Search Results (527)

In the Kelameili volcanic gas reservoir, primary hydraulic fracturing treatments in some wells take place on a limited scale, resulting in a rapid decline in production post stimulation and necessitating re-fracturing operations. However, prolonged production has led to a significant evolution in the in situ stress field, which complicates the design of re-fracturing parameters. To address this, this study adopts an integrated geology–engineering approach to develop a formation-specific geomechanical model, using rock mechanical test results and well-log inversion to reconstruct the reservoir’s initial stress field. The dynamic stress field simulations and re-fracturing parameter optimization were performed for Block Dixi-14. The results show that stress superposition effects induced by multiple fracturing stages and injection–production cycles have significantly altered the current in situ stress distribution. For Well K6, the optimized re-fracturing parameters comprised a pump rate of 12 m³/min, total fluid volume of 1200 m³, prepad fluid ratio of 50–60%, and proppant volume of 75 m³, and the daily gas production increased by 56% correspondingly, demonstrating the effectiveness of the optimized re-fracturing design. This study not only provides a more realistic simulation framework for fracturing volcanic rock gas reservoirs but also offers a scientific basis for fracture design optimization and enhanced gas recovery. The geology–engineering integrated methodology enables the accurate prediction and assessment of dynamic stress field evolution during fracturing, thereby guiding field operations. Full article

To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direct injection (GDI) engines using advanced machine learning methods. Three machine learning approaches, namely, support vector regression (SVR), deep neural network (DNN), and a Stacking integration model of SVR and DNN, were employed, respectively, to predict the soot mass concentration at the GPF inlet. The input data includes engine speed, torque, ignition timing, throttle valve opening angle, fuel injection pressure, and pulse width. Exhaust gas soot mass concentration at the three-way catalyst (TWC) outlet is obtained by an engine bench test. The results show that the correlation coefficients (R²) of SVR, DNN, and Stacking integration model of SVR and DNN are 0.937, 0.984, and 0.992, respectively, and the prediction ranges of soot mass concentration are 0–0.038 mg/s, 0–0.030 mg/s, and 0–0.07 mg/s, respectively. The distribution, median, and data density of prediction results obtained by the three machine learning approaches fit well with the test results. However, the prediction result of the SVR model is poor when the soot mass concentration exceeds 0.038 mg/s. The median of the prediction result obtained by the DNN model is closer to the test result, specifically for data points in the 25–75% range. However, there are a few negative prediction results in the test dataset due to overfitting. Integrating SVR and DNN models through stacked models extends the predictive range of a single SVR or DNN model while mitigating the overfitting of DNN models. The results of the study can serve as a reference for the development of accurate prediction algorithms to estimate soot loads in GPFs, which in turn can provide some basis for the control of the particulate mass and particle number (PN) emitted from GDI engines. Full article

Natural gas storage is an effective solution to address the energy supply–demand imbalance, and underground gas storage (UGS) is a primary method for storing natural gas. The overarching goal of this study is to monitor and analyze surface deformation at the Hutubi underground gas storage facility in Xinjiang, China, which is the largest gas storage facility in the country. This research aims to ensure the stable and efficient operation of the facility through long-term monitoring, using remote sensing data and advanced modeling techniques. The study employs the SBAS-InSAR method, leveraging Synthetic Aperture Radar (SAR) data from the TerraSAR and Sentinel-1 sensors to observe displacement time series from 2013 to 2024. The data is processed through wavelet transformation for denoising, followed by the application of a Gray Wolf Optimization (GWO) algorithm combined with Variational Mode Decomposition (VMD) to decompose both surface deformation and gas pressure data. The key focus is the development of a high-precision predictive model using a Gated Recurrent Unit (GRU) network, referred to as GWO-VMD-GRU, to accurately predict surface deformation. The results show periodic surface uplift and subsidence at the facility, with a notable net uplift. During the period from August 2013 to March 2015, the maximum uplift rate was 6 mm/year, while from January 2015 to December 2024, it increased to 12 mm/year. The surface deformation correlates with gas injection and extraction periods, indicating periodic variations. The accuracy of the InSAR-derived displacement data is validated through high-precision GNSS data. The GWO-VMD-GRU model demonstrates strong predictive performance with a coefficient of determination (R²) greater than 0.98 for the gas well test points. This study provides a valuable reference for the future safe operation and management of underground gas storage facilities, demonstrating significant contributions to both scientific understanding and practical applications in underground gas storage management. Full article

