Municipal wastewater treatment systems use the chemical oxygen demand test (COD) to identify organic contaminants in industrial effluents that impede treatment due to their high concentration. This study reduced the COD levels in tannery wastewater using a multistage treatment process that included Fenton oxidation, chemical coagulation, and nanotechnology based on a synthetic soluble COD standard solution. At an acidic pH of 5, Fenton oxidation reduces the COD concentration by approximately 79%. It achieves this by combining 10 mL/L of H2O2 and 0.1 g/L of FeCl2. Furthermore, the author selected the FeCl3 coagulant for the coagulation process based on the best results of comparisons between different coagulants. At pH 8.5, the coagulation dose of 0.15 g/L achieved the maximum COD removal efficiency of approximately 56.7%. Finally, nano bimetallic Fe/Cu was used to complete the degradation and adsorption of the remaining organic pollutants. The XRD, SEM, and EDX analyses proved the formation of Fe/Cu nanoparticles. A dose of 0.09 g/L Fe/Cu NPs, 30 min of contact time, and a stirring rate of 200 rpm achieve a maximum removal efficiency of about 93% of COD at pH 7.5. The kinetics studies were analyzed using pseudo-first-order P.F.O., pseudo-second-order P.S.O., and intraparticle diffusion models. The P.S.O. showed the best fit among the kinetic models, with an R2 of 0.998. Finally, the authors recommended that technique for highly contaminated industrial effluents treatment for agriculture or industrial purposes.

Oil refineries produce annually large quantities of oily sludge and non-biodegradable wastewater during petroleum refining that require adequate management to minimize its environmental impact. The fraction solid of the oily sludge accounts for 25 wt% and without treatment for their valorization. This work is focused on the valorization of these solid particles through their transformation into porous materials with enhanced properties and with potential application in the catalytic wet air oxidation (CWAO) of a non-biodegradable spent caustic refinery wastewater. Hence, dealing with the valorization and treatment of both refinery wastes in a circular approach aligned with the petrol refinery transformations by 2050. The obtained oily sludge carbonaceous materials showed improved surface area (260-762 m2/g) and a high Fe content. The good catalytic performance of these materials in CWAO processes has been attributed to the simultaneous presence of surface basic sites and iron species. Those materials with higher content of Fe and basic sites yielded the highest degradation of organic compounds present in the spent caustic refinery wastewater. In particular, the best-performing material ACT-NP 1.1 (non-preoxidated and thermically treated with 1:1 mass ratio KOH:solid) showed a chemical oxygen demand (COD) removal of 60 % after 3 h of reaction and with a higher degradation rate than that achieved with thermal oxidation without catalyst (WAO) and that using an iron-free commercial activated carbon. Moreover, the biodegradability of the treated wastewater increased up to 80% (from ca. 31% initially of the untreated effluent). Finally, this material was reused up to three catalytic cycles without losing metal species and keeping the catalytic performance.

Recent escalating concerns surrounding textile wastewater pollution and the urgent need for sustainable treatment solutions to mitigate its environmental impact. This study investigates the multifaceted effects of Spirulina platensis (SP) cultivation in textile wastewater from two different sources (TWW1 and TWW2), focusing on growth kinetics, Chemical Oxygen Demand (COD), and nutrient removal percentage, and seed germination enhancement. Results showed that SP exhibited comparable growth performance in TWW1 and TWW2 to the control, indicating its potential for sustainable wastewater treatment. Moreover, maximum COD removal percentages were achieved, reaching 62.59±1.88 % for TWW1 and 46.68±1.40 % for TWW2 on day 5. The COD removal process aligns best with the first-order kinetic model. Nutrient removal rates showed decreasing trends over time, with maximum phosphate removal percentages of 36.42±0.73 % for TWW1 and 62.18±1.24 % for TWW2, and maximum ammonia removal percentages of 59.34±1.18 % for TWW1 and 69.31±1.39 % for TWW2. FTIR analysis confirmed pollutant removal-induced changes in algal biomass functional groups. Seed germination studies indicated enhanced shoot and root development of vigna radiatas using treated TWW1 and TWW2 compared to the control, suggesting potential applications for irrigation. An increase in the lipid & carbohydrate content post-treatment was observed and it would be suitable for biofuel production. This comprehensive assessment demonstrates the synergistic benefits of phycoremediation in simultaneously removing pollutants, promoting plant growth, and enhancing wastewater treatment efficiency, underscoring its potential for sustainable water management practices.

