Degradation of nitrobenzene wastewater in an acidic environment by Ti(IV)/H2O2/O3 in a rotating packed bed

End-to-end machine-learning for high-gravity ammonia stripping: Bridging the gap between scientific research and user-friendly applications

The removal and recovery of ammonia from wastewater are critical processes for achieving global environmental sustainability and promoting circular economic development. High-gravity technology is an advanced solution to achieve ammonia stripping from wastewater. This study used machine-learning (ML) techniques to provide more comprehensive insights on various influencing factors, including the operating parameters, wastewater characteristics, and design parameters of rotating packed beds. Bayesian auto-optimization combined with a boosting algorithm effectively overcame the challenges of modeling complex datasets with small sample sizes, multidimensional data, missing values, and skewed distributions. Accurate ML based predictive models for the ammonia removal efficiency (η) and mass transfer coefficient (KLa) were developed, the performance on the training set was R2 = 0.98 and R2 = 0.89, and on the testing set was R2 = 0.98 and R2 = 0.82. The developed model revealed that the stripping stage and gas-liquid ratio were the most influential features for predicting η, whereas the liquid flow and high-gravity factor were the most important features for predicting KLa. The well-trained model was then deployed in an online software application that could provide both predictive and auto-update functions for operators and managers, ensuring that practitioners could use the model. The end-to-end machine-learning approach used in this study-that is, covering data collection, model development, and application-could improve the availability of research results, providing valuable references for the further advancement of technology in the field of environmental.

Efficient utilization of free radicals in advanced oxidation processes under high-gravity environment for disposing pollutants in effluents and gases: A critical review

Advanced oxidation processes (AOPs) using strongly oxidizing radicals are promising for wastewater treatment and gas purification. Nevertheless, the short half-life of radicals and the limited mass transfer in traditional reactors cause under-utilization of radicals and low pollutant removal efficiency. High-gravity technology (HiGee)-enhanced AOPs (HiGee-AOPs) have been demonstrated a promising way to enhance radical utilization in a rotating packed bed reactor (RPB). Here, we review the potential mechanisms of intensified radical utilization in HiGee-AOPs, structures and performance of RPB, and applications of HiGee in AOPs. The intensification mechanisms are described from three aspects: enhanced generation of radicals by efficient mass transfer, in-situ radical utilization under frequent liquid film renewal, and selective effect on radical utilization due to micromixing in RPB. Based on these mechanisms, we propose a novel High-gravity flow reaction with the essence of efficiency, in-situ, and selectivity in order to better explain the strengthening mechanisms in HiGee-AOPs. HiGee-AOPs possess great potential for treating effluent and gaseous pollutants due to characteristics of High-gravity flow reaction. We discuss the pros and cons of different RPBs and their applications to specific HiGee-AOPs. HiGee improve the following AOPs: (1) facilitate interfacial mass transfer in homogeneous AOPs, (2) enhance mass transfer to expose more catalytically active sites and mass-produce nanocatalysts for heterogeneous AOPs, (3) inhibit bubble accumulation on the electrode surface of electrochemical AOPs, (4) increase the mass transfer between liquid and catalysts in UV-assisted AOPs, (5) improve the micromixing efficiency of ultrasound-based AOPs. Strategies outlined in this paper should inspire further development of HiGee-AOPs.

Catalytic Ozonation of Nitrobenzene by Manganese-Based Y Zeolites

Catalytic ozonation process (COP) is considered as a cost-efficient technology for the treatment of refractory chemical wastewaters. The catalyst performance plays an important role for the treatment efficiency. The present study investigated efficiencies and mechanisms of manganese (Mn)-based Y zeolites in COPs for removing nitrobenzene from water. The catalysts of Mn/NaY and Mn/USY were prepared by incipient wetness impregnation, while Mn-USY was obtained by hydrothermal synthesis. Mn-USY contained a greater ratio of Mn2+ than Mn/NaY, and Mn/USY. Mn oxides loaded on Y zeolites promoted the COP efficiencies. Mn/NaY increased total organic carbon removal in COP by 7.3% compared to NaY, while Mn/USY and Mn-USY increased 11.5 and 15.8%, respectively, relative to USY in COP. Multivalent Mn oxides (Mn2+, Mn3+, and Mn4+) were highly dispersed on the surface of NaY or USY, and function as catalytic active sites, increasing mineralization. Mn-USY showed the highest total organic carbon removal (44.3%) in COP among the three catalysts, because Mn-USY had a higher ratio of Mn2+ to the total Mn oxides on the surface than Mn/NaY and Mn/USY and the catalytic effects from intercorrelations between Mn oxides and mesoporous surface structures. The hydroxyl radicals and superoxide radicals governed oxidations in COP using Mn-USY. Nitrobenzene was oxidized to polyhydroxy phenol, polyhydroxy nitrophenol, and p-benzoquinone. The intermediates were then oxidized to small organic acids and ultimately carbon dioxide and water. This study demonstrates the potential of Y zeolites used in COP for the treatment of refractory chemical wastewaters.