This work deals with the catalytic steam reforming of raw syngas to increase the efficiency of coupling gasification with downstream processes (such as fuel cells and catalytic chemical syntheses) by producing high-temperature, ready-to-use syngas without cooling it for cleaning and conditioning. Such a combination is considered a key point for the future exploitation of syngas produced by steam gasification of biogenic solid fuel. The design and construction of an integrated gasification and gas conditioning system were proposed approximately 20 years ago; however, they still require further in-depth study for practical applications. A 3D model of the freeboard of a pilot-scale, fluidized bed gasification plant equipped with catalytic ceramic candles was used to investigate the optimal operating conditions for in situ syngas upgrading. The global kinetic parameters for methane and tar reforming reactions were determined experimentally. A fluidized bed gasification reactor (~5 kWth) equipped with a 45 cm long segment of a fully commercial filter candle in its freeboard was used for a series of tests at different temperatures. Using a computational fluid dynamics (CFD) description, the relevant parameters for apparent kinetic equations were obtained in the frame of a first-order reaction model to describe the steam reforming of key tar species. As a further step, a CFD model of the freeboard of a 100 kWth gasification plant, equipped with six catalytic ceramic candles, was developed in ANSYS FLUENT^®. The composition of the syngas input into the gasifier freeboard was obtained from experimental results based on the pilot-scale plant. Simulations showed tar catalytic conversions of 80% for toluene and 41% for naphthalene, still insufficient compared to the threshold limits required for operating solid oxide fuel cells (SOFCs). An overly low freeboard temperature level was identified as the bottleneck for enhancing gas catalytic conversions, so further simulations were performed by injecting an auxiliary stream of O₂/steam (50/50 wt.%) through a series of nozzles at different heights. The best simulation results were obtained when the O₂/steam stream was fed entirely at the bottom of the freeboard, achieving temperatures high enough to achieve a tar content below the safe operating conditions for SOFCs, with minimal loss of hydrogen content or LHV in the fuel gas. Full article

Mesoporous silicon dioxide films have been shown to be well suited as adhesion-promoting interlayers for generating high-strength polymer–metal interfaces. These films can be fabricated via microwave plasma-enhanced chemical vapor deposition using the precursor hexamethyldisiloxane and oxygen as working gas. The resulting mesoporous structures enable polymer infiltration during overmolding, which leads to a nanoscale form-locking mechanism after solidification. This mechanism allows for efficient stress transfer across the interface and makes the resulting adhesion highly dependent on the morphology of the deposited film. To gain a deeper understanding of the underlying deposition mechanisms and improve process stability, this work investigates the growth behavior of mesoporous silica films using a multiple regression analysis approach. The seven process parameters coating time, distance, chamber pressure, substrate temperature, flow rate, plasma pulse duration, and pause-to-pulse ratio were systematically varied within a Design of Experiments framework. The resulting films were characterized by their free surface area, mean agglomerate diameter, and film thickness using digital image analysis, white light interferometry, and atomic force microscopy. The deposited films exhibit a wide range of morphological appearances, ranging from quasi-dense to dust-like structures. As part of this research, the free surface area varied from 15 to 55 percent, the mean agglomerate diameter from 17 to 126 nm, and the film thickness from 35 to 1600 nm. The derived growth model describes the deposition process with high statistical accuracy. Furthermore, all coatings were overmolded via injection molding and subjected to mechanical testing, allowing a direct correlation between film morphology and their performance as adhesion-promoting interlayers. Full article

The pore-throat structure of the extremely low-permeability sandstone reservoir in the Huachi area of the Ordos Basin is complex and highly heterogeneous. Currently, there are issues such as unclear understanding of the micro-pore-throat structural characteristics, primary controlling factors of reservoir quality, and classification boundaries of the reservoir in the study area, which seriously restricts the exploration and development effectiveness of the reservoir in this region. It is necessary to use a combination of various analytical techniques to comprehensively characterize the pore-throat structure and establish reservoir classification evaluation standards in order to better understand the reservoir. This study employs a suite of analytical and testing techniques, including cast thin sections (CTS), scanning electron microscopy (SEM), cathodoluminescence (CL), X-ray diffraction (XRD), as well as high-pressure mercury injection (HPMI) and constant-rate mercury injection (CRMI), and applies fractal theory for analysis. The research findings indicate that the extremely low-permeability sandstone reservoir of the Chang 3 section primarily consists of arkose and a minor amount of lithic arkose. The types of pore-throat are diverse, with intergranular pores, feldspar dissolution pores, and clay interstitial pores and microcracks being the most prevalent. The throat types are predominantly sheet-type, followed by pore shrinkage-type and tubular throats. The pore-throat network of low-permeability sandstone is primarily composed of nanopores (pore-throat radius r < 0.01 μm), micropores (0.01 < r < 0.1 μm), mesopores (0.1 < r < 1.0 μm), and macropores (r > 1.0 μm). The complexity of the reservoir pore-throat structure was quantitatively characterized by fractal theory. Nanopores do not exhibit ideal fractal characteristics. By splicing high-pressure mercury injection and constant-rate mercury injection at a pore-throat radius of 0.12 μm, a more detailed characterization of the full pore-throat size distribution can be achieved. The average fractal dimensions for micropores (Dh2), mesopores (Dc3), and macropores (Dc4) are 2.43, 2.75, and 2.95, respectively. This indicates that the larger the pore-throat size, the rougher the surface, and the more complex the structure. The degree of development and surface roughness of large pores significantly influence the heterogeneity and permeability of the reservoir in the study area. Dh2, Dc3, and Dc4 are primarily controlled by a combination of pore-throat structural parameters, sedimentary processes, and diagenetic processes. Underwater diversion channels and dissolution are key factors in the formation of effective storage space. Based on sedimentary processes, reservoir space types, pore-throat structural parameters, and the characteristics of mercury injection curves, the study area is divided into three categories. This classification provides a theoretical basis for predicting sweet spots in oil and gas exploration within the study area. Full article