Water outages caused by elevated ammonium (NH4+-N) levels are a prevalent problem faced by conventional raw water treatment plants in developing countries. A treatment solution requires a short hydraulic retention time (HRT) to overcome nitrification rate limitation in oligotrophic conditions. In this study, the performance of polluted raw water treatment using a green downflow sponge biofilm (DSB) technology was evaluated. We operated two DSB reactors, DSB-1 and DSB-2 under different NH4+-N concentration ranges (DSB-1: 3.2-5.0 mg L-1; DSB-2: 1.7-2.6 mg L-1) over 360 days and monitored their performance under short HRT (60 min, 30 min, 20 min, and 15 min). The experimental results revealed vertical segregation of organic removal in the upper reactor depths and nitrification in the lower depths. Under the shortest HRT of 15 min, both DSB reactors achieved stable NH4+-N and chemical oxygen demand removal (≥95%) and produced minimal effluent nitrite (NO2--N). DSB system could facilitate complete NH4+-N oxidation to nitrate (NO3--N) without external aeration energy requirement. The 16S rRNA sequencing data revealed that nitrifying bacteria Nitrosomonas and Nitrospira in the reactor were stratified. Putative comammox bacteria with high ammonia affinity was successfully enriched in DSB-2 operating at a lower NH4+-N loading rate, which is advantageous in oligotrophic treatment. This study suggests that a high hydraulic rate DSB system with efficient ammonia removal could incorporate ammonia treatment capability into polluted raw water treatment process and ensure safe water supply in many developing countries.

Supercritical water gasification technology provides a favorable technology to achieve pollution elimination and resource utilization of phenolic wastewater. In this study, the reaction mechanism of phenolic wastewater supercritical water gasification was investigated using a combination of experimental and computational methods. Five reaction channels were identified to elucidate the underlying pathway of phenol decomposition. Importantly, the rate-determining step was found to be the dearomatization reaction. By integrating computational and experimental analyses, it was found that phenol decomposition via the path with the lowest energy barrier generates cyclopentadiene, featuring a dearomatization barrier of 70.97 kcal/mol. Additionally, supercritical water plays a catalytic role in the dearomatization process by facilitating proton transfer. Based on the obtained reaction pathway, alkali salts (Na2CO3 and K2CO3) are employed as a catalyst to diminish the energy barrier of the rate-determining step to 40.00 kcal/mol and 37.14 kcal/mol. Alkali salts catalysis significantly improved carbon conversion and pollutant removal from phenolic wastewater, increasing CGE from 58.44% to 93.55% and COD removal efficiency from 94.11% to 99.79%. Overall, this study provides a comprehensive understanding of the decomposition mechanism of phenolic wastewater in supercritical water.

Produced water (PW) is the largest by-product that comes out of the oil wells during oil and gas (O&G) field exploration. PW contains high-salt concentration along with other organic and inorganic components; therefore, PW must be treated before disposal. Electrocoagulation (EC) is an effective treatment method to remove pollutants from PW which has been the focus of many experimental studies; however, a mathematical model specifically for PW treatment by EC has not been developed yet. In this work, a comprehensive mathematical model has been developed to elucidate the role of EC operating parameters on the PW treatment performance and determine the mechanism for COD (Chemical Oxygen Demand) removal. The present model considers and identifies the dominant Al-hydroxy complex species and their contribution to the COD removal from synthetic PW samples by estimating their rate constants and comparing their magnitudes and investigates multi-scale modelling of the EC reactor. The influence of working parameters such as current density, initial pH, interelectrode distance, mixing speed and solution volume of PW on Al coagulant production and COD removal was investigated and modelled. The study estimates the rate constants of the reactions taking place for COD removal by EC process and by comparing their magnitudes identifies the dominant reactions and coagulant species involved in the process. The mathematical model prediction of COD removal fits well with the experimental data at 10 mA cm-2, 15 mA cm-2 and 20 mA cm-2 current density with R2 value of 0.96, 0.97 and 0.92, respectively and for dissolved Al concentration R2 value of 0.96, 0.99, and 0.97, respectively. The simulated results reproduced a good fit at initial pH of 6.1, 7.3 and 8.6 with R2 value of 0.92, 0.96 and 0.98, respectively for COD removal. The mathematical model and the experimental results showed the role of dominant Al-hydroxy complex species such as Al OH 2 + , Al OH 2 + , Al OH 3 , Al 2 OH 2 + 4 and Al OH 4 - in controlling the COD removal process. Under different operating conditions considered in the study, the model also predicted the COD removal performance of the EC reactors at different reactor volumes with R2 value of 0.96 for higher solution volume and larger reactor. The model presented and rate constants determined in the study will provide a theoretical basis for designing, scaling up and operating the EC reactor for oil-field PW treatment.

In this work, the Mn, Co, Ce co-doped corn cob biochar (MCCBC) as catalytic particle electrodes in a three-dimensional heterogeneous electro-Fenton-like (3D-HEFL) system for the efficient degradation of coking wastewater was investigated. Various characterization methods such as SEM, EDS, XRD, XPS and electrochemical analysis were employed for the prepared materials. The results showed that the MCCBC particle electrodes had excellent electrochemical degradation performances of COD in coking wastewater, and the COD removal and degradation rates of the 3D/HEFL system were 85.35% and 0.0563 min-1 respectively. RSM optimized conditions revealed higher COD removal rate at 89.23% after 31.6 min of electrolysis. The efficient degradability and wide adaptability of the 3D/HEFL system were due to its beneficial coupling mechanism, including the synergistic effect between the system factors (3D and HEFL) as well as the synergistic interactions between the ROS (dominated by •OH and supplemented by O2•-) in the system. Moreover, the COD removal rate of MCCBC could still remain at 81.41% after 5 cycles with a lower ion leaching and a specific energy consumption of 11.28 kWh kg-1 COD. The superior performance of MCCBC, as catalytic particle electrodes showed a great potential for engineering applications for the advanced treatment of coking wastewater.

