Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling

Response surface methodology for process optimization in livestock wastewater treatment: A review

With increasing demand for meat and dairy products, the volume of wastewater generated from the livestock industry has become a significant environmental concern. The treatment of livestock wastewater (LWW) is a challenging process that involves removing nutrients, organic matter, pathogens, and other pollutants from livestock manure and urine. In response to this challenge, researchers have developed and investigated different biological, physical, and chemical treatment technologies that perform better upon optimization. Optimization of LWW handling processes can help improve the efficacy and sustainability of treatment systems as well as minimize environmental impacts and associated costs. Response surface methodology (RSM) as an optimization approach can effectively optimize operational parameters that affect process performance. This review article summarizes the main steps of RSM, recent applications of RSM in LWW treatment, highlights the advantages and limitations of this technique, and provides recommendations for future research and practice, including its cost-effectiveness, accuracy, and ability to improve treatment efficiency.

WITHDRAWN: Effects of hydraulic retention time and cultivation on nutrients removal and biomass production in wastewater by membrane photobioreactor: Modeling and optimization by machine learning and response surface methodology

This article has been withdrawn at the request of the Editor-in-Chief.

Degradation of formaldehyde by photo-Fenton process over n-ZVI/TiO2 catalyst

The degradation of formaldehyde in a photo-Fenton reaction was studied using n-ZVI/TiO2 as the catalyst. The effects of %n-ZVI loading, catalyst dosage, H2O2, and pH on formaldehyde degradation were studied. The n-ZVI/TiO2 catalysts were prepared by impregnation with chemical reduction, and their catalytic activity was evaluated in a batch reactor under UVC light. Transmission electron microscopy (TEM) was used to determine that the n-ZVI nanoparticle size was 39.41 nm. X-ray photoelectron spectroscopy (XPS) was used to study the oxidation states of 2%n‑ZVI/TiO2, and the Fe 2p spectrum of 2%n-ZVI/TiO2 indicated the presence of Fe0. The optimal conditions for the complete removal of formaldehyde within 30 min were an n-ZVI loading of 2 wt.%, a catalyst dosage of 0.5 g/L, 30 mM H2O2, and an initial pH of 3. After the reaction, the C-H functional group of formaldehyde was not observed due to the •OH radicals generated by Fe0 and H2O2 attacking the formaldehyde molecule. Moreover, no Fe leaching was observed, presenting an advantage compared with homogeneous Fe catalysts. Therefore, 2%n‑ZVI/TiO2 shows excellent potential as a photo-Fenton catalyst for the environmentally friendly degradation of formaldehyde.

High-temperature decomposition chemistry of trimethylsiloxane surfactants, a potential Fluorine-Free replacement for fire suppression

Per- and polyfluoroalkyl substances (PFAS) have become global environmental contaminants due to being notoriously difficult to degrade, and it has become increasingly important to employ suitable PFAS alternatives, especially in aqueous film-forming foams (AFFF). Trimethylsiloxane (TriSil) surfactants are potential fluorine-free replacements for PFAS in fire suppression technologies. Yet because these compounds may be more susceptible to high-temperature decomposition, it is necessary to assess the potential environmental impact of their thermal degradation products. Our study analyzes the high-temperature degradation of a truncated trimethylsiloxane (TriSil-1n) surfactant based on quantum mechanical methods. The degradation chemistry of TriSil-1n was studied through radical formation and propagation initiated from two prominent pathways (unimolecular and bimolecular reactions) at both 298 K and 1200 K, a relevant temperature in flames and thermal incinerators. Regardless of the pathway taken and temperature, all radical intermediates stemmed from the polyethylene glycol chain and primarily formed stable polydimethylsiloxanes (PDMS) and small organics such as ethylene, formaldehyde, and acetaldehyde, among other products. The major degradation products of TriSil-1n resulting from high-temperature thermal degradation as predicted by this study would be relatively less harmful to the environment compared to PFAS incineration/combustion products from previous research, supporting the replacement of PFAS with TriSil surfactants.

Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models

Predictive capability of response surface methodology (RSM) and ant colony optimization combined with support vector regression (ACO-SVR) models are applied for determining optimal parameters in the process of heterogeneous Fenton oxidation of melanoidin, a high molecular weight polymer widely produced during fermentation processes generating large quantities of wastewater with intense brown color and extremely high chemical oxygen demand (COD). Prediction of the performance of nano zero-valent iron supported on activated carbon cloth-chitosan (ACC-CH-nZVI) catalysts was carried out using Box-Behnken design (BBD) and analysis of variance to evaluate the interaction of independent variables involved in heterogeneous Fenton reaction. The optimized condition with minimal consumption of H₂O₂ (173 mM) resulted in 77.1% decolorization of melanoidin-contaminated water corresponding to 74.4% COD removal at pH 3 (600 mg/l Fe dosage) for 90 min reaction time. The corresponding weight ratio of H₂O₂ to COD was 0.98, much lower than the stoichiometric value 2.125, indicating the effectiveness of ACC-CH-nZVI as a heterogeneous Fenton-like catalyst. In comparison to previously published experimental results, ACO-SVR model shows higher coefficient of determination (R²; 0.9983) but lower root mean squared error (RMSE) and mean absolute error (MAE) than those of RSM model, indicating relative superiority in prediction capability. Besides, ACO algorithm appears to be a promising tool for improving forecasting accuracy of SVR model. This work demonstrates the applicability of ACO-SVR model in predicting the performance of wastewater treatment using Fenton process with limited number of experiment and exhibits satisfactory prediction results.