CO2 methanation over heterogeneous catalysts: recent progress and future prospects

Bimetallic catalysts have attracted extensive attention for a wide range of applications in energy production and environmental remediation due to their tunable chemical/physical properties. These properties are mainly governed by a number of parameters such as compositions of the bimetallic systems, their preparation method, and their morphostructure. In this regard, numerous efforts have been made to develop “designer” bimetallic catalysts with specific nanostructures and surface properties as a result of recent advances in the area of materials chemistry. The present review highlights a detailed overview of the development of nickel-based bimetallic catalysts for energy and environmental applications. Starting from a materials science perspective in order to obtain controlled morphologies and surface properties, with a focus on the fundamental understanding of these bimetallic systems to make a correlation with their catalytic behaviors, a detailed account is provided on the utilization of these systems in the catalytic reactions related to energy production and environmental remediation. We include the entire library of nickel-based bimetallic catalysts for both chemical and electrochemical processes such as catalytic reforming, dehydrogenation, hydrogenation, electrocatalysis and many other reactions.

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Ni-based bimetallic heterogeneous catalysts for energy and environmental applications

CO2 methanation is a well-known reaction that is of interest as a capture and storage (CCS) process and as a renewable energy storage system based on a power-to-gas conversion process by substitute or synthetic natural gas (SNG) production. Integrating water electrolysis and CO2 methanation is a highly effective way to store energy produced by renewables sources. The conversion of electricity into methane takes place via two steps: hydrogen is produced by electrolysis and converted to methane by CO2 methanation. The effectiveness and efficiency of power-to-gas plants strongly depend on the CO2 methanation process. For this reason, research on CO2 methanation has intensified over the last 10 years. The rise of active, selective, and stable catalysts is the core of the CO2 methanation process. Novel, heterogeneous catalysts have been tested and tuned such that the CO2 methanation process increases their productivity. The present work aims to give a critical overview of CO2 methanation catalyst production and research carried out in the last 50 years. The fundamentals of reaction mechanism, catalyst deactivation, and catalyst promoters, as well as a discussion of current and future developments in CO2 methanation, are also included.

Supported Catalysts for CO2 Methanation: A Review

Power-to-Methane is a concept that converts electrical into chemical energy using CO2 and H2O. The concept brings the possibility of connecting the power grid to different sectors, where CH4 is needed such as mobility and industry. In this review, a comprehensive overview of the state-of-the-art of Power-to-Methane is presented. The Power-to-Methane process chain is described in detail. Fundamentals of water electrolysis are highlighted and cell technologies are discussed and assessed. CO2 sources are pointed out, CO2 separation technologies are depicted and compared, and some separation projects worldwide are listed. Thermodynamics of methanation process is analyzed; catalysts and reactors used are descripted and evaluated. Finally, Power-to-Methane plants in operation and construction are addressed.

Power-to-Methane: A state-of-the-art review

Catalytic carbon dioxide hydrogenation to methane: A review of recent studies

Structural characteristics of the northern Okinawa Trough and adjacent areas from regional seismic reflection data: Geologic and tectonic implications

The co-methanation of carbon dioxide containing syngas was carried out on Al2O3 supported NixFe1−x (x is 0.1, 0.3, 0.5, 0.7 and 0.9) catalysts for synthetic natural gas (SNG) production. The catalysts were prepared by wet-impregnation method taking 20 weight percent of the metallic component over the support, and its characteristics were analyzed by BET surface area, XRD and H2-TPR. The maximum carbon conversion and CH4 selectivity are achieved on Ni0.7Fe0.3/Al2O3 catalyst. Further, increase of Fe content led to enhancing the water gas shift reaction and hydrocarbon formation.

Co-methanation of CO and CO2 on the NiX-Fe1−X/Al2O3 catalysts; effect of Fe contents

The present invention is devised to solve the problem in which a state cannot be kept track of because an outgoing traffic and an incoming traffic pass through different firewalls on a Multiple Entry/Exit Point (MEP) network having a plurality of entry points. In the present invention, firewalls physically remote from each other can share connection information using a modified SYN cookie, so that stateful inspection firewalls physically remote from each other can be used even on the MEP network.

Method of sharing state between stateful inspection firewalls on mep network

In South Korea, where there are no resources such as natural gas or crude oil, research on alternative fuels has been actively conducted since the 1990s. The research on synthetic oil is subdivided into Coal to Liquid (CTL), Gas to Liquid (GTL), Biomass to Liquid (BTL), etc., and was developed with the focus on catalysts, their preparation, reactor types, and operation technologies according to the product to be obtained. In Fischer–Tropsch synthesis for synthetic oil from syngas, stability, CO conversion rate, and product selectivity of catalysts depends on the design of their components, such as their active material, promoter, and support. Most of the developed catalysts were Fe- and Co-based catalysts and were developed in spherical and cylindrical shapes according to the reactor type. Recently, hybrid catalysts in combination with cracking catalysts were developed to control the distribution of the product. In this review, we survey recent studies related to the design of catalysts for production of light hydrocarbons and middle distillates, including hybrid catalysts, encapsulated core–shell catalysts, catalysts with active materials with well-organized sizes and shapes, and catalysts with shape- and size-controlled supports. Finally, we introduce recent research and development (R&D) trends in the production of light hydrocarbons and middle distillates and in the catalytic processes being applied to the development of catalysts in Korea.

Catalytic Technologies for CO Hydrogenation for the Production of Light Hydrocarbons and Middle Distillates

Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.

https://www.mdpi.com/2075-4418/11/6/943/pdf

Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database

Jin-Ho Kim

Papers

Structural characteristics of the northern Okinawa Trough and adjacent areas from regional seismic reflection data: Geologic and tectonic implications

Co-methanation of CO and CO2 on the NiX-Fe1−X/Al2O3 catalysts; effect of Fe contents

Method of sharing state between stateful inspection firewalls on mep network

Catalytic Technologies for CO Hydrogenation for the Production of Light Hydrocarbons and Middle Distillates

Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database