Modelling and optimization of photocatalytic degradation of phenol via TiO2 nanoparticles: An insight into response surface methodology and artificial neural network

Analysis of Photocatalytic Degradation of Phenol by Zinc Oxide Using Response Surface Methodology

In this study, the photocatalytic degradation of phenol, which is commonly found in industrial wastewater at high rates, was investigated using a zinc oxide (ZnO) catalyst. It is thought that our findings will contribute to the removal of phenol in industrial wastewater. The experimental study was conducted in a batch-type air-fed cylindrical photocatalytic reactor, and a central composite design (CCD) was chosen and analyzed using response surface methodology (RSM). The study aimed to explore the effects of initial phenol concentration, catalyst concentration, airflow rate, and degradation time on the photocatalytic degradation of phenol and the removal efficiency of total organic carbon (TOC). A quadratic regression model was developed to establish the relationship between phenol degradation, TOC removal effectiveness, and the four factors mentioned. The validity of the model was assessed through an analysis of variance (ANOVA). A good agreement was observed between the model results and the experimental data. As a result of the experiments carried out under optimized conditions, the degradation percentage of phenol was found to be 77.15 %, and the degradation percentage of TOC was 59.87 %. Additionally, pseudo-first-order kinetics were used in the photocatalytic degradation of phenol.

A novel nanocomposite for photocatalytic rhodamine B dye removal from wastewater using visible light

Providing safe access to water and addressing the impact of waterborne diseases, which claim over two million lives annually, is a major contribution to water purification. The study introduces a novel nanocomposite, Ch/Fe3O4/α-MoO3, which exhibits outstanding photocatalytic efficacy under visible light. An in-depth investigation of the nanocomposite's synthesis, characterization, and photodegradation mechanisms reveals its outstanding capabilities. Photocatalytic activity is influenced by the catalytic dose, pH, dye concentration, and reaction time, according to the study. A response surface method is used to determine the optimal conditions for Rhodamine B degradation, which results in 96.3% removal efficiency at pH 8.5, dye concentration 25 mg/L, nanocomposite dose at 22 mg/L, and reaction time 50 min. As a result of its high surface area, biocompatibility, availability, and magnetization with iron compounds, Chitosan is an excellent substrate for enhancing the photocatalytic properties of MoO3 nanoparticles. A nanocomposite with an energy band of 3.18 eV exhibits improved visible light absorption. This study confirms the nanocomposite's recyclability and stability, affirming its practicality. Besides dye removal, it offers hope for the global quest for clean water sources by addressing a broader range of waterborne contaminants. By combining molybdenum and magnetite, nanocomposite materials facilitate the degradation of pollutant and bacteria, contributing positively to society's quest for clean and safe water. It emphasizes the role nanotechnology plays in preserving human health and well-being in combating waterborne diseases.

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash-Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit-cost ratio.

Biopolymeric Nanocomposites for Wastewater Remediation: An Overview on Recent Progress and Challenges

Essential for human development, water is increasingly polluted by diverse anthropogenic activities, containing contaminants like organic dyes, acids, antibiotics, inorganic salts, and heavy metals. Conventional methods fall short, prompting the exploration of advanced, cost-effective remediation. Recent research focuses on sustainable adsorption, with nano-modifications enhancing adsorbent efficacy against persistent waterborne pollutants. This review delves into recent advancements (2020-2023) in sustainable biopolymeric nanocomposites, spotlighting the applications of biopolymers like chitosan in wastewater remediation, particularly as adsorbents and filtration membranes along with their mechanism. The advantages and drawbacks of various biopolymers have also been discussed along with their modification in synthesizing biopolymeric nanocomposites by combining the benefits of biodegradable polymers and nanomaterials for enhanced physiochemical and mechanical properties for their application in wastewater treatment. The important functions of biopolymeric nanocomposites by adsorbing, removing, and selectively targeting contaminants, contributing to the purification and sustainable management of water resources, have also been elaborated on. Furthermore, it outlines the reusability and current challenges for the further exploration of biopolymers in this burgeoning field for environmental applications.

Biomass based biochar production approaches and its applications in wastewater treatment, machine learning and microbial sensors

Biochar is a stable carbonaceous material derived from various biomass and can be utilized as adsorbents, catalysts and precursors in various environmental applications. This review discusses various feedstock materials and methods of biochar production via traditional as well as modern approaches. Additionally, the biochar characteristics, HTC process, and its modification by employing steam and gas purging, acidic, basic / alkaline and organo-solvent, electro- and magnetic fields have been discussed. The recent biochar applications for real water, wastewater and industrial wastewater for the abstraction of environmental contaminants also reviewed. Moreover, applications in machine learning and microbial sensors were discussed. In the meantime, analyses on commercial and environmental profit, current ecological concerns and the future directions of biochar application have been well presented.

