Numerical modeling of mass transfer processes coupling with reaction for the design of the ozone oxidation treatment of wastewater

Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study

In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring a flotation cell with two non-concentric ultraviolet lamps. A total of 438 datapoints were collected from published works and distributed into 70% training and 30% test datasets while cross-validation was utilized to assess the training reliability. The models incorporated 15 input variables concerning reaction kinetics, molecular properties, hydrodynamic information, presence of radiation, and catalytic properties. It was observed that the Support Vector Regression (SVR) presented a poor performance as the ε hyperparameter ignored large error over low concentration levels. Meanwhile, the Artificial Neural Networks (ANN) model was able to provide rough estimations on the expected degradation of the pollutants without requiring information regarding reaction rate constants. The multi-objective optimization analysis suggested a leading role due to ozone kinetic for a rapid degradation of the contaminants and most of the results required intensification with hydrogen peroxide and Fenton process. Although both models were affected by accuracy limitations, this work provided a lightweight model to evaluate different Advanced Oxidation Processes (AOPs) by providing general information regarding the process operational conditions as well as know molecular and catalytic properties.

Interaction Mechanisms and Application of Ozone Micro/Nanobubbles and Nanoparticles: A Review and Perspective

Ozone micro/nanobubbles with catalytic processes are widely used in the treatment of refractory organic wastewater. Micro/nanobubble technology overcomes the limitations of ozone mass transfer and ozone utilization in the application of ozone oxidation, and effectively improves the oxidation efficiency of ozone. The presence of micro/nanobubbles keeps the catalyst particles in a dynamic discrete state, which effectively increases the contact frequency between the catalyst and refractory organic matter and greatly improves the mineralization efficiency of refractory organic matter. This paper expounds on the characteristics and advantages of micro/nanobubble technology and summarizes the synergistic mechanism of microbubble nanoparticles and the mechanism of catalyst ozone micro/nanobubble systems in the treatment of refractory organics. An interaction mechanism of nanoparticles and ozone microbubbles is suggested, and the proposed theories on ozone microbubble systems are discussed with suggestions for future studies on systems of nanoparticles and ozone microbubbles.

Investigation of carbon dioxide photoreduction process in a laboratory-scale photoreactor by computational fluid dynamic and reaction kinetic modeling

The production of solar fuels via the photoreduction of carbon dioxide to methane by titanium oxide is a promising process to control greenhouse gas emissions and provide alternative renewable fuels. Although several reaction mechanisms have been proposed, the detailed steps are still ambiguous, and the limiting factors are not well defined. To improve our understanding of the mechanisms of carbon dioxide photoreduction, a multi-physics model was developed using COMSOL. The novelty of this work is the computational fluid dynamic model combined with the novel carbon dioxide photoreduction intrinsic reaction kinetic model, which was built based on three-steps, namely gas adsorption, surface reactions and desorption, while the ultraviolet light intensity distribution was simulated by the Gaussian distribution model and Beer-Lambert model. The carbon dioxide photoreduction process conducted in a laboratory-scale reactor under different carbon dioxide and water moisture partial pressures was then modeled based on the intrinsic kinetic model. It was found that the simulation results for methane, carbon monoxide and hydrogen yield match the experiments in the concentration range of 10−4 mol·m−3 at the low carbon dioxide and water moisture partial pressure. Finally, the factors of adsorption site concentration, adsorption equilibrium constant, ultraviolet light intensity and temperature were evaluated.