Fuels

The water flooding characteristic curve is a crucial tool in reservoir dynamic analysis, commonly employed to estimate water-driven geological reserves and recoverable reserves. However, due to approximations in theoretical derivations—such as equating average water saturation with outlet saturation or assuming that water cut approaches unity—most conventional curves achieve high accuracy only during the high water-cut stage (>80%). This study eliminates systematic errors and enhances calculation accuracy by establishing an improved water flooding curve equation. Firstly, a theoretical analysis of the error in a WOR (water–oil ratio)-type water flooding characteristic curve is performed. The results demonstrate that as water cut increases, calculated dynamic geological and recoverable reserves gradually rise, approaching actual values only when the water cut exceeds 90%. Secondly, a new type of water flooding characteristic curve is derived by using the Buckley–Leverett water drive oil theory and the Welge equation to modify the saturation approximation. Comparative analysis via reservoir numerical simulation demonstrates that the proposed curve significantly enhances prediction accuracy across all water-cut stages above 50%, outperforming conventional curves. After the water cut reaches 50%, the calculation error of dynamic geological reserves is less than 10%, and the calculation error of recoverable reserves is less than 5%. Field application shows that the new water flooding characteristic curve maintains a stable linear shape under certain development conditions. After the adjustment of development conditions, it jumps to form a new stable straight-line segment, which is conducive to the rapid and accurate evaluation of the adjustment effect.

Laboratory-based research on microbial fuel cells (MFCs) is often costly and limited to a small number of variables, making optimization challenging. However, machine learning (ML) offers a promising solution by enabling efficient multivariate principal component analysis (PCA) and multivariable optimization. These techniques can provide significant insights and optimization opportunities. The goal of this study is to propose an ML-based approach to explore the relationships between bioelectricity generation (in terms of voltage, power density (PD), current density (CD), and coulombic efficiency (CE)) and two key variables, chemical oxygen demand (COD) and pH, as well as to recommend their optimal combinations. Specifically, the objectives are to (1) integrate a laboratory-based MFC study with multivariate data analyses; (2) apply PCA to reduce data complexity by focusing on the principal components that account for the greatest variance, thus improving interpretability; and (3) identify the optimal combinations of COD and pH for maximizing bioelectricity generation. The PCA results demonstrated that COD positively influenced the generated voltage while having an inverse effect on CE. Additionally, both PD and CD increased with higher pH values. The optimal combination of COD and pH improved CD, PD, and CE; however, their optimal combination for generated voltage differed, with higher COD leading to higher voltage. The optimal predicted voltage, CD, PD, and CE of the study were 795.71 (mV), 1451.80 (mA/m²), 57.46 (mW/m²), and 4.85%, respectively. By integrating ML approaches, this study contributed to the optimization of bioelectricity generation from wastewater and offered valuable insights for researchers working in this field.

The aviation industry, responsible for approximately 2.5–3.5% of global greenhouse gas emissions, faces increasing pressure to adopt sustainable energy solutions. Hydrogen, with its high gravimetric energy density and zero carbon emissions during use, has emerged as a promising alternative fuel to support aviation decarbonization. However, its large-scale implementation remains hindered by cryogenic storage requirements, safety risks, infrastructure adaptation, and economic constraints. This study aims to identify and evaluate the primary technical and operational risks associated with hydrogen utilization in aviation through a comprehensive Monte Carlo Simulation-based risk assessment. The analysis specifically focuses on four key domains—hydrogen leakage, cryogenic storage, explosion hazards, and infrastructure challenges—while excluding economic and lifecycle aspects to maintain a technical scope only. A 10,000-iteration simulation was conducted to quantify the probability and impact of each risk factor. Results indicate that hydrogen leakage and explosion hazards represent the most critical risks, with mean risk scores exceeding 20 on a 25-point scale, whereas investment costs and technical expertise were ranked as comparatively low-level risks. Based on these findings, strategic mitigation measures—including real-time leak detection systems, composite cryotank technologies, and standardized safety protocols—are proposed to enhance system reliability and support the safe integration of hydrogen-powered aviation. This study contributes to a data-driven understanding of hydrogen-related risks and provides a technological roadmap for advancing carbon-neutral air transport.