In this study, a comprehensive experimental framework was developed to quantitatively characterize the pore structure of a deep coal-rock (DCR; reservoirs below [3000 m]) gas reservoir. Experimentally, petrological and mineral characteristics were determined by performing proximate analysis and scanning electron microscopy (SEM) as well as by measuring vitrinite reflectance and maceral components. Additionally, physisorption and high-pressure mercury injection (HPMI) tests were conducted to quantitatively characterize the nano- to micron-scale pores in the DCR gas reservoir at multiple scales. The DCR in the NQ Block is predominantly composed of vitrinite, accounting for approximately 77.75%, followed by inertinite. The pore space is predominantly characterized by cellular pores, but porosity development is relatively limited as most of such pores are extensively filled with clay minerals. The isothermal adsorption curves of CO₂ and N₂ in the NQ Block and the DJ Block exhibit very similar variation patterns. The pore types and morphologies of the DCR reservoir are relatively consistent, with a significant development of nanoscale pores in both blocks. Notably, micropore metrics per unit mass (pore volume (PV): 0.0242 cm³/g; and specific surface area (SSA): 77.7545 m²/g) indicate 50% lower gas adsorption potential in the DJ Block. In contrast, the PV and SSA of the mesopores per unit mass in the NQ Block are relatively consistent with those in the DJ and SF Blocks. Additionally, the peak mercury intake in the NQ Block occurs within the pore diameter < 20 nm, with nearly 60% of the mercury beginning to enter in large quantities only when the pore size exceeds 20 nm. This indicates that nanoscale pores are predominantly developed in the DCR of the NQ block, which aligns with the findings from physical adsorption experiments and SEM analyses. Overall, the development characteristics of multi-scale pores in the DCR formations of the NQ Block and the eastern part of the Basin are relatively similar, with both total PV and total SSA showing an L-shaped distribution. Due to the disparity in micropore SSA, however, the total SSA of the DJ Block is approximately twice that of the NQ Block. This discovery has established a robust foundation for the subsequent exploitation of natural gas resources in DCR formations within the NQ Block. Full article

The capillary force driving the water penetration process in the coal pore network is the key factor affecting the effect of coal seam water injection. The resistivity method can be used to determine the migration characteristics of water in coal. In order to study the relationship between the resistivity of gas-bearing coal and the migration of water in the process of imbibition, the self-generated imbibition tests of coal under different external water conditions were carried out by using the self-developed gas-bearing coal imbibition experimental platform and the dynamic response characteristics of coal resistivity with external water were obtained. The results show that the water injected into the coal body migrates from bottom to top under the driving of capillary force, and the resistivity of the wetted coal body shows a sudden decline, slow decline, and gradually stable stage change. Through the slice drying method, it is found that the moisture in the coal body is almost uniform after imbibition, and the resistivity method can be used to accurately and quantitatively characterize the moisture content of the coal body. In the axial direction, as water infiltrates layer by layer, the sudden change time of resistivity is delayed with the deepening of the layer. The resistivity of each layer first drops sharply then slows down and tends to stabilize. The stable value of resistivity increases gradually with the depth of the layer. In the radial direction, within the same plane, water first migrates to the centre of the coal body and then begins to spread outwards. The average mutation time and stable value of coal resistivity during spontaneous imbibition decrease with increasing water content. When the water content reaches 10%, the stable value of resistivity tends to be constant, and the relationship between the stable value of coal resistivity and water content conforms to an exponential function. Full article

