Search Results (9)

Methane source-rate inversion from remote-sensing plume imagery is essential for emissions monitoring, but its accuracy is often limited by uncertainty in ancillary wind information. This study examines how physical knowledge can be integrated into a deep-learning inversion model when the available wind input is imperfect. Using a controlled large-eddy-simulation (LES) benchmark designed for EnMAP/PRISMA-style imaging-spectrometer methane quantification, we compare six models that span image-only regression, flexible wind conditioning, simplified hard integrated-mass-enhancement (IME) coupling, and soft physics-guided learning under clean inputs, deterministic wind bias, stochastic Gaussian wind noise, and source-rate-stratified tests. Under clean benchmark conditions, flexible wind conditioning provides the best scalar accuracy, with FiLM reaching a mean absolute percentage error (MAPE) of 6.19% and a root mean squared error (RMSE) of 1323.36, followed closely by Concat (MAPE 6.37%, RMSE 1325.69). The simplified hard-coupling model is sensitive to wind perturbations: DIN-hard rises from MAPE 8.44% under clean inputs to 31.39% and 26.89% under deterministic wind-bias multipliers α = 0.7 and α = 1.3, respectively, and becomes unstable under stronger Gaussian wind noise in the tested protocol. By contrast, DIN-soft-v2 remains competitive under clean conditions (MAPE 6.39%, RMSE 1360.94), follows smoother degradation under biased or noisy wind, and improves plume spatial diagnostics relative to DIN-soft (center-of-mass shift 3.92 versus 4.07 pixels; plume alignment degree 2.60 versus 2.72 degrees). The calibrated IME-style physical baseline reaches a clean MAPE 24.45%, indicating that the learning-based models substantially outperform this benchmark physical proxy. Within this LES-based benchmark and the tested wind-perturbation protocols, the results suggest that IME-inspired physical knowledge is more robustly incorporated as a calibratable soft prior than as the simplified hard log-additive forward coupling considered here; however, transfer to real satellite scenes still requires validation. Full article

Steam methane reforming (SMR) powered by induction heating offers a promising route for low CO₂-emission hydrogen production, but predictive modelling remains challenging because the available experimental data are limited and heterogeneous. This study proposes a hybrid Bayesian neural network (H-BNN) to predict the mass of hydrogen (MoH) from literature-derived SMR data using operating variables including temperature, flow rate, power input, time-on-stream, and interval duration. Feedforward neural network (FNN) and classical Bayesian neural network (BNN) models were also developed as benchmarks, and all three architectures were evaluated with ReLU, Tanh, and GELU activation functions. To address data scarcity, only the training split was augmented at scales of $k=2$, 5, and 10, while the validation and test sets were kept unchanged. The H-BNN combines deterministic feature extraction with Bayesian uncertainty-aware prediction, enabling a balance between accuracy and uncertainty representation. Across the validation-selected models, test performance reached R² ∼ 0.9894 to 0.9969, with mean absolute errors of 0.0126 g to 0.0217 g. The strongest advantage appeared at k = 2, where the H-BNN outperformed the benchmark models. Overall, the proposed H-BNN is a promising framework for hydrogen prediction under data-scarce conditions, although its predictive intervals remain informative rather than fully calibrated. Full article

The study investigates the influence of atmospheric pressure fluctuations on methane emissions in a decommissioned coal mine in Poland (SRK S.A., KWK “Krupiński”). Continuous measurements of methane concentrations and atmospheric pressure were analyzed to identify periods of dynamic pressure drops, which were then correlated with recorded methane levels. Strong linear relationships were observed, with correlation coefficients ranging from 0.88 to 0.97 and determination coefficients exceeding 0.85, indicating that pressure changes are a primary factor influencing methane release. Individual regression models for each identified case showed the lowest mean absolute errors compared to generalized models, highlighting the impact of atypical cases on predictive performance. Key findings align with previous studies, confirming that both the magnitude and the gradient of pressure decline directly affect the rate and scale of methane release and that threshold effects may limit further concentration increases despite continued pressure drops. The results suggest the potential to develop a predictive model linking atmospheric pressure variations to methane emissions, which could support forecasting of methane capture in decommissioned mines or ventilation methane levels in active mines. Understanding these mechanisms is crucial for both occupational safety and for effective methane emission reduction strategies in the mining sector. Full article

