Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches

Preparation and Characterization of the MMT@Fe3O4@Ag Nanocomposite for Catalytic Degradation of Methyl Yellow: Reaction Parameters and Mechanism Based on the Artificial Neuron Network

The montmorillonite@iron oxide@silver (MMT@Fe3O4@Ag) nanocomposite, which is recyclable and exhibits high catalytic activity, was evaluated for the degradation of methyl yellow (MY), a carcinogenic azo dye. For this purpose, MMT@Fe3O4 was first synthesized via the coprecipitation method and then Ag was doped to MMT@Fe3O4 via the chemical reduction method. MMT, MMT@Fe3O4, and MMT@Fe3O4@Ag were characterized by various techniques including scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, vibrating sample magnetometer, and thermal gravimetric analysis. The results illustrated that MMT@Fe3O4@Ag exhibited a higher catalytic ability than MMT@Fe3O4 toward decolorization of MY with a degradation efficiency of 100% in 10 min at pH 7.1 in the presence of sodium borohydride (NaBH4). Further, some parameters like the amount of NaBH4, initial dye concentration, and pH were also studied to determine optimum reaction conditions. MMT@Fe3O4@Ag could be easily separated and recycled from the reaction medium using an external magnet. Thus, the Ag-doped MMT@Fe3O4 nanocomposite proved to have good catalytic activity, high MY degradation rate and reusability, and easy separation and simple synthesis method. These properties make it a promising catalyst for the treatment of wastewater containing organic pollutants. In addition, artificial neural network (ANN) simulation, which is a mathematical model with an artificial intelligence algorithm, was used for the degradation process. This model was evaluated with the parameters used in the experiment as the input and output layers. Last, the degradation of MY with the synthesized catalyst into different products was demonstrated by high-performance liquid chromatography (HPLC) analysis.

Exploring the progress and challenges of ultrasonic technology in environmental remediation

Amidst escalating environmental pollution due to accelerated industrialization and urbanization, there is an acute demand for effective and sustainable environmental remediation strategies. Ultrasonic technology, recognized for its green and efficient characteristics, has gained significant prominence in mitigating environmental pollution in aquatic, soil, and atmospheric ecosystems. The review provides a comprehensive analysis of the role of ultrasonic treatment in wastewater treatment, air quality improvement, and soil remediation. We systematically evaluate existing research to assess the effectiveness of ultrasonic technology in degrading pollutants and its potential for large-scale deployment. The review also examines the challenges associated with ultrasonic remediation, including optimizing operational parameters, enhancing energy transfer efficiency, and understanding the unique degradation mechanisms for various pollutants. Furthermore, we discuss environmental and safety considerations, along with the economic implications related to equipment costs and energy consumption. This review aims to contribute to the advancement of ultrasonic technology in environmental remediation by presenting a forward-looking perspective, aligning with the goals of environmental protection and sustainable development.

Characterization and adsorption performance of nano-hydroxyapatite synthesized from Conus litteratus waste seashells for Congo red dye removal

In this research, nano-hydroxyapatite synthesized from Conus litteratus seashells (Ss/nHAp) and the potential of Ss/nHAp as an adsorbent for eliminating Congo Red (CR) dye from aqueous solutions were explored. The synthesized Ss/nHAp was subjected to characterization using various techniques, including XRD, XPS, FTIR, Raman, BET, FESEM etc. in order to understand the material thoroughly. Batch adsorption experiments were conducted to establish the optimal conditions for removing the dye, considering variables such as adsorbent dosage, contact time, pH and initial dye concentration. 85% of the CR dye was eliminated within a short span of 10 minutes using a minimal adsorbent dose of 0.1 g, under neutral pH and room temperature, showcasing its efficiency. The adsorption process adhered to pseudo second order kinetics and was best fitted by the Langmuir isotherm model, with a maximum adsorption capacity (q max) of 24 mg g-1. The material's reusability was demonstrated through regeneration studies, with efficiency slightly decreasing to 74% by the fifth cycle due to adsorbent loss. This study establishes Ss/nHAp as a low-cost, eco-friendly, and effective adsorbent for dye-contaminated water treatment, paving the way for future applications in industrial wastewater remediation.

