Abstract

Valve and control faults in building-level radiator systems degrade the efficiency of district heating networks and cause occupant discomfort. Although methods for fault detection and compensation exist independently, integrated systems that diagnose and react to specific faults are a recognized research gap. This study presents an integrated fault detection and compensation framework implemented and evaluated within the EnergyPlus simulation environment. The system uses a machine-learning-informed proxy to trigger fault-specific supervisory control actions. A comparative analysis of control-based (stuck-open) versus physically constrained (stuck-closed) faults through an ablation study revealed fundamentally different operational behaviors. For the stuck-open fault, the fault detection and compensation system was highly effective, reducing the discomfort degree-hours by 68% and the energy consumption by 23%. For the stuck-closed fault, the system’s inability to restore comfort confirmed its role as a high-fidelity diagnostic tool, proving that such physical faults require maintenance rather than supervisory control. These findings establish a framework for fault-specific responses in district heating-connected buildings.

Abstract

Ensuring energy security under extreme weather conditions is a major challenge faced by modern urban power grids. Flood disasters cause large-scale power outages by submerging distribution equipment, seriously endangering the reliability and resilience of power supply systems. To improve the disaster prevention capability of distribution networks and fortify the defense line for energy security, this paper proposes an intelligent diagnosis framework integrating physical mechanisms and graph learning. First, this study established a high-precision flood fault-power flow reconfiguration model to quantify the failure risk of key equipment such as ring main units (RMUs) under different flood depths. Furthermore, a two-layer random walk network was innovatively constructed: by simulating the adaptive walk of virtual particles between layers, the topological connectivity and electrical vulnerability of nodes under disaster conditions are captured simultaneously. Finally, the Skip-gram model was adopted to map distribution network nodes into low-dimensional embedding vectors, thereby realizing unsupervised and efficient intelligent diagnosis of node vulnerability. Case studies show that this method can accurately identify the key vulnerable nodes that are most likely to trigger systemic collapse during floods. The research results can provide precise decision support for preventive reinforcement, emergency control, and rapid recovery of distribution systems under extreme weather, and serve as a key intelligent technology for building a more resilient and safer future energy system.

Abstract

Aiming at the problems of untimely monitoring, difficult quantitative assessment, and inaccurate prediction of oil and water well casings, this study develops two models: a casing Health Index (HI) calculation model and an HI time-series prediction model. First, the main influencing factors of casing damage were screened and identified. Then, a casing HI model based on the weighted fusion of Principal Component Analysis (PCA) and Mean Decrease Impurity (MDI) importance values was constructed. Furthermore, research on the time-series prediction model of oil and water well casing HI based on Long Short-Term Memory (LSTM) network was conducted. Using 87 production intervals (18 damaged) from 13 casings in an oilfield of PetroChina, 14 key parameters were identified to calculate and predict HI. Results show: the HI model realizes quantitative health risk evaluation; the LSTM model captures the temporal trend of HI with high accuracy. This study realizes real-time, quantitative, and accurate assessment and prediction of the service operation health status of oil and water well casings, and provides good guidance for the early warning of oil and water well casing damage.

Abstract

Achieving global net-zero targets requires urgent reductions in CO₂ emissions, underscoring the importance of renewable, carbon-neutral fuels such as biogas and biomethane. The growing demand for these fuels has intensified interest in solidified biomethane (Bio-SNG) systems as safe, compact, and energy-dense storage pathways. Conventional hydrate-based storage methods, however, often require stringent operating conditions or complex crystallizer setups, limiting their large-scale applicability. In this work, we introduce a novel concept of utilizing amino acid-modified ice cubes as a simple and low-cost medium for biogas/biomethane storage. The ice cubes, prepared with small amounts of selected amino acids, serve as pre-formed solid matrices that can rapidly encapsulate gas under mild pressure-temperature conditions. Kinetic investigations revealed that amino acid-modified ice cubes significantly reduced hydrate induction times and accelerated gas uptake compared to pure ice and bulk amino acid solution. Morphological analysis showed the development of porous, snowy cloud structures on the ice surface, which facilitated gas interaction and promoted hydrate growth. Together, these effects enabled rapid storage kinetics and high energy density, with storage capacities comparable to advanced hydrate pellet technologies. This work demonstrates amino acid-modified ice cubes as a simple and scalable route for solidified biomethane storage, opening pathways toward cost-effective and sustainable solidified biomethane technologies for renewable energy applications.

Abstract

This study develops a semi-analytical model for multiphase productivity prediction in fractured horizontal wells, integrating equivalent fracture elements with a dynamic drainage radius. By incorporating fracture orientation, aperture, density, and stress sensitivity, the model accurately characterizes flow across hydraulic fractures, natural fractures, and the matrix. Built on material balance principles, the three-phase coupled flow model explicitly accounts for complex fracture geometries and stress-sensitive permeability. Validation against field data demonstrates high predictive accuracy and practical engineering utility. Sensitivity analysis reveals that hydraulic fracture half-length, natural fracture orientation, and stress sensitivity dominate ultimate recovery, while aperture and density primarily influence early-phase production. This work provides an efficient theoretical framework for production optimization in complex fractured reservoirs.

Abstract

Accurately predicting the trend of mean room temperature under known operational states of heating systems is of great significance for achieving low-carbon and energy-efficient regulation of heating systems. Existing studies have not clearly identified the most suitable method for predicting building mean room temperature. Therefore, the effectiveness of various machine learning algorithms, mathematical regression algorithms, and recently emerging bio-inspired optimization algorithms in predicting the mean indoor temperature of buildings was compared and analyzed. Data were collected from an office building, and the input variables of the prediction model were selected based on physical mechanisms. The main conclusion is that XGBoost demonstrates the highest prediction accuracy among the 22 algorithms, with a mean absolute percentage error of 0.22%.