The top–down emission rate retrieval algorithm (TERRA) method for calculating the net flux out of a box has been employed by other researchers to assess large sources of methane release. This usually requires a manned aircraft drone with powerful performance to fly over the boundary layer. Few studies have focused on low-altitude box sampling mass balance methods for small sources of methane release, such as at maximum flight altitudes of less than 100 m. The accuracy and sources of uncertainty in such a method still need to be determined as they differ from the conditions of large sources. Nineteen flights were conducted to detect methane emissions from Chinese oil field well sites using a measurement system consisting of a quadcopter and methane, wind speed, wind direction, air pressure, and temperature sensors. The accuracy and uncertainty of the method are discussed. The average absolute relative error of the measurement is 18.5%, with an average uncertainty of 55.75%. The uncertainty is mainly caused by the wind speed and direction, and the background CH₄ concentration. The main paths to reduce uncertainty and improve accuracy for low-altitude box sampling include subtracting the background concentration during flux retrieval, enhancing the accuracy of methane measurements, selecting a period of downwind dominant or wind direction change of less than 30 degrees, and ensuring a maximum flight height greater than 50 m with a horizontal distance from the pollution source center of less than 75 m. The results show that TERRA-based low-altitude box sampling is suitable for quantifying methane release rates from small sources. Full article

Homogeneous charge compression ignition is considered a promising solution to face the increasing regulations imposed by the legislator in the transport sector, thanks to pollutant and CO₂ emissions reduction. In this work, a quasi-dimensional multi-zone HCCI model integrated with 1D commercial software is developed and validated. It is based on the control mass Lagrangian approach and computes the mixture chemistry evolution through offline tabulation of chemical kinetics (tabulated kinetic of ignition). Thus, the simulation can predict mixture auto-ignition with reduced computational effort and high accuracy. Multi-zone schematization mimics the typical thermal stratification of HCCI engines, controlling the combustion evolution. The model is coupled to sub-models for pollutant emissions estimation. Initially, the tabulated chemistry approach is validated against a chemical kinetics solver applied to a constant-volume homogeneous reactor, considering various fuel blends. The model is then used to simulate the operations of four engines using different fuels (hydrogen, methane, n-heptane, and n-heptane/toluene/ethanol blend), under various boundary conditions. The model predictivity is demonstrated against pressure traces, heat release rate, and noxious emissions. The numerical results showed to adequately agree with measured counterparts (average relative error of 1.3% on in-cylinder pressure peak, average absolute error of 0.95 CAD on pressure peak angle, average relative error of 8.4% on uHCs emissions, absolute error below 1 ppm on NOx emissions) only adapting the thermal stratification to the engines under study. The methodology proved to be a reliable tool to investigate the operation of an HCCI engine, applicable in the development of new engine architecture. Full article

India being the world’s second largest cultivator of paddy, it is very important that the extent of the resulting methane emissions is estimated, and steps are taken to minimize these emissions. Peninsular India is a prime rice-producing region; however, no significant information is available on the contribution of this region to methane emissions, nor are there available studies that show the effect of cultivars, growth seasons, soil characteristics, etc., on methane emissions. As one of the attempts to cover this knowledge gap, emissions of methane from paddy fields, situated in four villages of Puducherry, India, involving three rice cultivars, three soil types and two growth seasons have been studied. All the fields had a continuously flooded pattern of irrigation with water supplied at a rate of 11,500–20,000 m³/ha. Whereas the cultivars ADT 39 and ADT 45 generated the highest methane flux during their reproductive phase, with lesser emission during the vegetative phase and much less during maturity, CO 45 exhibited copious methane emissions during the vegetative phase, with several orders of magnitude lesser emission during the reproductive and the maturity phases. These trends were independent of the location of the field and soil type, though the absolute and the relative values of the emissions varied from location to location. Irrespective of the cultivar, the quantities of methane emission increased linearly with soil temperature across the day but decreased exponentially as soil pH increased beyond 7. Full article