Sonophotocatalytic degradation of rhodamine B dye using Zr doped ZnO nanoparticles: Kinetic study and toxicity assessment

This study investigates sonophotocatalysis for the oxidation of Rhodamine B (RhB) dye using Zr doped ZnO nanoparticles. The synthesized nanoparticles were characterized by scanning electron microscope, Fourier-transform infrared, X-ray diffraction, Brunauer Emmett-Teller, thermogravimetric analysis, and photoluminescence. The hybrid sonophotocatalysis system achieved 98.73 ± 1.25% degradation of RhB under the conditions: RhB dye concentration = 10 ppm, 4 wt% of Zr doped ZnO = 0.1 g, reaction temperature = 30 °C, time = 60 min, pH = 7. A kinetic model was developed to predict the efficiency of RhB degradation through intrinsic elementary chemical reactions. The high coefficient of determination (R2 = 0.96) indicates the model's prediction accuracy in describing the degradation kinetics of RhB within sonophotocatalysis system. The electrical energy per order (EEO) analysis demonstrated that US + UVC + 4 wt% of Zr doped ZnO significantly reduced EEO (1055 kWh m-3 order-1). The synergy index for this combination was approximately 1.60, demonstrating a significant synergistic effect. Density Functional Theory (DFT) studies were utilized to propose the most probable degradation pathway, identifying reactive sites and potential byproducts. The simulations revealed the central carbon atom (1C site) as highly susceptible to radical attacks, indicating potential decolorization pathways such as N-de-ethylation, chromophore cleavage, and ring opening. Toxicity assessments of both the parent dye and its intermediates were conducted using ECOSAR (Ecological Structure Activity Relationship) to evaluate their ecological impacts. The combination of experimental results and kinetic modeling and simulations offers a deep understanding of RhB degradation complexities, driving advancements in sustainable water treatment technologies.

Biogenic engineered zinc oxide nanoparticle for sulfur black dye removal from contaminated wastewater: comparative optimization, simulation modeling, and isotherms

This research work aimed to isolate and culture the bacterium Bacillus paramycoides for biogenic fabrication of zinc oxide nanoparticles, specifically ZnO and ZnO-ME nanoparticles (nanoparticles fabricated from bacterial extracts only - ZnO, and from bacterial cell mass including extract - ZnO-ME). SEM investigation revealed the spherical-shaped NPs with 22.33 and 39 nm in size for ZnO and ZnO-ME, respectively. The Brunauer, Emmett, and Teller (BET) studies revealed mesoporous structure with pore diameters of 13.839 and 13.88 nm and surface area of 7.617 and 33.635 m2/gm for ZnO and ZnO-ME, respectively. Various parameters for the adsorption of sulfur black dye onto both ZnO and ZnO-ME were screened and optimized using Plackett-Burman Design (PBD), Full Factorial Design (FFD) and Central Composite Design (CCD). The results of the optimization modeling study revealed that FFD yielded the most predictable and best-fitting results among all the models studied, with R2 values of 0.998 for ZnO and 0.993 for ZnO-ME. Notably, ZnO-ME exhibited a greater dye removal efficiency 80% than ZnO i.e., 71%, it may be due to the presence of amorphous carbon on the surface of ZnO-ME. Among the various isothermal models, the Freundlich model displayed the strongest correlation with the dye removal data, confirming the multilayer adsorption of dye on both nanoparticles and supporting physisorption. Therefore, ZnO and ZnO-ME nanoparticles have been proven as potential tools for mitigating environmental impacts associated with dye-containing wastewater.

