It is well known that urbanization and industrialization have resulted in the rapidly increasing emissions of volatile organic compounds (VOCs), which are a major contributor to the formation of secondary pollutants (e.g., tropospheric ozone, PAN (peroxyacetyl nitrate), and secondary organic aerosols) and photochemical smog. The emission of these pollutants has led to a large decline in air quality in numerous regions around the world, which has ultimately led to concerns regarding their impact on human health and general well-being. Catalytic oxidation is regarded as one of the most promising strategies for VOC removal from industrial waste streams. This Review systematically documents the progresses and developments made in the understanding and design of heterogeneous catalysts for VOC oxidation over the past two decades. It addresses in detail how catalytic performance is often drastically affected by the pollutant sources and reaction conditions. It also highlights the primary routes for catalyst deactivation and discusses protocols for their subsequent reactivation. Kinetic models and proposed oxidation mechanisms for representative VOCs are also provided. Typical catalytic reactors and oxidizers for industrial VOC destruction are further discussed. We believe that this Review will provide a great foundation and reference point for future design and development in this field.

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Recent Advances in the Catalytic Oxidation of Volatile Organic Compounds: A Review Based on Pollutant Sorts and Sources.

Nonradical-based advanced oxidation processes for pollutant removal have attracted much attention due to their inherent advantages. Herein we report that magnesium oxides (MgO) in CuOMgO/Fe3O4 not only enhanced the catalytic properties but also switched the free radical peroxymonosulfate (PMS)-activated process into the 1O2 based nonradical process. CuOMgO/Fe3O4 catalyst exhibited consistent performance in a wide pH range from 5.0 to 10.0, and the degradation kinetics were not inhibited by the common free radical scavengers, anions, or natural organic matter. Quantitative structure-activity relationships (QSARs) revealed the relationship between the degradation rate constant of 14 substituted phenols and their conventional descriptor variables (i.e., Hammett constants σ, σ-, σ+), half-wave oxidation potential (E1/2), and pKa values. QSARs together with the kinetic isotopic effect (KIE) recognized the electron transfer as the dominant oxidation process. Characterizations and DFT calculation indicated that the incorporated MgO alters the copper sites to highly oxidized metal centers, offering a more suitable platform for PMS to generate metastable copper intermediates. These highly oxidized metals centers of copper played the key role in producing O2•- after accepting an electron from another PMS molecule, and finally 1O2 as sole reactive species was generated from the direct oxidation of O2•- through thermodynamically feasible reactions.

/pdf/tuning-of-persulfate-activation-from-a-free-radical-to-a-3lm36sr5f1.pdf

Tuning of Persulfate Activation from a Free Radical to a Nonradical Pathway through the Incorporation of Non-Redox Magnesium Oxide

As a promising approach for carbon dioxide capture, chemical looping combustion has been extensively investigated for more than two decades. However, the chemical looping strategy can be and has been extended well beyond carbon capture. In fact, significant impacts on emission reduction, energy conservation, and value-creation can be anticipated from chemical looping beyond combustion (CLBC). This article aims to demonstrate the versatility and transformational benefits of CLBC. Specifically, we focus on the use of oxygen carriers or redox catalysts for chemical production – a $4 trillion industry that consumes 40.9 quadrillion BTU of energy. Compared to state-of-the-art chemical production technologies, we illustrate that chemical looping offers significant opportunities for process intensification and exergy loss minimization. In many cases, an order of magnitude reduction in energy consumption and CO2 emission can be realized without the needs for carbon dioxide capture. In addition to providing various CLBC examples, this article elaborates on generalized design principles for CLBC, potential benefits and pitfalls, as well as redox catalyst selection, design, optimization, and redox reaction mechanism.

/pdf/chemical-looping-beyond-combustion-a-perspective-2ohy8fd521.pdf

Chemical looping beyond combustion – a perspective

The catalytic oxidation of toluene to CO2 and H2O over nanoflower spinel CoMn2O4 synthesized by the oxalic acid sol–gel method has been investigated, and it demonstrates lower activation energy (35...

Revealing the Highly Catalytic Performance of Spinel CoMn2O4 for Toluene Oxidation: Involvement and Replenishment of Oxygen Species Using In Situ Designed-TP Techniques

The development of efficient photocatalysts for the degradation of organic pollutants and production of hydrogen peroxide (H2 O2 ) is an attractive two-in-one strategy to address environmental remediation concerns and chemical resource demands. Graphitic carbon nitride (g-C3 N4 ) possesses unique electronic and optical properties. However, bulk g-C3 N4 suffers from inefficient sunlight absorption and low carrier mobility. Once exfoliated, ultrathin nanosheets of g-C3 N4 attain much intriguing photocatalytic activity. Herein, a mussel-inspired strategy is developed to yield silver-decorated ultrathin g-C3 N4 nanosheets (Ag@U-g-C3 N4 -NS). The optimum Ag@U-g-C3 N4 -NS photocatalyst exhibits enhanced electrochemical properties and excellent performance for the degradation of organic pollutants. Due to the photoformed valence band holes and selective two-electron reduction of O2 by the conduction band electrons, it also renders an efficient, economic, and green route to light-driven H2 O2 production with an initial rate of 0.75 × 10-6 m min-1 . The improved photocatalytic performance is primarily attributed to the large specific surface area of the U-g-C3 N4 -NS layer, the surface plasmon resonance effect induced by Ag nanoparticles, and the cooperative electronic capture properties between Ag and U-g-C3 N4 -NS. Consequently, this unique photocatalyst possesses the extended absorption region, which effectively suppresses the recombination of electron-hole pairs and facilitates the transfer of electrons to participate in photocatalytic reactions.