This article presents a methodology for predicting the absolute methane emission rate for longwall caving extraction based on the determination of destressing zones generated by longwall mining operations, by means of numerical modelling. This methodology was applied for the conditions of the K-2 longwall panel in the KWK Pniówek mine. The finite difference method code FLAC2D was employed as an element of the methodology to determine the destressing zones. All results including the numerical modelling results, empirical results and the measured (in situ) results were gathered in the comparative analysis. As the final results, the accuracy and reliability of the proposed methodology were evaluated. Full article

In recent years, industries such as oil and gas production, waste management, and renewable natural gas/biogas have made a concerted effort to limit and offset anthropogenic sources of methane emissions. However, the state of emissions, what is emitting and at what rate, is highly variable and depends strongly on the micro-scale emissions that have large impacts on the macro-scale aggregates. Bottom-up emissions estimates are better verified using additional independent facility-level measurements, which has led to industry-wide efforts such as the Oil and Gas Methane Partnership (OGMP) push for more accurate measurements. Robust measurement techniques are needed to accurately quantify and mitigate these greenhouse gas emissions. Deployed on both fixed-wing and multi-rotor unmanned aerial vehicles (UAVs), a miniature tunable diode laser absorption spectroscopy (TDLAS) sensor has accurately quantified methane emissions from oil and gas assets all over the world since 2017. To compare bottom-up and top-down measurements, it is essential that both values are accompanied with a defensible estimate of measurement uncertainty. In this study, uncertainty has been determined through controlled release experiments as well as statistically using real field data. Two independent deployment methods for quantifying methane emissions utilizing the in situ TDLAS sensor are introduced: fixed-wing and multi-rotor. The fixed-wing, long-endurance UAV method accurately measured emissions with an absolute percentage difference between emitted and mass flux measurement of less than 16% and an average error of 6%, confirming its suitability for offshore applications. For the quadcopter rotary drone surveys, two flight patterns were performed: perimeter polygons and downwind flux planes. Flying perimeter polygons resulted in an absolute error less than 36% difference and average error of 16.2%, and downwind flux planes less than 32% absolute difference and average difference of 24.8% when flying downwind flux planes. This work demonstrates the applicability of ultra-sensitive miniature spectrometers for industrial methane emission quantification at facility level with many potential applications. Full article

A gas well blowout in south central Texas in November 2019 that lasted for 20 days provided a unique opportunity to test the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model’s plume dispersion against hydrocarbon air monitoring data at two nearby state monitoring stations. We estimated daily blowout hydrocarbon emission rates from satellite measurement-based results on methane emissions in conjunction with previously reported composition data of the local hydrocarbon resource. Using highly elevated hydrocarbon mixing ratios observed during several days at the two downwind monitoring stations, we calculated excess abundances above expected local background mixing ratios. Subsequent comparisons to HYSPLIT plume dispersion model outputs, generated using High-Resolution Rapid Refresh (HRRR) or North American Mesoscale (NAM) forecast meteorological input data, showed that the model generally reproduces both the timing and magnitude of the plume in various meteorological conditions. Absolute hydrocarbon mixing ratios could typically be reproduced within a factor of two. However, when lower emission rate estimates provided by the company in charge of the well were used, downwind hydrocarbon observations could not be reproduced. Overall, our results suggest that HYSPLIT, in combination with high-resolution meteorological input data, is a useful tool to accurately forecast chemical plume dispersion and potential human exposure in disaster situations. Full article