Degradation of direct red 23 dye in a plug flow reactor with adjustable angle baffles using nano-Fe3O4: modelling and optimisation

Photocatalytic degradation is a key technique in wastewater treatment, particularly for toxic dye removal, yet challenges related to poor hydrodynamics and mass transfer limitations persist. This study addresses these challenges by innovatively employing an adjustable-angle baffle in a plug flow reactor (PFR) to enhance dye removal efficiency. A lab-scale PFR with an adjustable baffle was utilised to assess the impact of various factors, including baffle angle, catalyst concentration, hydraulic retention time (HRT), pH, and initial dye concentration, on the removal of direct red 23 dye. The experimental design employed a central composite design (CCD), with subsequent data analysis using response surface methodology (RSM) and artificial neural network (ANN) models. The findings demonstrate that the adjustable baffle significantly impacts dye removal, achieving maximum efficiency at an optimal angle of 77.5 degrees. The ANN model outperformed the RSM model, with a higher determination coefficient (R2) of 0.994 compared to 0.928. Furthermore, RSM and genetic algorithms yielded closely aligned optimal conditions, validating their accuracy. The optimised conditions achieved a dye removal efficiency of 89.47%. Significantly, the study also identified degradation as the dominant mechanism over adsorption and highlighted the impressive stability of nano-Fe3O4 during the recycling process. Mineralisation analysis revealed the presence of lightweight organic residual molecules post-treatment. These outcomes demonstrate the effectiveness of adjustable baffles in PFRs, marking a significant advancement in wastewater treatment technologies and underscoring the critical role of baffle orientation and catalyst concentration in optimising dye removal processes.

Application and innovation of artificial intelligence models in wastewater treatment

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

Sustainable approach for the treatment of dye-containing wastewater – a critical review

In the world’s rapidly expanding economy, textile industries are recognized as a substantial contributor to economic growth, but they are one of the most significant polluting industrial sectors. Dye-contaminated water sources can pose serious public health concerns, including toxicity, mutagenicity, and carcinogenicity among other adverse health effects. Despite a limited understanding of efficacious decolorization methodologies, the pursuit of a sustainable strategy for the treatment of a wide spectrum of dyes remains a formidable challenge. This article conducted an exhaustive review of extant literature pertaining to diverse physical, chemical, biological, and hybrid processes with the aim of ascertaining their efficacy. It also elucidates the advantages and disadvantages, cost considerations, as well as scalability impediments of the treatment methodologies, thereby facilitating the identification of optimal strategies for establishing techno-economically efficient processes in the sustainable handling of these effluents. The hybrid configuration exhibited superior efficiency and was documented to surmount the limitations and constraints inherent to individual techniques. The study also revealed that most of the proven and established dye removal techniques share a common limitation viz., the generation of secondary pollution (i.e., sludge generation, toxic intermediates, etc.) to the ecosystem.

Development of an artificial neural network (ANN) for the prediction of a pilot scale mobile wastewater treatment plant performance

Productive activities such as pig farming are a fundamental part of the economy in Mexico. Unfortunately, because of this activity, large quantities of wastewater are generated that have a negative impact in the environment. This work shows an alternative for treating piggery wastewater based on advanced oxidation processes (Fenton and solar photo Fenton, SPF) that have been probed successfully in previous works. In the first stage, Fenton and SPF were carried out on a laboratory scale using a Taguchi L9-type experimental design. From the statistical analysis of this design, the operating parameters: pH, time, hydrogen peroxide concentration [H2O2], and iron ferrous concentration [Fe2+] that maximize the response variables: Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), and color were chosen. From these, a cascade forward neural network was implemented to establish a correlation between data from the variables to the physicochemical parameters to be measure being that a great fit of the data was obtained having a correlation coefficient of 0.99 which permits to optimize the pollutant degradation and predict the removal efficiencies at pilot scale but with a projection to a future industrial scale. A relevant result, it was found that the optimal values for maximizing the removal of physicochemical parameters were pH = 3, time = 60 min, H2O2/COD = 1.5 mg L-1, and H2O2/Fe2+ = 2.5 mg L-1. With these conditions degradation percentages of 91.44%, 47.14%, and 97.89% for COD, TOC, and color were obtained from the Fenton process, while for SPF the degradation percentage increased moderately. From the ANN analysis, the possibility to establish an intelligent system that permits to predict multiple results from operational conditions has been achieved.

Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds

Sulfamethoxazole and metronidazole are emerging pollutants commonly found in surface water and wastewater. These compounds have a significant environmental impact, being necessary in the design of technologies for their removal. Recently, the advanced oxidation process has been proven successful in the elimination of this kind of compounds. In this sense, the present work discusses the application of UV/H2O2 and ozonation for the degradation of both molecules in single and binary systems. Experimental kinetic data from O3 and UV/H2O2 process were adequately described by a first and second kinetic model, respectively. From the ANOVA analysis, it was determined that the most statistically significant variables were the initial concentration of the drugs (0.03 mmol L-1) and the pH = 8 for UV/H2O2 system, and only the pH (optimal value of 6) was significant for degradation with O3. Results showed that both molecules were eliminated with high degradation efficiencies (88-94% for UV/H2O2 and 79-98% for O3) in short reaction times (around 30-90 min). The modeling was performed using a quadratic regression model through response surface methodology representing adequately 90 % of the experimental data. On the other hand, an artificial neural network was used to evaluate a non-linear multi-variable system, a 98% of fit between the model and experimental data was obtained. The identification of degradation byproducts was performed by high-performance liquid chromatography coupled to a time mass detector. After each process, at least four to five stable byproducts were found in the treated water, reducing the mineralization percentage to 20% for both molecules.

Functionalization of Shorea faguetiana biochar using Fe2O3 nanoparticles and MXene for rapid removal of methyl blue and lead from both single and binary systems

The synthesis of polymeric magnetic composites is a promising strategy for the rapid and efficient treatment of wastewater. Lead and methyl blue are extremely hazardous to living organisms. The sorption of Pb2+ and the dye methyl blue (MB) by biochar is an ecologically sustainable method to remediate this type of water pollution. We functionalized Shorea faguetiana biochar with Fe2O3 and MXene, resulting in Fe2O3/BC/MXene composites with an efficient, rapid, and selective adsorption performance. Based on X-ray photoelectron and Fourier transform infrared spectrometry, we found that the Fe2O3/BC/MXene composites had an increased number of surface functional groups (F-, C[double bond, length as m-dash]O, CN, NH, and OH-) compared with the original biochar. The batch sorption findings showed that the maximum sorption capacities for Pb2+ and MB at 293 K were 882.76 and 758.03 mg g-1, respectively. The sorption phenomena obeyed a pseudo-second-order (R2 = 1) model and the Langmuir isotherm. There was no competition between MB and Pb2+ in binary solutions, indicating that MB and Pb2+ did not influence each other as a result of their different adsorption mechanisms (electrostatic interaction for Pb2+ and hydrogen bonding for MB). This illustrates monolayer sorption on the Fe2O3/BC/MXene composite governed by chemical adsorption. Thermodynamic investigations indicated that the sorption process was spontaneous and exothermic at 293-313 K, suggesting that it is feasible for practical applications. Fe2O3/BC/MXene can selectively adsorb Pb2+ ions and MB from wastewater containing multiple interfering metal ions. The sorption capacities were still high after five reusability experiments. This work provides a novel Fe2O3/BC/MXene composite for the rapid and efficient removal of Pb2+ and MB.

A review on existing and emerging approaches for textile wastewater treatments: challenges and future perspectives

This comprehensive review explores the complex environment of textile wastewater treatment technologies, highlighting both well-established and emerging techniques. Textile wastewater poses a significant environmental challenge, containing diverse contaminants and chemicals. The review presents a detailed examination of conventional treatments such as coagulation, flocculation, and biological processes, highlighting their effectiveness and limitations. In textile industry, various textile operations such as sizing, de-sizing, dyeing, bleaching, and mercerization consume large quantities of water generating effluent high in color, chemical oxygen demand, and solids. The dyes, mordants, and variety of other chemicals used in textile processing lead to effluent variable in characteristics. Furthermore, it explores innovative and emerging techniques, including advanced oxidation processes, membrane filtration, and nanotechnology-based solutions. Future perspectives in textile wastewater treatment are discussed in-depth, emphasizing the importance of interdisciplinary research, technological advancements, and the integration of circular economy principles. Numerous dyes used in the textile industry have been shown to have mutagenic, cytotoxic, and ecotoxic potential in studies. Therefore, it is necessary to assess the methods used to remediate textile waste water. Major topics including the chemical composition of textile waste water, the chemistry of the dye molecules, the selection of a treatment technique, the benefits and drawbacks of the various treatment options, and the cost of operation are also addressed. Overall, this review offers a valuable resource for researchers and industry professionals working in the textile industry, pointing towards a more sustainable and environmentally responsible future.