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

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

Synergistic potency of ultrasound and solar energy towards oxidation of 2,4-dichlorophenol: a chemometrics approach

Industrial units based on chemical processes-the textile and paper industries-are major sources of chlorophenols in the environment, and chlorophenolic compounds persist within the environment for a long time with high toxicity levels. The photo-assisted Fenton's and photocatalysis processes were investigated for the degradation of chlorophenols in the present study. Response surface methodology was employed to get optimised conditions for photocatalysis and photo-Fenton process-governing factors, thus, yielding a profound removal efficiency. Under optimised conditions, with a photocatalyst dose of 0.2 g/L, oxidant concentration of 10.0 mM and pH 5.0, complete removal of 2,4-dichlorophenol (2,4-DCP) was observed in 210 minutes in photocatalytic treatment. In the case of the photo-Fenton process, at an H2O2 dose of 5.0 mM and Fe2+ concentration of 0.5 mM, the organic pollutant was eliminated within 5 minutes of reaction time under acidic conditions (pH 3.0). The RSM model reported the perfect fit of experimental data with the predicted response. Among different isotherm models, the Langmuir isotherm was the best fit. The process followed pseudo-first order rate kinetics among various kinetics models. For the obtained optimised conditions, sonication and solar energy-driven processes were incorporated to study enhanced mineralisation. The solar-assisted Fenton process reported maximum mineralisation (90%) and cost-effective ($0.01/litre for 100 mg/L 2,4-DCP) treatment among different hybrid oxidation processes. The work provides insight into harnessing the naturally available solar energy, reducing the overall treatment cost and opting for a sustainable treatment method.

Degradation of p-cresol in the presence of UV light driven in an integrated system containing photocatalytic and packed bed biofilm reactor

Integratingphotocatalysisand biodegradation has shown great potential in wastewater treatment technology. In this study, the degradation of p-cresol in water has been investigated through an integrated system comprising of photocatalytic and packed bed biofilm reactor (PBBR). In the photocatalytic reactor, the biodegradability index (BOD5/COD, BOD5/TOC) of the p-cresol solution was first shown to increase (from 0.098±0.023 to 0.59±0.089 for BOD5/COD and from 0.27±0.030 to 1.74±0.03 for BOD5/TOC). The pseudo-first-order rate constant (kap) was found to be 0.011 min-1. The % removal of the integrated system was found to be 98.43±1.31% at an initial concentration of 700 mg L-1 and residence time of 120 h, which was significantly higher than the PBBR operated alone (34.82±2.62%) under optimized conditions (pH 7.0 and T = 32±2 °C). Using an integrated approach, the efficient removal of p-cresol opens novel future perspectives for catalytic degradation using chemical oxidation.

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

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

Green Synthesis of ZnO and Black TiO2 Materials and Their Application in Photodegradation of Organic Pollutants

ZnO and black TiO2 have been selected as the most efficient materials for organic pollution abatement due to their increased efficiency when compared to other materials. However, the concept of green chemistry makes it desirable to design green synthesis approaches for their production. In this study, black TiO2 was synthesized using an environmentally safe synthetic technique with glycerol as a reductant. ZnO was prepared by using ionic-liquid-based microwave-assisted extracts of Polygonum minus. To investigate the materials' potential to photodegrade organic pollutants, methylene blue (MB) and phenol were chosen as model organic pollutants. Both materials were found to exhibit spherical morphologies and a mesoporous structure and were efficient absorbers of visible light. ZnO exhibited electron-hole pair recombination lower than that of black TiO2. Black TiO2 was discovered to be an anatase phase, whereas ZnO was found to have a hexagonal wurtzite structure. In contrast to black TiO2, which had a surface area of 239.99 m2/g and a particle size of 28 nm, ZnO had a surface area of 353.11 m2/g and a particle size of 32 nm. With a degradation time of 60 min, ZnO was able to eliminate 97.50% of the 40 mg/L MB. Black TiO2, on the other hand, could reduce 90.0% of the same amount of MB in 60 min. When tested for phenol degradation, ZnO and black TiO2 activities were reduced by nearly 15 and 25%, respectively. A detailed examination of both ZnO and black TiO2 materials revealed that ZnO has more potential and versatility for the degradation of organic pollutants under visible light irradiation.

