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The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo- XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2 , NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. [ABSTRACT FROM AUTHOR]

In this paper, a multi-step framework to design flexible power-to-methane processes is presented. The approach considers all crucial parts of the power-to-methane process, including the renewable energy source. The aim is to provide a design that allows for feasible operation under the fluctuating conditions induced by the renewable energy source at all times. For each part of the process, the operating windows are analyzed and optimized under different site-specific constraints, and recommendations for actions regarding the technical implementations are provided. The impact of the carbon dioxide source is scrutinized, and subsequent scheduling optimization is performed to determine necessary storage capacities. The framework is demonstrated on two carbon dioxide availability scenarios where the renewable energy source is a wind turbine coupled to a polymer exchange membrane electrolyzer that produces hydrogen, which is then converted to methane in a fixed-bed reactor. The results show that the feasible operation of the system strongly depends on the site-specific conditions. In particular, the availability of carbon dioxide impacts the flexibility of the methanation reactor. When the stochiometric ratio between hydrogen and carbon dioxide cannot be sustained, the operating window of the reactor deteriorates significantly. However, it is shown that appropriate integration and scheduling of storage capacities debottlenecks the reactor and enables the utilization of all electricity generated by the wind turbine. • A framework for the design of flexible power-to-methane systems is introduced. • Optimization approaches for each step of the framework are provided. • A dynamic optimization approach for storage integration is given. • The impact of the carbon dioxide provision flexibility is investigated. • The framework is tested in a case study with an onshore wind turbine in Germany. [ABSTRACT FROM AUTHOR]