Waste iron as a robust and ecological catalyst for decomposition industrial dyes under UV irradiation

In an era of increasing environmental awareness, it is very important to work towards eliminating or at least reducing as many harmful industrial substances as possible. However, the implementation of green chemistry methods for wastewater treatment can be difficult especially due to complexity, the high cost of reagents, and the required long process time. This paper focuses on using waste iron (WI) to remove two kinds of amaranth dye commonly used in industry. To enhance the process, UV irradiation and hydrogen peroxide were used. The novelty of the research was the use of efficient and reusable WI as a heterogeneous catalyst in the process. WI material characteristics was done before and after the process using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray fluorescence (XRF). Zeta potential, size characterization, circularity, and direct band gap were also determined. As a result of treatment complete decolorization of both dyes was achieved, as well as 99% absorbance removal after 15-min process time. The total organic carbon (TOC) decrease after 60-min process time was in the range from 86.6 to 89.8%. Modified pseudo-second-order reaction reflects obtained results of treatment efficiency. Treatment results, confirmed by WI material characterization, indicate satisfactory stability of the catalyst and good oxidation capacity.

Operational parameter prediction of electrocoagulation system in a rural decentralized water treatment plant by interpretable machine learning model

Electrocoagulation (EC) is a promising alternative for decentralized drinking water treatment in rural areas as a chemical-free technology. However, seasonal fluctuations of water quality in influent remain a significant challenge for rural decentralized water supply, which was a potential threat to water safety. The frequent operation was required to ensure the effluent water quality by the experienced technicians, who were in shortage in rural areas. If the operational parameters prediction model based on water quality could be established, it might reduce the dependence on technicians. Therefore, an artificial neural network (ANN) model combined with genetic algorithm (GA) was used to establish a prediction model for unattended intelligent operation. Data on water quality and operational parameters were collected from a practical EC system in a decentralized water treatment plant. Seven water quality parameters (e.g., turbidity, temperature, pH and conductivity) were selected as input variables and the operational current was employed as the output. A non-linear relationship between water quality parameters and the operational current was verified by correlation analysis and principal component analysis (PCA). The mean squared error (MSE) and coefficient of determination (R²) were used as evaluation indexes to optimize the structure of the GA-ANN model. Influent turbidity was identified to be crucial in the GA-ANN model by model interpretation using sensitivity analysis and scenario analysis. The Garson weight of turbidity in the seven input variables achieved 45.4%. The predictive accuracy of the GA-ANN model sharply declined from 90% to 67.1% when influent turbidity data were absent. In addition, it was estimated that energy consumption savings of the GA-ANN method declined by 14.2% in comparison with the gradient control method. This study verifies the feasibility and stability of machine learning strategy for unattended operation in the rural decentralized water treatment plant.

Possibilities and Limitations of the Sono-Fenton Process Using Mid-High-Frequency Ultrasound for the Degradation of Organic Pollutants

Mid-high-frequency ultrasound (200-1000 kHz) eliminates organic pollutants and also generates H₂O₂. To take advantage of H₂O₂, iron species can be added, generating a hybrid sono-Fenton process (sF). This paper presents the possibilities and limitations of sF. Heterogeneous (a natural mineral) and homogeneous (Fe²⁺ and Fe³⁺ ions) iron sources were considered. Acetaminophen, ciprofloxacin, and methyl orange were the target organic pollutants. Ultrasound alone induced the pollutants degradation, and the dual competing role of the natural mineral (0.02-0.20 g L^-1) meant that it had no significant effects on the elimination of pollutants. In contrast, both Fe²⁺ and Fe³⁺ ions enhanced the pollutants' degradation, and the elimination using Fe²⁺ was better because of its higher reactivity toward H₂O₂. However, the enhancement decreased at high Fe²⁺ concentrations (e.g., 5 mg L^-1) because of scavenger effects. The Fe²⁺ addition significantly accelerated the elimination of acetaminophen and methyl orange. For ciprofloxacin, at short treatment times, the degradation was enhanced, but the pollutant complexation with Fe³⁺ that came from the Fenton reaction caused degradation to stop. Additionally, sF did not decrease the antimicrobial activity associated with ciprofloxacin, whereas ultrasound alone did. Therefore, the chemical structure of the pollutant plays a crucial role in the feasibility of the sF process.