Influence of wastewater matrix on the visible light degradation of phenol using AgCl/Bi24O31Cl10 photocatalyst

A significant amount of research has been conducted on the development and application of photocatalytic materials for the visible light degradation of organic pollutants in wastewater. However, most pollutant degradation studies are conducted using simulated wastewater often prepared using DI water. This is far removed from the realities of environmentally relevant water systems. It is therefore important to investigate the activity of these semiconductor materials with real water samples. In this study, the photocatalytic activity of the photocatalyst was investigated in the secondary effluent of a wastewater treatment plant (WWTP) in Pretoria, South Africa, for the degradation of phenol under visible light irradiation. The experimental design was done using the Taguchi method L16 orthogonal tray with three factors (pH, initial phenol concentration, and photocatalyst dosage) and four levels. The results show that pH is the highest-ranked significant factor influencing the degradation rate, closely followed by the initial concentration of the pollutant. The photocatalyst dosage had the least significant impact on degradation. The effects of individual anion components such as Cl-, NO3-, NO2-, SO42- and cations such as Ca2+, Mg2+, Zn2+, and K+ were investigated. While Cl- did not negatively influence the degradation rate, the results show that NO3- and SO42- inhibit the degradation of phenol. More specifically, the presence of nitrites resulted in total impeding of the degradation process illustrating that nitrite concentrations ≥ 20 ppm should be removed from wastewater prior to photocatalytic degradation. The cations investigated promoted the degradation of phenol. Generally, there was enhanced degradation in the water matrix when compared to DI water, and the results revealed improved degradation efficiency due to the cumulative impact of various components of the wastewater.

Optimization of adsorption performance of cerium-loaded intercalated bentonite by CCD-RSM and GA-BPNN and its application in simultaneous removal of phosphorus and ammonia nitrogen

Excessive phosphorus (P) and ammonia nitrogen (NH₃-N) in water bodies can lead to eutrophication of the aquatic environment. Therefore, it is important to develop a technology that can efficiently remove P and NH₃-N from water. Here, the adsorption performance of cerium-loaded intercalated bentonite (Ce-bentonite) was optimized based on single-factor experiments using central composite design-response surface methodology (CCD-RSM) and genetic algorithm-back propagation neural network (GA-BPNN) models. Based on the determination coefficient (R²), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE), the GA-BPNN model was found to be more accurate in predicting adsorption conditions than the CCD-RSM model. The validation results showed that the removal efficiency of P and NH₃-N by Ce-bentonite under optimal adsorption conditions (adsorbent dosage = 1.0 g, adsorption time = 60 min, pH = 8, initial concentration = 30 mg/L) reached 95.70% and 65.93%. Furthermore, based on the application of these optimal conditions in simultaneous removal of P and NH₃-N by Ce-bentonite, pseudo-second order and Freundlich models were able to better analyze adsorption kinetics and isotherms. It is concluded that the optimization of experimental conditions by GA-BPNN has some guidance and provides a new approach to explore adsorption performance after optimizing the conditions.

Optimized photodegradation of palm oil agroindustry waste effluent using multivalent manganese-modified black titanium dioxide

This article presents a methodological approach to use manganese (Mn³⁺Mn⁷⁺)-modified black titanium dioxide (Mn/BTiO₂) as a photocatalyst to optimize and improve visible-light-driven photodegradation of treated agro-industrial effluent (TPOME). A modified wet chemical process was used to prepare BTiO₂. The BTiO₂ was then wet impregnated with Mn and calcined at 300 °C for 1 h to produce Mn/BTiO₂. The activity of Mn/BTiO₂ was investigated in terms of photo-assisted elimination of chemical oxygen demand (COD), phenolic compounds (PCs), color, and total organic carbon (TOC). Using the design of experiments (DOE), the conditions of the photocatalytic process, including photocatalyst loading, Mn concentration, hydrogen peroxide (H₂O₂) dose, and irradiation time, were optimized. Under the optimum conditions (0.85 g/L photocatalyst loading, 0.048 mol/L H₂O₂ dose, 0.301 wt.% Mn concentration, and 204 min irradiation time) COD, PCs, color, and TOC removal efficiencies of 88.87%, 86.04%, 62.8%, and 84.66%, respectively, were obtained. Statistical analysis showed that the response variable's removal from TPOME estimation had high R² and low RMSE, MSE, MAD, MAE, and MAPE values, indicating high reliability. This study demonstrated the significant potential of the developed photocatalytic system for the treatment of waste effluent generated by the palm oil industry and other agro-industries, with the ability to simultaneously reduce a number of organic pollution indicators (OPIs).

A response surface modeling and optimization of photocatalytic degradation of 2,4-dichlorophenol in water using hierarchical nano-assemblages of CuBi2O4 particles