Abstract

The mixed displacement of CO₂ and associated gas (EOR) in oil fields is an important path for the green transformation of the upstream petroleum industry, but the actual characteristics of the coupled flow of impurity CO₂ – crude oil in porous media are still unclear. This article uses online low field nuclear magnetic resonance (LF-NMR) technology to systematically explore the flow characteristics of impurities CO₂ and crude oil in porous media by real-time monitoring of transverse relaxation time (T2) and one-dimensional (1D) signals, and analyze the flow differences of pores with different sizes and positions. The results indicate that impurity gases accelerate gas breakthrough and breakthrough through a dual mechanism of reducing miscibility and enhancing pressure gradient, resulting in a 3-9% decrease in recovery rate; The flow of CO₂ – crude oil has significant stages: in the initial stage, the physical driving force preferentially uses large and medium-sized pores, and impurity gases can enhance this process; The mid-term gas expands the utilization of small pores through diffusion extraction miscibility effects; After breakthrough, the use of small and medium-sized pores is maintained through the mixing extraction oil carrying effect of the surrounding pores, but this effect will be weakened by impurity gases in the middle and later stages. Injecting gas for sweeping presents spatial heterogeneity: the front section has a more balanced sweeping effect due to the resistance effect, and the utilization effect is significantly better than that of the middle and rear sections; Due to finger penetration and gas channeling, a large number of unused medium and large pores remain in the middle and rear parts. This article provides an important reference for a deeper understanding of the flow mechanism of CO₂ flooding with impurities.

Abstract

To support urban energy planning and data-driven assessment, this paper proposes a comprehensive framework for robust trend analysis and seasonal meteorological attribution of multi-stations, long-term, monthly global solar radiation (GSR) data, elucidating the seasonal heterogeneity. Firstly, we conduct anomaly detection to ensure data robustness. Subsequently, long-term trends are analysed using the Mann-Kendall test and Sen’s slope estimator, while the same calendar?month variability is quantified by the Coefficient of Variation (CoV). The CoV results indicate lower variability at the urban GSR station compared to the coastal GSR station, with both stations displaying similar seasonal patterns-higher variability from November to May and reduced variability from June to October. After that, we apply Generalised Additive Model (GAM) P-splines to model the Pooled Anomaly (PA) and Seasonaldisaggregation and evaluate the performance of the two models using leave-one-year-out (LOYO) cross?validation. The PA model attains a Q2-LOYO value of 0.72, whereas the Season model performs best in spring (Q2-LOYO= 0.83), with relatively lower performance observed during summer and autumn compared to the PA model. The attribute results indicate cloud cover and temperature are both key factors: Cloud cover exerts a significantly negative influence on GSR, with the strongest inhibitory effect observed during winter, whereas temperature demonstrates the highest positive sensitivity in autumn. The impacts of these variables in spring and summer are comparable in magnitude. Relative humidity is unstable in summer, making its independent effect hard to identify, while a significant negative trend in other seasons. Rainfall consistently exhibits a negative association across all seasons, with the most substantial suppressive effect observed in winter. Air pressure and wind speed generally serve as marginal contributors to GSR variability.

Abstract

The competitive adsorption between shale oil and CO₂ in water-containing kerogen is of great significance for CO₂ enhanced oil recovery (CO2-EOR) and CO₂ sequestration. In this study, molecular dynamics (MD) method was used to construct dry kerogen model, and grand canonical Monte Carlo (GCMC) method was used to construct shale reservoir kerogen model with different water contents. By combining the GCMC and MD methods, the competitive adsorption behavior of shale oil and CO₂ in water-containing kerogen was simulated. The adsorption characteristics and kinetic phenomena of different adsorbates were discussed, and the feasibility of CO₂ sequestration was evaluated. The results show that as the water content increases, the CO₂ adsorption capacity decreases, while the isosteric heat of adsorption remains initially lower and then rises. In multi-component systems, as the water content increases, the adsorption capacity of shale oil weakens, and the displacement effect of CO₂ on shale oil improves. The adsorption capacity of long-chain alkanes in shale oil is relatively weak, and the competitive adsorption between short-chain alkanes and CO₂ becomes more obvious. Importantly, regulated by water content, the adsorption selectivity of CO₂ is conducive to its sequestration under the condition of appropriate water content. Meanwhile, water-alternating-gas (WAG) injection can be considered as an effective method for EOR. This study quantitatively reveals the competitive adsorption characteristics of shale oil and CO₂ in water-containing kerogen, and offers insights at the microscopic scale for the efficient and green development of shale oil reservoirs.

Abstract

Compressor surge as a typical fault in gas turbines, poses a serious threat to stability and reliability. Traditional surge control strategies typically rely on bleed valves to extend the stability margin. However, this approach comes at the expense of overall engine performance, leading to significant efficiency and power losses. Modern advanced gas turbines often adopt variable-geometry designs, such as inlet guide vanes, to provide additional flexibility for performance and stability management. This paper presents an integrated surge modeling framework for a variable-geometry compressor, combining a physics-based surge model with a component-level gas turbine model. The surge model is integrated with a component-level model, thereby enabling accurate representation of system behavior under both stable and unstable conditions. The results indicate that properly selected guide vane angles have a pronounced impact on the surge margin and effectively postpone the onset of surge, thus creating a wider window for implementing effective control actions. This work provides a physics-based modeling foundation for real-time gas turbine surge monitoring and lays the groundwork for developing active control strategies that balance stability enhancement with performance preservation in advanced variable-geometry gas turbines, while also offering practical insights into variable-geometry operation for improved efficiency in future energy conversion systems.