N Doped Activated Biochar from Pyrolyzing Wood Powder for Prompt BPA Removal via Peroxymonosulfate Activation

In the present study, nitrogen doped biochar (N-PPB) and nitrogen doped activated biochar (AN-PPB) were prepared and used for removing bisphenol A (BPA) in water through activating peroxymonosulfate. It was found from the results that N-PPB exhibited superior catalytic performance over pristine biochar since nitrogen could brought about abundant active sites to the biochar structure. The non-radical singlet oxygen (1O2) was determined to be the dominant active species responsible for BPA degradation. Having non-radical pathway in the N-PPB/PMS system, the BPA degradation was barely influenced by many external environmental factors including solution pH value, temperature, foreign organic, and inorganic matters. Furthermore, AN-PPB had richer porosity than N-PPB, which showed even faster BPA removal efficiency than N-PPB through an adsorptive/catalytic synergy. The finding of this study introduces a novel way of designing hieratical structured biochar catalysts for effective organic pollutant removal in water.

Integrated ozone-sono-Fenton for the enhanced degradation of acid orange 7: process optimization and kinetic evaluation

The performance of novel hybrid advanced oxidation, ozone-sono-Fenton process in degradation of acid orange 7 (AO7), as a model of azo dyes was modelled and optimized using response surface methodology (RSM) based on central composite design (CCD). Utilizing a bubbling reactor equipped with an ultrasound probe and in the presence of Fenton reagents, a promising hybrid homogeneous AOP, ozone-sono-Fenton, was investigated. According to the experimental results, the variation trend of degradation efficiency (DE%) with pH, reaction time and Fe²⁺/H₂O₂ molar ratio was modelled with the reduced quadratic model. Additionally, the suitability of the model was indicated with close to unity regression coefficient [Formula: see text]. Furthermore, the comparative study of degradation efficiency and COD removal for the individual methods including ozonation, sonication and Fenton reagents as well as their hybrid processes reveals that the novel proposed technique, ozone-sono-Fenton process, is able to rapid and complete degradation of acid orange 7 with initial concentration of 300 mg L^-1, 100% in only 12 min. The complete degradation was obtained under optimum conditions such as pH = 6, reaction time = 12 min and Fe²⁺/H₂O₂ molar ratio = 0.0040. The kinetics evaluation of the acid orange 7 concentration during the processing implied the first-order reaction. Considering the synergetic effect and cost-effectiveness of the hybrid method, the promising ozone-sono-Fenton method could effectively degrade using a wide range of organic contaminants.

Efficient electrochemical removal of 5-fluorouracil pharmaceutical from wastewater by mixed metal oxides via anodic oxidation process

Nowadays, the entry of organic compounds into water resources is one of the leading global concerns due to the lack of water resources and rapid population growth. In this research, anodic oxidation (AO) method was used to remove 5-fluorouracil (5-FU) from aqueous solutions via Ni/RuO₂ and Ti/IrO₂-TiO₂-RuO₂ electrodes as cathode and anode, respectively. For this purpose, the characterization analysis of the electrodes, including X-ray diffraction, scanning electron microscopy, energy dispersive X-ray, and atomic force microscopy were performed. The electrochemical performance of the anode was investigated via cyclic voltammetry analysis. Then, the effect of operational variables, including applied current (mA), initial pH of the solution, initial 5-FU concentration (mg/L), and process time (min) on the 5-FU removal efficiency under the AO process was evaluated via artificial neural network (ANN) modeling. The results revealed that the maximum 5-FU removal efficiency was 96.96%. The applied current intensity, pH, initial 5-FU concentration, and process time were 300 mA, 5, 20 mg/L, and 140 min, respectively. Moreover, the investigation of 5-FU removal by-products and mineralization efficiency of the AO process was carried out via gas chromatography-mass spectrometry and total organic carbon analysis, respectively. The total organic carbon mineralization efficiency was 84.80% after 6 h of reaction time. The reusability and stability of the Ti/IrO₂-TiO₂-RuO₂ anode on 5-FU removal efficiency were measured and showed an approximately 5% decay in 5-FU removal efficiency after eight consecutive runs. The overall results and analysis confirmed this method is capable of removing 5-FU through Ti/IrO₂-TiO₂-RuO₂ anode and Ni/RuO₂ cathode from aqueous medium.