Photocatalytic degradation, as an advanced oxidation process (AOPs), offers a great advantage to target persistent organic pollutants (POPs) in water. RSM in the present study which is statistical means for optimizing processes like photocatalysis with minimum laboratory experimentation. RSM has a history of being a potent design experiment tool for creating new processes, modifying their designs, and optimizing their performances. Herein, a highly sought-after, easily preparable, visible-light active, copper bismuth oxide (CuBi₂O₄) is applied against a toxic emerging contaminant, 2,4-dichlorophenol (2,4-DCP) under an LED light source (viible light λ > 420 nm). A simple coprecipitation method was adopted to synthesize CuBi₂O₄ and later analyzed with FESEM, EDX, XRD, FTIR, and spectroscopy to determine its intrinsic properties. Principally, the photocatalytic degradation investigations were based on response surface methodology (RSM), which is a commanding tool in the optimization of the processes. The 2,4-DCP concentration (pollutant loading), CuBi₂O₄ dosage (catalyst dosge), contact time, and pH were the chosen as dependent factors, that were optimized. However, under optimal conditions, the CuBi₂O₄ nanoparticle showed a remarkable photocatalytic performance of 91.6% at pH = 11.0 with a pollutant concentration of 0.5 mg/L and a catalyst dose of 5 mg/L within 8 h. The obtained RSM model showed a satisfactory correlation between experimental and predicted values of 2,4-DCP removal, with an agreeable probability value (p) of 0.0069 and coefficient of regression (R²) of 0.990. It is therefore anticipated that the study may open up new possibilities for formulating a plan to specifically target these organic pollutants. In addition, CuBi₂O₄ possessed fair reusability for three-consequent cycles. Hence, the as-synthesized nanoparticles applied for photocatalysis foster a fit-for-purpose and reliable system in the decontamination of 2,4 DCP in environmental samples, and also the study highlights the efficient use of RSM for environmental remediation, particularly in AOP implementation.

Photo-Antibacterial Activity of Two-Dimensional (2D)-Based Hybrid Materials: Effective Treatment Strategy for Controlling Bacterial Infection

Bacterial contamination in water bodies is a severe scourge that affects human health and causes mortality and morbidity. Researchers continue to develop next-generation materials for controlling bacterial infections from water. Photo-antibacterial activity continues to gain the interest of researchers due to its adequate, rapid, and antibiotic-free process. Photo-antibacterial materials do not have any side effects and have a minimal chance of developing bacterial resistance due to their rapid efficacy. Photocatalytic two-dimensional nanomaterials (2D-NMs) have great potential for the control of bacterial infection due to their exceptional properties, such as high surface area, tunable band gap, specific structure, and tunable surface functional groups. Moreover, the optical and electric properties of 2D-NMs might be tuned by creating heterojunctions or by the doping of metals/carbon/polymers, subsequently enhancing their photo-antibacterial ability. This review article focuses on the synthesis of 2D-NM-based hybrid materials, the effect of dopants in 2D-NMs, and their photo-antibacterial application. We also discuss how we could improve photo-antibacterials by using different strategies and the role of artificial intelligence (AI) in the photocatalyst and in the degradation of pollutants. Finally, we discuss was of improving the photo-antibacterial activity of 2D-NMs, the toxicity mechanism, and their challenges.

Mesoporous LaVO4/MCM-48 nanocomposite with visible-light-driven photocatalytic degradation of phenol in wastewater

Dearomatization through photocatalytic oxidation is a swiftly rising phenolic compounds removal technology that works at trifling operations requirements with a special emphasis on the generation of nontoxic products. The study aims to develop a LaVO₄/MCM-48 nanocomposite that was prepared via a hydrothermally approach assisting the employment of an MCM-48 matrix, which was then utilized for phenol degradation processes. Various techniques including UV-Vis DRS, FTIR, PL, Raman, TEM, and BET analyses are employed to characterize the developed photocatalyst. The developed photocatalyst presented remarkable characteristics, especially increased light photon utilization, and reduced recombination rate leading to enhanced visible-light-driven photodegradation performance owing to the improved specific surface area, specific porosities, and <2 eV narrow energy bandgap. The LaVO₄/MCM-48 nanocomposite was experienced on aqueous phenol solution having 20 mg/L concentration under visible-light exposure, demonstrating exceptional performance in photodegradation up to 99.28%, comparatively higher than pure LaVO₄. The conducted kinetic measurements revealed good accordance with pseudo first-order. A possible reaction mechanism for photocatalytic degradation was also predicted. The as-synthesized LaVO₄/MCM-48 nanocomposite presented excellent stability and recyclability.

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.

Anaerobic Digested Wastewater CO2 Sequestration Using a Biophotocatalytic System with a Magnetized Photocatalyst (Fe-TiO2)