The present study aims to investigate the efficiency of a combined cheese wastewater treatment approach involving coagulation with ferric chloride coupled with a photo-Fenton-like oxidation process for potential reuse in irrigation. Laboratory-scale tests were conducted, examining the effect of various operational parameters on the treatment process. Specifically, the effects of initial wastewater pH, coagulant dosage, decantation time for the coagulation process, and initial pH, chemical oxygen demand (COD) concentration, and Fe3+ and H2O2 dosages for photo-Fenton-like oxidation were studied. Coagulation was found effective at natural pH of 6 and showed a highest removal efficiency in terms of COD (50.6%), biological oxygen demand BOD5 (42.1%), turbidity (99.3%), and least sludge volume generation (11.8% v/v) for an optimum coagulant dose of 400 mg Fe3+ L-1 and 8 h of decantation time. Thereafter, photo-Fenton-like oxidation (Fe3+/H2O2/UVA-300W) of the pretreated cheese effluent enhanced the removal of COD, BOD5 and TOC to 91.2%, 91.4%, and 97.5%, respectively, using the optimized conditions (pH = 3; [Fe3+] = 5.0 × 10-4 mol L-1; [H2O2] = 0.2 mol L-1 and tirr = 24 h). This study also shows that the proposed combined process allowed a significant phytotoxicity reduction toward lentil seed germination. The obtained outcome was encouraging and supports the possible use of the treated cheese wastewater as an additional water source for agricultural irrigation.

The vegetable oil refinery industry generates highly polluted effluents during oil production, necessitating proper treatment before discharge to prevent environmental hazards. Treating such wastewater has become a major environmental concern in developing countries. Chemical oxygen demand (COD) is a key parameter in assessing the wastewater\'s organic pollutant load. High COD levels can lead to reduced dissolved oxygen in water bodies, negatively affecting aquatic life. Various technologies have been employed to treat oily wastewater, but microbial degradation has gained attention due to its potential to remove organic pollutants efficiently. This study aims to optimize the biodegradation treatment process for vegetable oil industrial effluent using response surface methodology (RSM). The wastewater\'s physicochemical properties were characterized to achieve this, and COD removal was analyzed. Furthermore, RSM was used to investigate the combined effects of pH, contact duration, and microbial concentration on COD removal efficiency. The result showed that the microbial strain used recorded a maximum COD removal of 92%. Furthermore, a quadratic model was developed to predict COD removal based on the experimental variables. From the analysis of variance (ANOVA) analysis, the model was found to be significant at p < 0.0004 and accurately predicted COD removal rates within the experimental region, with an R2 value of 90.99% and adjusted R2 value of 82.89%. Contour plots and statistical analysis revealed the importance of contact duration and microbial concentration on COD removal. PRACTITIONER POINTS: Response surface methodology (RSM) optimization achieved a significant chemical oxygen demand (COD) removal efficiency of 92% in vegetable oil industrial effluents. The study\'s success in optimizing COD removal using RSM highlights the potential for efficient and environmentally friendly wastewater treatment. Practitioners can benefit from the identified factors (pH, contact time, and microbial concentration) to enhance the operation of treatment systems. The developed predictive model offers a practical tool for plant operators and engineers to tailor wastewater treatment processes. This research underscores the importance of sustainable practices in wastewater treatment, emphasizing the role of microbial degradation in addressing organic pollutant loads.

A continuous stirred tank bioreactor (CSTB) with cell recycling combined with ceramic membrane technology and inoculated with Rhodococcus opacus PD630 was employed to treat petroleum refinery wastewater for simultaneous chemical oxygen demand (COD) removal and lipid production from the retentate obtained during wastewater treatment. In the present study, the COD removal efficiency (CODRE) (%) and lipid concentration (g/L) were predicted using two artificial intelligence models, i.e., an artificial neural network (ANN) and a neuro-fuzzy neural network (NF-NN) with a network topology of 6-25-2 being the best for NF-NN. The results revealed the superiority of NF-NN over ANN in terms of determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Three learning algorithms were tested with NF-NN; among them, the Bayesian regularization backpropagation (BR-BP) outperformed others. The sensitivity analysis revealed that, if solid retention time and biomass concentrations were maintained between 35 and 75 h and 3.0 g/L and 3.5 g/L, respectively, high CODRE (93%) and lipid concentration (2.8 g/L) could be obtained consistently.