Waste preserved wood derived biochar catalyst for promoted peroxymonosulfate activation towards bisphenol A degradation with low metal ion release: The insight into the mechanisms

The rational disposal of waste preserved wood is of great significance since its embedded metals (Cu, As, and Cr) pose potential threat to environment and human health. In this study, a biochar catalyst derived from waste preserved wood (PWB) was prepared for the degradation of bisphenol A (BPA) via peroxymonosulfate (PMS) activation. The PWB exhibited prominent catalytic degradation capability towards BPA compared with common wood derived biochar (CWB). Further tests and analysis elucidated that both radical species (OH) and non-radical species (¹O₂) were generated by the PWB/PMS system, whereas only ¹O₂ was detected in CWB/PMS system. Specifically, the metal compounds, especially metallic Cu in the PWB activated PMS via radical pathway, and the CO groups in the biochar generated the non-radical pathway, the coexistence of which resulted in higher BPA degradation rate in PWB/PMS system. It was also demonstrated that the heavy metal ion leaching (As and Cr) in PWB/PMS system was negligible. Furthermore, the biochar could effectively inhibit the leakage of oxidized Cu ions. This study provides a novel approach to prepare high-efficient carbocatalysts for organic pollutant degradation in water, which also enables the waste preserved wood with an environmental nondestructive mode of dispatch.

The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning

During the last few years, important advances have been made in big data exploration, complex pattern recognition and prediction of complex variables. Machine learning (ML) algorithms can efficiently analyze voluminous data, identify complex patterns and extract conclusions. In chemical engineering, the application of machine learning approaches has become highly attractive due to the growing complexity of this field. Machine learning allows computers to solve problems by learning from large data sets and provides researchers with an excellent opportunity to enhance the quality of predictions for the output variables of a chemical process. Its performance has been increasingly exploited to overcome a wide range of challenges in chemistry and chemical engineering, including improving computational chemistry, planning materials synthesis and modeling pollutant removal processes. In this review, we introduce this discipline in terms of its accessible to chemistry and highlight studies that illustrate in-depth the exploitation of machine learning. The main aim of the review paper is to answer these questions by analyzing physicochemical processes that exploit machine learning in organic and inorganic pollutants removal. In general, the purpose of this review is both to provide a summary of research related to the removal of various contaminants performed by ML models and to present future research needs in ML for contaminant removal.

Modeling and optimization of simultaneous degradation of rhodamine B and acid red 14 binary solution by homogeneous Fenton reaction: a chemometrics approach

This study aimed to propose a mathematical method to investigate and optimize the simultaneous elimination process of multiple organic pollutants using the Fenton process. Hence, the treatment of rhodamine B (RB) and acid red 14 (AR14) dyes in their binary solution was studied. Multivariate curve resolution alternating least square (MCR-ALS), a novel chemometric method, was applied along with correlation constraints to resolute the UV-Vis spectrophotometric data, enabling quantification of investigated dyes despite a high spectral overlapping. Response surface methodology was adopted to assess the model and optimize individual and interactive effects of three independent factors (Fe²⁺, H₂O₂ and initial pH) on the simultaneous elimination of RB and AR14. The values of the regression coefficient for RB and AR14 were determined as 98.48 and 98.67 percent, respectively, revealing the reliability of the obtained polynomial models to predict decolorization efficiencies. Desirability function was employed to optimize the independent variables to attain the highest possible degradation performance for both dyes in their binary solution. At the optimum point of operation ([Fe²⁺] = 143.88 mg/L, [H₂O₂] = 126.89 mg/L and pH = 3.71), degradation efficiencies of RB and AR14 were found as 81.58% and 80.22%, respectively, which were nearly identical to the experimental results.