This study presents a biophotocatalytic system as a sustainable technology for the recovery of clean water and renewable energy from wastewater, thereby providing a unique opportunity to drive industrialization and global sustainable development throughputs. Herein, inhouse magnetized photocatalyst (Fe-TiO₂) with surface area 62.73 m²/g synthesized via co-precipitation, was hypothesized to hasten an up-flow anaerobic sludge blanket (UASB) reactor for the treatment of local South Africa municipality wastewater with the benefit of high-quality biogas production. A lab scale UASB process with a working volume of 5 L coupled with two UV-lights (T8 blacklight-blue tube, 365 nm, 18 W) was operated batchwise under mesophilic conditions for the period of 30 days with a constant organic load charge of 2.76 kg COD/m³. d. This biophotocatalytic system performance was investigated and compared with and without the Fe-TiO₂ charge (2–6 g) with respect to effluent quality, biogas production and CO₂ methanation. Using chemical oxygen demand (COD) measured as the degree of degradation of the pollutants, the best efficiency of 93% COD removal was achieved by a 4 g Fe-TiO₂ charge at 14 days and pH of 7.13, as compared to zero charge where only 49.6% degradation was achieved. Under the same charge, cumulative biogas and methane content of 1500 mL/g COD.d and 85% were respectively attained as compared with the control with 400 mL/g COD.d and 65% methane content. Also, the energy produced can be used to offset the energy utilized by the UV-light for the wastewater abatement and other limitations of photocatalysis. The BP system was found to be an eco-friendly and cost-effective technology to be explored in water treatment settings.

Coprecipitation aided synthesis of bimetallic silver tungstate: a response surface simulation of sunlight-driven photocatalytic removal of 2,4-dichlorophenol

In the present study, the response surface methodology (RSM) model was used to investigate the photocatalytic performance of silver tungstate (Ag₂WO₄) in the removal of 2,4-dichlorophenol (2,4-DCP) under natural sunlight. The Ag₂WO₄ which has nanoflower-like structure was synthesized by a coprecipitation method. The synthesized photocatalyst was characterized for FESEM, TEM, EDX, XRD, FTIR, and UV-Vis spectroscopy. RSM was employed to scrutinize the suitable model to yield a profound pollutant removal rate. The four independent factors such as pollutant concentration, catalyst dosage, pH, and contact time are simulated using RSM. A total of 91% of 2,4-DCP degradation was achieved at a higher catalyst dosage and lower pollutant concentration with a contact duration of 8 h in an alkaline pH condition. The coefficient of regression (R²) and probability value (P) were 0.98 and 0.0472, respectively, which confirmed the ideality of RSM modeling. The study discusses on the possible photocatalytic degradation mechanisms of 2,4-DCP. The results showed a significant dependence of the photocatalytic removal of 2,4-DCP on the functional parameters.

One-Step Coprecipitation Synthesis of BiOClxBr1-x Photocatalysts Decorated with CQDs at Room Temperature with Enhanced Visible-Light Response

BiOCl_xBr_1-x (0 ≤ x ≤ 1) solid solutions were synthesized at room temperature by one-step coprecipitation. Relative proportions of halogens in the anion layer were regulated, and thus, the band gap of BiOCl_xBr_1-x could be adjusted to suitable values to enhance the photocatalytic reaction. BiOCl_xBr_1-x exhibited enhanced visible-light response and higher photocatalytic activity in degrading rhodamine B (RhB) compared with individual BiOCl or BiOBr. Especially, BiOCl_0.5Br_0.5 showed the highest photocatalytic activity. Comparative tests showed that within 36 min the degradation rates of RhB upon BiOBr, BiOCl, and BiOCl_0.5Br_0.5 were 55.66, 24.03, and 94.91%, respectively. BiOCl_0.5Br_0.5 was further decorated with carbon quantum dots (CQDs) to promote the separation of photogenerated charge carriers. The photocatalytic activity was considerably enhanced by moderate doping of CQDs, and the degradation rate of RhB reached nearly 100% within 18 min upon 3CQDs-BiOCl_0.5Br_0.5 (the loading content of CQDs was 0.42 wt %). Active-species-trapping tests confirmed that h⁺ is the primary active species for photocatalytic degradation of RhB, whereas ^•O₂^- and e^- were the secondary ones. The synergistic effects of the band structure adjustment and CQD decoration on the photocatalytic activity were mainly expounded as the enhanced separation of photogenerated charge carriers and optimal redox potentials. In addition, the reuse and service life of the catalysts were analyzed. After five cycles, the photocatalytic activity still remained over 95%.

Mn3O4 Catalysts for Advanced Oxidation of Phenolic Contaminants in Aqueous Solutions

Water-soluble organic pollutants, such as phenolic compounds, have been exposed to environments globally. They have a significant impact on groundwater and surface water quality. In this work, different Mn3O4 catalysts were prepared for metal oxide activation of peroxymonosulfate (PMS) to remove the phenolic compound from the water environment. The as-prepared catalysts were characterized using thermogravimetric-differential thermal analysis (TG-DTA), powder X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET) surface area analysis. Furthermore, the effect of temperature and reusability of the as-prepared Mn3O4 catalysts is also investigated. The Mn3O4 nanoparticles (NPs) catalyst reveals an excellent performance for activating PMS to remove phenol compounds. Mn3O4 NPs exhibits 96.057% efficiency in removing 25 ppm within 60 min. The kinetic analysis shows that Mn3O4 NPs fitted into pseudo-first order kinetic model and exhibited relatively low energy activation of 42.6 kJ/mol. The reusability test of Mn3O4 NPs displays exceptional stability with 84.29% efficiency after three-sequential cycles. The as-prepared Mn3O4 NPs is proven suitable for phenolic remediation in aqueous solutions.

