. To address these limitations, this study investigated the performance of in-line coagulation and flocculation processes as an alternative to conventional methods. The objectives were to determine the efficiency of in-line coagulation and flocculation, understand the underlying mechanisms, establish optimal operating conditions and design criteria, and to develop theoretical models for predicting turbidity removal efficiency in the in-line coagulation-flocculation process. Experiments were conducted using a continuous setup consisting of a static mixer and a 35-m helically coiled hydraulic tube flocculator.Various operating parameters, including water flow rate (100 – 800 L/hr), initial turbidity (20 – 200 NTU), and coagulant types (aluminum sulfate, poly aluminum chloride, aluminum chlorohydrate, and ferric chloride), were examined. The results demonstrated that aluminum sulfate was the appropriate coagulant for the water characteristics studied, and the in-line coagulation and flocculation processes achieved turbidity removal efficiencies of approximately 91 % under all operating conditions, with a notable 97 % removal efficiency achieved at a liquid flow rate of 600 L/hr, with a Gt value of 21,715 and an overflow rate of 2 m/hr. A prediction model of turbidity removal efficiency was developed, showing good agreement between experimental and predicted removal efficiency model, with an average deviation of about 20 %. This model can aid in determining optimal operating conditions and serve as a hybrid process in future water treatment systems, potentially applicable to other separation processes as well.

Removal of ammoniacal nitrogen from Malaysian palm oil mill effluent (POME) using optimized operating parameters of peat soil as natural adsorbent

Nowadays, the use of natural absorbents to remove pollutants from POME has gained remarkable attention. The main objective of this study is to investigate the suitability and performance of modified peat soil as an adsorbent for the removal of NH3-N from POME. The chemical activation method was performed using readily available NaOH for the first time to improve the adsorption performance of naturally available low-cost peat soil. The physical properties of raw and modified peat soil were determined using water-holding capacity, moisture content, bulk density, porosity, and BET surface area. The adsorbents were also characterized by SEM and FTIR to investigate surface morphology and chemical composition. To optimize the experimental parameters namely adsorbent dosage, agitation rate, and contact time for removal of NH3-N from POME, response surface methodology (RSM) was employed in this study with two different activation ratios. Substantial improvement of physical properties was attained after the modification of raw peat soil. The SEM images of modified peat soil showed a more porous space structure with larger voids while the FT-IR demonstrated the distinctive functional groups in the raw and modified peat soil. At optimized conditions of 5.71 g/L adsorbent dosage, 50 rpm agitation rate, and 38.96 min contact time predicted removal efficiency of NH3-N has been revealed 64.06 and 58.74 % at 1:20 and 1:30 activation ratios, respectively. The experimental investigation using optimized parameters showed 69.12 ± 2.5 and 61.57 ± 4.3 % removal of NH3-N. The experimental and predicted results showed good agreement. The rapid removal of NH3-N (69.1 % within 39 min) was achieved by chemically modified peat soil in this study compared to previously reported studies. Nevertheless, the raw and modified peat soil showed good stability up to three cycles of reusability.

Trimethoprim Removal from Aqueous Solutions via Volcanic Ash-Soil Adsorption: Process Modeling and Optimization

Antibiotic contamination of water sources is a significant environmental and public health concern. This contamination is classified among the most dangerous types of pollution currently because of their harmful effects. Therefore, it is essential to identify effective and environmentally friendly ways to deal with those dangerous compounds. Within this context, this work looked into whether soils made from volcanic ash could be used as cost-effective adsorbents to remove the antibiotic trimethoprim (TRM) from aqueous solutions. To examine the impacts of the main operating parameters on TRM removal, which are the initial antibiotic concentration (C), contact time (t), stirring speed (S), and solid-to-liquid ratio (R), a Central Composite Design (CCD) based on the Response Surface Methodology (RSM) was employed. Full quadratic polynomial models were used to correlate the experimental data, allowing for the estimation of each factor’s influence. With a predicted removal efficiency of 77.59%, the removal process optimization yielded the following set of optimal conditions: C = 4.5 mg/L, t = 45.5 min, S = 747 rpm, and R = 0.04 g/mL. Experiments conducted under predicted ideal conditions supported both the result and the previously developed model’s capacity for prediction. Additionally, the adsorption mechanism was also proposed based on the characterization of the adsorbent before and after the treatment. The study’s findings provide the possibility of using soils formed from volcanic ash as a cost-effective adsorbent material for the removal of TRM and likely other similar pollutants from contaminated waters.

Continuous Remediation of Congo Red Dye Using Polyurethane-Polyaniline Nano-Composite Foam: Experiment and Optimization Study

This study employed an innovative approach, utilizing prepared dried polyurethane-polyaniline nano-composite, through in-situ polymerization, for continuous remediation of Congo red dye. Response Surface Methodology (RSM) based on the Box-Behnken design (BBD) model was utilized to optimize the processing parameters, including initial dye concentration, flow rate, and pH. The two-factor interaction (2FI) model emerged as the most significant, highlighting the influence of individual and interaction effects of the factors. Optimization of the dye remediation process yielded the optimal conditions of a flow rate of 10 mL/min, acidic pH of 5.00, and dye concentration of 20 mg/L, resulting in an impressive, predicted removal efficiency of 99.09% agreeing with the experimental value. Moreover, the maximum adsorption capacity was determined to be 329.68 mg/g. Characterization of the adsorbent material involved techniques such as Scanning electron microscopy (SEM), Fourier transforms infrared spectra (FTIR), X-ray spectroscopy (XRD), and Zeta potential analysis. This material offers a sustainable alternative in industries to treat Congo red dye before being disposed of into the environment.

Nitrate and ammonium removal in constructed wetlands: Experimental insights and zero-dimensional numerical modeling

Constructed wetlands (CWs) have emerged as effective wastewater treatment systems, mimicked natural wetland processes but engineered for enhanced pollutant removal efficiency. Ammonium (NH4+) and nitrate (NO3−) are among common pollutants in wastewater, posing significant environmental and health risks. The primary objective of this study is to compares the performance of CWs using gravel and three sizes of natural pumice, along with phragmites australis, in horizontal and horizontal-vertical CWs for nitrate and ammonium removal in the complementary treatment of domestic wastewater. Additionally, the study aims to develop and validate a numerical model using MATLAB software to predict the removal efficiency of these pollutants, thereby contributing to the optimization of CW design and operation. The model operates as a zero-dimensional model based on the law of mass conservation, treating the wetland as a completely mixed reactor, thus avoiding complexities associated with solute movement in porous media. It accurately could predict removal efficiency of chemical, biochemical, and biological indicators while considering active and passive absorption mechanisms by plant uptake. Notably, the determination of coefficients in the model equation does not rely on potentially error-prone laboratory measurements due to sampling issues. Instead, optimization techniques alongside field data robustly estimate these coefficients, ensuring reliability and practicality. Results indicate that higher pollutant concentrations increase reaction rates, particularly enhancing CW efficiency in ammonium removal. Pumice, especially in larger sizes, exhibits superior absorption due to increased porosity and surface area. Overall, the model accurately predicts nitrates concentrations, demonstrating its potential for CW performance optimization and confirming the significance of effective pollutant removal strategies in wastewater treatment.

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.