The increasing concentration of atmospheric CO2 since the Industrial Revolution has driven research into subsurface storage as a viable solution. This study focuses on developing machine learning models to estimate net present value and carbon footprint in a combined gas production and CO2 sequestration scenario in shales. The dataset comprised a large set of numerical simulation scenarios, which were run using PSU SHALECOMP, a 3-dimensional, compositional and multiphase simulator, which incorporates an equation of state to capture the effects of pressure and temperature variations. A horizontal production/injection well with multiple hydraulic fractures was modeled using the stimulated reservoir volume approach which represents the volume impacted by hydraulic fractures as well as the induced fractures through the natural fracture network in the reservoir. The results of these scenarios were used to calculate net present value and carbon footprint associated with each scenario. Exploratory data analysis and feature engineering revealed that the net present value is primarily governed by stimulated reservoir volume’s fracture permeability, original gas in place within the stimulated reservoir volume, and injection constraints, whereas the carbon footprint is predominantly controlled by total production duration and injected CO2 volume. Machine learning models were trained to build robust forecasting tools for net present value and carbon footprint. These models revealed that the selected neural network model outperformed multiple linear regression and random forests models in predicting both net present value and carbon footprint, with R2 values of 0.99 and 0.96, respectively, for the testing sets. To further refine these estimates and improve the robustness of predictions, future research should focus on improving the certainty in deterministic and probabilistic estimations of net present value and carbon footprint by gathering more comprehensive data, and conducting detailed analyses of carbon emissions and operational costs. This research represents a significant step toward understanding the economic and environmental implications of CO2 sequestration in shale reservoirs, contributing valuable insights for future developments in this field.
Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs. Missing features must then be reconstructed, and their errors can propagate through the model. If this input uncertainty is not reflected, prediction intervals may become miscalibrated, affecting demand-response scheduling. Our work examines where uncertainty should be placed once inference inputs are reconstructed. We develop a unified one-day-ahead probabilistic forecasting framework that aligns temporal resolution, reconstructs the unavailable inputs, and derives causal features, and we compare a modular post-hoc residual-quantile scheme with an integrated in-model quantile-learning scheme. The comparison uses three mid-scale Deep Learning (DL) backbones: recurrent, hybrid recurrent, and attention-based Temporal Fusion Transformer (TFT) models, under identical inputs, forecasting horizon, preprocessing rules, and training budgets. Results show that uncertainty placement is backbone-dependent. Integrated quantile learning is most reliable with the TFT, yielding 2.2–3.6% MAPE and 28–83 W RMSE on the labeled test window, while producing intervals about 5 × narrower than the modular intervals at the closest-to-nominal coverage level. Diebold-Mariano tests support the TFT ranking and the mixed behavior of the recurrent backbones. A reconstruction-sensitivity test shows that reconstructed inputs increase the Quantile Score (QS) by 106% while interval width remains nearly unchanged, indicating that the model does not automatically absorb reconstruction-induced uncertainty. Robustness checks against non-DL baselines and seasonal hold-out weeks support this ranking. Our results expose the limits of post-hoc residual quantiles when inference depends on reconstructed inputs.
We report the first successful synthesis of a novel magnetically recoverable nanocatalyst, Fe3O4@Al2O3/Co-Cs, incorporating cesium (Cs) as a co-catalytic promoter for efficient and sustainable hydrogen generation via NaBH4 hydrolysis. In this system, magnetite (Fe3O4) serves as a magnetic core, enabling facile separation and recyclability, whereas the Al2O3 shell enhances thermal stability, structural integrity, and active-site dispersion. Cobalt (Co) acts as the primary active metal, whereas Cs is introduced here for the first time in NaBH4 hydrolysis as an electronic promoter, significantly enhancing electron transfer and accelerating the hydrogen generation rate (HGR). Comprehensive characterization using FTIR, XRD, FE-SEM/EDX, and BET analyses confirmed the successful formation of a hierarchical core-shell structure. FE-SEM images revealed a flower-like morphology composed of aggregated nanosheets and nanoparticles that promoted efficient mass transfer and gas diffusion. Catalytic performance tests conducted at 30 degrees C demonstrated a high hydrogen generation rate of 17.24 L g- 1 min- 1. The apparent activation energy was determined to be 27.18 kJ mol-1, indicating favorable reaction kinetics under mild conditions. Thermodynamic analysis revealed a low enthalpy of adsorption (Delta Hads = 35.66 +/- 0.01 kJ mol-1) and a small entropy change (Delta S degrees = 0.09022 +/- 0.01 kJ mol- 1 K-1), suggesting favorable interactions between the reactants and catalyst surface. Density functional theory (DFT) calculations further confirmed that the Fe3O4@Al2O3/Co-Cs system exhibited enhanced adsorption strength and reduced activation barriers, facilitating NaBH4 hydrolysis through efficient charge transfer, intermediate stabilization, and synergistic catalytic effects. Overall, this study highlights the novel role of cesium as an alkali metal promoter in NaBH4 hydrolysis, opening new opportunities for the design of advanced non-noble metal catalysts for hydrogen generation.