Pumice-supported ZnO-photocatalyzed degradation of organic pollutant in textile effluent: optimization by response surface methodology, artificial neural network, and adaptive neural-fuzzy inference system

A heterogeneous photocatalysis was adopted to treat textile industry effluent using a combination of pumice-supported ZnO (PUM-ZnO) photocatalyst and solar irradiation. The visible light-responsive PUM-ZnO photocatalyst was prepared via the impregnation method and characterized using various spectroscopic techniques. The photocatalytic degradation process was modeled via response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS), while the optimization of the three independent parameters significant to the photocatalytic process was carried out by a genetic algorithm (GA) and RSM methods. The low standard error of prediction (SEP) of 0.56-1.75% and high coefficient of determination (R²) greater than 0.96 for the models developed indicated that they adequately predicted the photodegradation process with high accuracy in the order of ANFIS > ANN > RSM. The process optimization results from the developed models showed that GA performed better than RSM. The best optimal condition (3.29 g/L catalyst dosage, 45.85 min irradiation time, and 3.13 effluent pH) that resulted in maximum degradation efficiency of 99.46% was achieved by the ANFIS model coupled with GA (ANFIS-GA).

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R² value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m² d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment.

Response Surface Methodology for Optimization of Rotating Biological Contactor Combined with External Membrane Filtration for Wastewater Treatment

A large amount of wastewater is directly discharged into water bodies without treatment, causing surface water contamination. A rotating biological contactor (RBC) is an attached biological wastewater treatment process that offers a low energy footprint. However, its unstable removal efficiency makes it less popular. This study optimized operating parameters in RBC combined with external membrane filtration (RBC-ME), in which the latter acted as a post-treatment step to stabilize the biological performance. Response surface methodology (RSM) was employed to optimize the biological and filtration performance by exploiting three parameters, namely disk rotation, hydraulic retention time (HRT), and sludge retention time (SRT). Results show that the RBC-ME exhibited superior biological treatment capacity and higher effluent quality compared to stand-alone RBC. It attained 87.9 ± 3.2% of chemical oxygen demand, 45.2 ± 0.7% total nitrogen, 97.9 ± 0.1% turbidity, and 98.9 ± 1.1% ammonia removals. The RSM showed a good agreement between the model and the experimental data. The maximum permeability of 144.6 L/m2 h bar could be achieved under the optimum parameters of 36.1 rpm disk rotation, 18 h HRT, and 14.9 d SRT. This work demonstrated the effective use of statistical modeling to enhance RBC-ME system performance to obtain a sustainable and energy-efficient condition.

Biophotocatalytic Reduction of CO2 in Anaerobic Biogas Produced from Wastewater Treatment Using an Integrated System

This study presents the bio-photocatalytic upgrading of biogas utilising carbon dioxide (CO2) as a potential option for beginning fossil fuel depletion and the associated environmental risks in the pursuit of sustainable development. Herein, magnetite photocatalyst (Fe-TiO2) was employed with an integrated anaerobic-photomagnetic system for the decontamination of municipality wastewater for biogas production. The Fe-TiO2 photocatalyst used, manufactured via a co-precipitation technique, had a specific surface area of 62.73 m2/g, micropore volume of 0.017 cm3/g and pore size of 1.337 nm. The results showed that using the ultraviolet-visible (UV-Vis) photomagnetic system as a post-treatment to the anaerobic digestion (AD) process was very effective with over 85% reduction in colour, chemical oxygen demand (COD) and turbidity. With an organic loading rate (OLR) of 0.394 kg COD/L·d and hydraulic retention time (HTR) of 21 days, a 92% degradation of the organic content (1.64 kgCOD/L) was attained. This maximised the bioenergy production to 5.52 kWh/m3 with over 10% excess energy to offset the energy demand of the UV-Vis lamp. Assuming 33% of the bioenergy produced was used as electricity to power the UV-Vis lamp, the CO2 emission reduction was 1.74 kg CO2 e/m3, with good potential for environmental conservation.