Crafting Mussel-Inspired Metal Nanoparticle-Decorated Ultrathin Graphitic Carbon Nitride for the Degradation of Chemical Pollutants and Production of Chemical Resources

Noble metal nanoparticles (noble metal NPs, noble metal = Ag, Pt and Pd) have been successfully immobilized on CoAl-layered double hydroxide (CoAl-LDHs) layers using a simple and facile self-redox process to afford noble metal NPs immobilized on CoAl-LDHs (N–CoAl-LDHs, N = Ag, Pt and Pd). In addition, the CoAl-LDHs and N-CoAl-LDHs were calcined in air at 600 °C to afford the CoAlO and N-CoAlO catalysts for toluene combustion. The catalytic and characterization results indicated that the Pd–CoAlO catalyst exhibited the highest catalytic activity because it possessed the highest low-temperature reducibility and the most abundant surface Co 3+ and surface adsorbed oxygen species. In addition, the noble metal NP sizes of the N–CoAlO catalysts affected the catalytic activity for toluene combustion, and the existence of catalytically active PdO contributes to the excellent catalytic activity of the Pd–CoAlO catalyst for toluene combustion. The comparison experiments reveal that the PdO phase and CoAlO support play significant role in the toluene combustion and they participate cooperatively in the reaction process. In addition, the in situ DRIFTS results indicated that the benzoate species are the main intermediate species in toluene combustion, which should be further oxidized by O 2 to the final products ( i.e ., CO 2 and H 2 O).

Pd–Co based spinel oxides derived from pd nanoparticles immobilized on layered double hydroxides for toluene combustion

Data-space inversion (DSI) methods provide posterior (history-matched) predictions for quantities of interest, along with uncertainty quantification, without constructing posterior models. Rather, predictions are generated directly from a large set of prior model simulations and observed data. In this work, we develop a data-space inversion with variable control (DSIVC) procedure that enables forecasting with user-specified well controls in the post-history-match prediction period. In DSIVC, flow simulations on all prior realizations, with randomly sampled well controls, are first performed. User-specified controls are treated as additional observations to be matched in posterior predictions. Posterior data samples are generated using a randomized maximum likelihood procedure with a gradient-based optimizer. For prescribed post-history-match well control settings, posterior predictions can be generated in seconds or minutes. Results are presented for a channelized system, and posterior predictions from DSIVC are compared with those from the standard DSI method. Standard DSI requires prior models to be re-simulated using the specified controls, while DSIVC requires only one set of prior simulations. Substantial uncertainty reduction is achieved through data-space inversion, and reasonable agreement between DSIVC and DSI results is generally observed. DSIVC is then applied for data assimilation combined with production optimization under uncertainty, as well as for closed-loop reservoir management, which entails a sequence of data assimilation and optimization steps. Clear improvement in the objective function is attained in these examples.

A data-space inversion procedure for well control optimization and closed-loop reservoir management

/pdf/use-of-multifidelity-training-data-and-transfer-learning-for-1ys4gp73.pdf

Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models

Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from usual model-based techniques in that they provide only posterior predictions for quantities (time series) of interest, not posterior models with calibrated parameters. DSI methods require a large number (O(500–1000)) of flow simulations to first be performed on prior geological realizations. Given observed data, posterior predictions for time series of interest, such as well injection or production rates, can then be generated directly. DSI operates in a Bayesian setting and provides posterior samples of the data vector. In this work, we develop and evaluate a new approach for data parameterization in DSI. Parameterization is useful in DSI as it reduces the number of variables to determine in the inversion, and it maintains the physical character of the data variables. The new parameterization uses a recurrent autoencoder (RAE) for dimension reduction, and a long short-term memory (LSTM) recurrent neural network architecture to represent flow-rate time series. The RAE-based parameterization is combined with an ensemble smoother with multiple data assimilation (ESMDA) for posterior data sample generation. Results are presented for two- and three-phase flows in a 2D channelized system and a 3D multi-Gaussian model. The new DSI RAE procedure, along with several existing DSI treatments, is assessed through detailed comparison to reference rejection sampling (RS) results. The new DSI methodology is shown to consistently outperform existing approaches, in terms of statistical (P10–P90 interval and Mahalanobis distance) agreement with RS results. The method is also shown to accurately capture derived quantities which are computed from variables considered directly in DSI. This requires correlation and covariance between variables to be properly captured, and accuracy in these relationships is demonstrated. The RAE-based parameterization developed here is clearly useful in DSI, and it may also find application in other subsurface flow problems.

Data-space inversion using a recurrent autoencoder for time-series parameterization

Fast forecasting of reservoir pressure distribution in geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we propose to use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

/pdf/deep-learning-accelerated-3d-carbon-storage-reservoir-2db2zqsi.pdf

Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR

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