In this study, the gamma and X-ray radiation attenuation properties of B4C-TiB2 reference ceramics and those reinforced with 1, 2, and 3 vol% graphene nanoplatelets (GNP) were systematically investigated. The composites were fabricated via Spark Plasma Sintering (SPS), enabling rapid densification and controlled microstructural evolution. B4C is known for its strong thermal neutron absorption due to its high 10B content. In addition, it contributes to gamma radiation attenuation through photon absorption and scattering interactions. The incorporation of TiB2 and GNPs into B4C enhances the composite density thereby increasing the photon interaction probability and improving the overall gamma shielding performance. Radiation attenuation measurements of the composites were conducted using 0.356 MeV (133Ba), 0.662 MeV (137Cs), and 1.173 MeV (60Co) sources, and diagnostic X-ray sources (50-110 kVp). Gamma and X-ray attenuation coefficients were independently calculated through MCNP6.2 Monte Carlo simulations to validate the experimental results. The close agreement between simulated and measured values demonstrates the reliability of the computational methodology and its consistency with established theoretical models of photon interaction cross sections. This consistency further supports the applicability of the approach for accurate evaluation and comparative analysis of photon attenuation performance in composite materials. In this study, key photon interaction parameters including the half value layer (HVL), tenth value layer (TVL), mean free path (MFP), effective atomic number (Zeff), and effective electron density (Neff) were computed. The results indicate that these parameters are significantly dependent on both the photon energy and the chemical composition of the examined composites. Both MCNP and experimental results show that the B4C-TiB2-GNP composites have linear attenuation coefficients (μ) ranging from 0.1468 to 0.2498 cm−1, with the sample with 1 vol% GNP (BT1G) exhibits the highest performance, reaching 0.2498 cm−1 for 133Ba. The results reveal that among all investigated samples, the BT1G composite exhibits the highest shielding efficiency in radiation conditions. GNP-reinforced B4C-TiB2 composites, may serve as a potential alternative to reduce the use of lead in gamma radiation protection.
The calibration of RANS turbulence models is essential for improving predictive accuracy. However, existing approaches are often limited to a single flow scenario that lacks generalizability, or they overfit closure coefficients to a selected set of flow configurations. This study introduces the Reinforced Holistic Calibration (RHC) framework that extends existing calibration strategies through an iterative reinforcement mechanism. RHC identifies cases with the largest prediction errors and recalibrates the RANS model within a multi-case loop, thereby systematically improving model generalizability while mitigating overfitting.The RHC framework was demonstrated by calibrating the k-ω Shear Stress Transport (SST) turbulence model for flows over wall-mounted rectangular prisms at various freestream velocities. Time-resolved particle image velocimetry (TR-PIV) measurements were carried out in a dedicated wind-tunnel campaign, where LEGO-based models enabled systematic and robust variations in prism geometry and spacing. The experimental campaign provided detailed turbulent flow fields for the calibration and validation phases.The optimized closure coefficients showed systematic changes: dissipation-related terms (β1, β2, β∗) were modified to adjust modeled dissipation of k and ω, while the production and stress limiter coefficients (γ1, γ2, a1, b1) were tuned to suppress excessive ω production and limit turbulent viscosity in separation regions. The calibration resulted in elevated turbulent kinetic energy levels within separation and wake zones, elongated reattachment lengths, and intensified after-body interactions in double-block configurations.The RHC framework delivers generalizable calibration, significantly enhancing the k-ω SST model’s predictive fidelity for flow over wall-mounted prisms. Its iterative procedure offers a cost-effective calibration for turbulence models.