Modified TiO₂ nanotubes-zeolite composite photocatalyst: Characteristics, microstructure and applicability for degrading triclocarban

The study explored the characteristics and effectiveness of modified TiO₂ nanotubes with zeolite as a composite photocatalyst (MTNZC) for the degradation of triclocarban (TCC) from the aqueous solution. MTNZC samples have been produced via electrochemical anodisation (ECA) followed by electrophoretic deposition (EPD). Three independent factors selected include MTNZC size (0.5-1 cm²), pH (3-10), and irradiation time (10-60 min). The observation revealed that the surface of Ti substrate by the 40 V of anodisation and 3 h of calcination was covered with the array ordered, smooth and optimum elongated nanotubes with average tube length was approximately 5.1 μm. EDS analysis proved the presence of Si, Mg, Al, and Na on MTNZC due to the chemical composition present in the zeolite. The average crystallite size of TiO₂ nanotubes increased from 2.07 to 3.95 nm by increasing anodisation voltage (10, 40, and 60 V) followed by 450 °C of calcination for 1, 3, and 6 h, respectively. The optimisation by RSM shows the F-value (36.12), the p-value of all responses were less than 0.0001, and the 95% confidence level of the model by all the responses indicated the model was significant. The R² in the range of 0.9433-0.9906 showed the suitability of the model to represent the actual relationship among the parameters. The photocatalytic degradation rate of TCC from the first and the fifth cycles were 94.2 and 77.4%, indicating the applicability of MTNZC to be used for several cycles.

Synthesis and Characterization of Carbon and Carbon-Nitrogen Doped Black TiO2 Nanomaterials and Their Application in Sonophotocatalytic Remediation of Treated Agro-Industrial Wastewater

The conventional open ponding system employed for palm oil mill agro-effluent (POME) treatment fails to lower the levels of organic pollutants to the mandatory standard discharge limits. In this work, carbon doped black TiO₂ (CB-TiO₂) and carbon-nitrogen co-doped black TiO₂ (CNB-TiO₂) were synthesized via glycerol assisted sol-gel techniques and employed for the remediation of treated palm oil mill effluent (TPOME). Both the samples were anatase phase, with a crystallite size of 11.09–22.18 nm, lower bandgap of 2.06–2.63 eV, superior visible light absorption ability, and a high surface area of 239.99–347.26 m²/g. The performance of CNB-TiO₂ was higher (51.48%) compared to only (45.72%) CB-TiO₂. Thus, the CNB-TiO₂ is employed in sonophotocatalytic reactions. Sonophotocatalytic process based on CNB-TiO₂, assisted by hydrogen peroxide (H₂O₂), and operated at an ultrasonication (US) frequency of 30 kHz and 40 W power under visible light irradiation proved to be the most efficient for chemical oxygen demand (COD) removal. More than 90% of COD was removed within 60 min of sonophotocatalytic reaction, producing the effluent with the COD concentration well below the stipulated permissible limit of 50 mg/L. The electrical energy required per order of magnitude was estimated to be only 177.59 kWh/m³, indicating extreme viability of the proposed process for the remediation of TPOME.

Analysis of Photocatalytic Degradation of Phenol with Exfoliated Graphitic Carbon Nitride and Light-Emitting Diodes Using Response Surface Methodology

Response surface methodology (RSM) involving a Box–Benkhen design (BBD) was employed to analyze the photocatalytic degradation of phenol using exfoliated graphitic carbon nitride (g-C3N4) and light-emitting diodes (wavelength = 430 nm). The interaction between three parameters, namely, catalyst concentration (0.25–0.75 g/L), pollutant concentration (20–100 ppm), and pH of the solution (3–10), was examined and modeled. An empirical regression quadratic model was developed to relate the phenol degradation efficiency with these three parameters. Analysis of variance (ANOVA) was then applied to examine the significance of the model; this showed that the model is significant with an insignificant lack of fit and an R2 of 0.96. The statistical analysis demonstrated that, in the studied range, phenol concentration considerably affected phenol degradation. The RSM model shows a significant correlation between predicted and experimental values of photocatalytic degradation of phenol. The model’s accuracy was tested for 50 ppm of phenol under optimal conditions involving a catalyst concentration of 0.4 g/L catalysts and a solution pH of 6.5. The model predicted a degradation efficiency of 88.62%, whereas the experimentally achieved efficiency was 83.75%.

Data mining for pesticide decontamination using heterogeneous photocatalytic processes

Pesticides are chemical compounds used to kill pests and weeds. Due to their nature, pesticides are potentially toxic to many organisms, including humans. Among the various methods used to decontaminate pesticides from the environment, the heterogeneous photocatalytic process is one of the most effective approaches. This study focuses on artificial intelligence (AI) techniques used to generate optimum predictive models for pesticide decontamination processes using heterogeneous photocatalytic processes. In the present study, 537 valid cases from 45 articles from January 2000 to April 2020 were filtered based on their content collected and analyzed. Based on cross-industry standard process (CRISP) methodology, a set of four classifiers were applied: Decision Trees (DT), Bayesian Network (BN), Support Vector Machines (SVM), and Feed Forward Multilayer Perceptron Neural Networks (MLP). To compare the accuracy of the selected algorithms, accuracy, and sensitivity criteria were applied. After the final analysis, the DT classification algorithm with seven factors of prediction, the accuracy of 91.06%, and sensitivity of 80.32% was selected as the optimal predictor model.