Hazardous wastes treatment technologies

A review of the literature published in 2019 on topics related to hazardous waste management in water, soils, sediments, and air. The review covered treatment technologies applying physical, chemical, and biological principles for the remediation of contaminated water, soils, sediments, and air. PRACTICAL POINTS: This report provides a review of technologies for the management of waters, wastewaters, air, sediments, and soils contaminated by various hazardous chemicals including inorganic (e.g., oxyanions, salts, and heavy metals), organic (e.g., halogenated, pharmaceuticals and personal care products, pesticides, and persistent organic chemicals) in three scientific areas of physical, chemical, and biological methods. Physical methods for the management of hazardous wastes including general adsorption, sand filtration, coagulation/flocculation, electrodialysis, electrokinetics, electro-sorption ( capacitive deionization, CDI), membrane (RO, NF, MF), photocatalysis, photoelectrochemical oxidation, sonochemical, non-thermal plasma, supercritical fluid, electrochemical oxidation, and electrochemical reduction processes were reviewed. Chemical methods including ozone-based, hydrogen peroxide-based, potassium permanganate processes, and Fenton and Fenton-like process were reviewed. Biological methods such as aerobic, anoxic, anaerobic, bioreactors, constructed wetlands, soil bioremediation and biofilter processes for the management of hazardous wastes, in mode of consortium and pure culture were reviewed. Case histories were reviewed in four areas including contaminated sediments, contaminated soils, mixed industrial solid wastes and radioactive wastes.

Modeling and Optimization of Pollutants Removal during Simultaneous Adsorption onto Activated Carbon with Advanced Oxidation in Aqueous Environment

The paper presents the results of studies on the modeling and optimization of organic pollutant removal from an aqueous solution in the course of simultaneous adsorption onto activated carbons with varied physical characteristics and oxidation using H₂O₂. The methodology for determining the models used for predicting the sorption and catalytic parameters in the process was presented. The analysis of the influence of the sorption and catalytic parameters of activated carbons as well as the oxidizer dose on the removal dynamics of organic dyes-phenol red and crystal violet-was carried out based on the designated empirical models. The obtained results confirm the influence of specific surface area (S) of the activated carbon and oxidizer dose on the values of the reaction rate constants related to the removal of pollutants from the solution in a simultaneous process. It was observed that the lower the specific surface area of carbon (S), the greater the influence of the oxidizer on the removal of pollutants from the solution. The proposed model, used for optimization of parameters in a simultaneous process, enables to analyze the effect of selected sorbents as well as the type and dose of the applied oxidizer on the pollutant removal efficiency. The practical application of models will enable to optimize the selection of a sorbent and oxidizer used simultaneously for a given group of pollutants and thus reduce the process costs.

Response surface methodology for predicting the dimethylphenol removal from wastewater via reverse osmosis process

Reverse Osmosis (RO) process can be considered as one of the intensively used pioneering equipment for reusing wastewater of several applications. The recent study presented the development of an accurate model for predicting the dimethylphenol removal from wastewater via RO process. The Response Surface Methodology (RSM) was applied to carry out this challenge based on actual experimental data collected from the literature. The independent variables considered are the inlet pressure (5.83–13.58) atm, inlet temperature (29.5–32) ° C, inlet feed flow rate (2.166–2.583) × 10–4 m3/s, and inlet concentration (0.854–8.049) × 10-3 kmol/m3 and the dimethylphenol removal is considered as the response variable. The analysis of variance showed that the inlet temperature and feed flow rate have a negative influence on dimethylphenol removal from wastewater while the inlet pressure and concentration show a positive influence. In this regard, F-value of 240.38 indicates a considerable contribution of the predicted variables of pressure and concentration against the process dimethylphenol rejection. Also, the predicted R2 value of 0.9772 shows the high accuracy of the model. An overall assessment of simulating the performance of RO process against the operating parameters has been systematically demonstrated using the proposed RSM model.