Artificial neural network modeling of cefixime photodegradation by synthesized CoBi2O4 nanoparticles

CoBi₂O₄ (CBO) nanoparticles were synthesized by sol-gel method using polyvinylpyrrolidone (PVP) as a complexing reagent. For a single phase with the spinel structure, the formed gel was dried and calcined at four temperatures stages. Various methods were used to identify and characterize the obtained spinel, such as X-ray diffraction (XRD), scanning electron micrograph (SEM-EDX), transmission electron microscope (TEM), Fourier transform infrared (FT-IR), X-ray fluorescence (XRF), Raman, and UV-Vis spectroscopies. The photocatalytic activity of CBO was examined for the degradation of a pharmaceutical product cefixime (CFX). Furthermore, for the prediction of the CFX degradation rate, an artificial neural network model was used. The network was trained using the experimental data obtained at different pH with different CBO doses and initial CFX concentrations. To optimize the network, various algorithms and transfer functions for the hidden layer were tested. By calculating the mean square error (MSE), 13 neurons were found to be the optimal number of neurons and produced the highest coefficient of correlation R² of 99.6%. The relative significance of the input variables was calculated, and the most impacting input was proved to be the initial CFX concentration. The effects of some scavenging agents were also studied. The results confirmed the dominant role of hydroxyl radical OH^• in the degradation process. With the novel CoBi₂O₄/ZnO hetero-system, the photocatalytic performance has been enhanced, giving an 80% degradation yield of CFX (10 mg/L) at neutral pH in only 3 h.

Modeling and Optimizing of NH4+ Removal from Stormwater by Coal-Based Granular Activated Carbon Using RSM and ANN Coupled with GA

As a key parameter in the adsorption process, removal rate is not available under most operating conditions due to the time and cost of experimental testing. To address this issue, evaluation of the efficiency of NH4+ removal from stormwater by coal-based granular activated carbon (CB-GAC), a novel approach, the response surface methodology (RSM), back-propagation artificial neural network (BP-ANN) coupled with genetic algorithm (GA), has been applied in this research. The sorption process was modeled based on Box-Behnben design (BBD) RSM method for independent variables: Contact time, initial concentration, temperature, and pH; suggesting a quadratic polynomial model with p-value < 0.001, R2 = 0.9762. The BP-ANN with a structure of 4-8-1 gave the best performance. Compared with the BBD-RSM model, the BP-ANN model indicated better prediction of the response with R2 = 0.9959. The weights derived from BP-ANN was further analyzed by Garson equation, and the results showed that the order of the variables’ effectiveness is as follow: Contact time (31.23%) > pH (24.68%) > temperature (22.93%) > initial concentration (21.16%). The process parameters were optimized via RSM optimization tools and GA. The results of validation experiments showed that the optimization results of GA-ANN are more accurate than BBD-RSM, with contact time = 899.41 min, initial concentration = 17.35 mg/L, temperature = 15 °C, pH = 6.98, NH4+ removal rate = 63.74%, and relative error = 0.87%. Furthermore, the CB-GAC has been characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Brunauer-Emmett-Teller (BET). The isotherm and kinetic studies of the adsorption process illustrated that adsorption of NH4+ onto CB-GAC corresponded Langmuir isotherm and pseudo-second-order kinetic models. The calculated maximum adsorption capacity was 0.2821 mg/g.

Eucheuma cottonii Seaweed-Based Biochar for Adsorption of Methylene Blue Dye

Pollution from dye containing wastewater leads to a variety of environmental problems, which can destroy plant life and eco-systems. This study reports development of a seaweed-based biochar as an adsorbent material for efficient adsorption of methylene blue (MB) dye from synthetic wastewater. The Eucheuma cottonii seaweed biochar was developed through pyrolysis using a tube furnace with N2 gas, and the properties were later improved by sulfuric acid treatment. The adsorption studies were conducted in a batch experimental setup under initial methylene blue concentrations of 50 to 200 mg/L, solution pH of 2 to 10, and temperature of 25 to 75 °C. The characterization results show that the developed biochar had a mesoporous pore morphology. The adsorbent possessed the surface area, pore size, and pore volume of 640 m2/g, 2.32 nm, and 0.54 cm3/g, respectively. An adsorption test for 200 mg/L of initial methylene blue at pH 4 showed the best performance. The adsorption data of the seaweed-based biochar followed the Langmuir isotherm adsorption model and the pseudo-second-order kinetic model, with the corresponding R2 of 0.994 and 0.995. The maximum adsorption capacity of methylene blue using the developed seaweed‑based biochar was 133.33 mg/g. The adsorption followed the chemisorption mechanism, which occurred via the formation of a monolayer of methylene blue dye on the seaweed-based biochar surface. The adsorption performance of the produced seaweed biochar is comparable to that of other commercial adsorbents, suggesting its potential for large-scale applications.