Recently, marketing authorizations were granted by the Federal Drug Administration (FDA) for pegcetacoplan and avacincaptad pegol, which inhibit C3 and C5 complement components, respectively. These two drugs were demonstrated to slow down the growth of atrophic areas in the retina. These authorizations represent a huge breakthrough for patients suffering from geographic atrophy (GA), the late stage of the dry form of Age-related Macular Degeneration (AMD). Until then, no treatment was available to treat this blinding disease. However, these two new compounds inhibiting the complement system are still not available for patients outside of the United States, and they are not devoid of drawbacks, including a poor effect on vision improvement, an increased risk of occurrence of the neovascular form of AMD and the burden of patients receiving recurrent intravitreal injections. Thus, the important medical need posed by GA remains incompletely answered, and new therapeutic options with alternative modes of action are still required. Oxidative stress and inflammation are two major potential targets to limit the progression of atrophic retinal lesions. Dimethyl fumarate, dimethyl itaconate and other activators of the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) display antioxidants and immunomodulatory properties that have shown evidence of efficacy in in vitro and in vivo models of dry AMD. Tecfidera^®, whose active principle is dimethyl fumarate, is already commercialized for the treatment of autoimmune diseases such as multiple sclerosis and psoriasis. The aim of this review is to present the rationale and the design of the clinical trial we initiated to test the effectiveness and safety of repurposing Tecfidera^®, which could represent a new therapeutic alternative in patients with the dry form of AMD. Full article

Post-combustion technology is a new kind of low-nitrogen combustion technology. To achieve the combined removal of nitrogen oxides (NO_x) and sulfur dioxide (SO₂) emissions, the post-combustion technology combined with the sorbent injection in the furnace and post-combustion chamber is proposed. Experiments investigating the effects of the sorbent addition in a post-combustion chamber and post-combustion air arrangement on NO_x and SO₂ emissions were conducted in a 0.1 MWth circulating fluidized bed test platform. In addition, a comparative analysis of the NO_x and SO₂ emissions under both combined removal methods was also performed. The results indicated that adding sorbent to the post-combustion chamber can reduce SO₂ emissions, but further increasing the amount of sorbent will not significantly improve the desulfurization effect. The injection position of the post-combustion air will affect the emissions of NO_x and SO₂ in the flue gas. When the three-stage distribution of post-combustion air is adopted, the further back the third nozzle is distributed, the lower the temperature in the post-combustion chamber, which is beneficial to the control of NO_x and SO₂ emissions. Compared with the conventional combined removal method, the NO_x emissions were significantly reduced under the new combined removal method. Through secondary desulfurization in the furnace and post-combustion chamber, oxygen-deficient combustion in the furnace can achieve the combined removal of NO_x and SO₂. Full article

Food contamination by Salmonella poses a significant public health risk, rendering products unfit for consumption. This study aimed to evaluate the efficiency of ozone gas (O₃), applied in flow and at low pressures, in inactivating Salmonella on black peppercorns (Piper nigrum L.). Samples were inoculated with a cocktail of four Salmonella serotypes and subjected to ozonation under flow or low-pressure conditions in a hypobaric chamber. For the flow treatment, ozone gas at 16 mg L⁻¹ was humidified by passing it through a 40% (w/v) sodium chloride solution and applied for 2, 4, and 8 h. For the hypobaric chamber treatment, an inlet O₃ concentration of 60 mg L⁻¹ was used, with 10, 15, and 20 injections. The results showed that, under flow ozonation for 8 h, Salmonella was absent in 25 g of the sample. Ozone treatment increased pH, total titratable acidity (TTA), antioxidant activity (DPPH*), lightness (L*), color saturation (C*), total phenolic content (TPC), and the concentration of major essential oil compounds in all treatments. Under low-pressure ozonation, Salmonella persisted in all tested conditions, along with changes in color difference (∆E*), moisture content, TTA, DPPH*, L*, C*, pH, TPC, and the concentration of major essential oil compounds. The essential oil yield was not affected. Although the application of ozone at low pressures reduced Salmonella contamination, it was not sufficient for complete inactivation under the tested conditions. However, the flow-applied ozone treatment proved effective in the inactivation of Salmonella in black peppercorns. Full article

Proper determination of the bottomhole pressure in a gas lift well is essential to enhance production, tackle operating concerns, and use the least amount of gas. Mechanistic models, empirical correlation, and hybrid models are usually limited by the requirements for calibration, large amounts of inputs, or limited scope of work. Through this study, sixteen well-tested machine learning (ML) models, such as genetic programming-based symbolic regression and neural networks, are developed and studied to accurately predict flowing BHP at the perforation depth, using a dataset from 304 gas lift wells. The dataset covers a variety of parameters related to reservoirs, completions, and operations. After careful preprocessing and analysis of features, the models were prepared and tested with cross-validation, random sampling, and blind testing. Among all approaches, using the L-BFGS optimizer on the neural network gave the best predictions, with an R² of 0.97, low errors, and better accuracy than other ML methods. Upon using SHAP analysis, it was found that the injection point depth, tubing depth, and fluid flow rate are the main determining factors. Further using the model on 30 unseen additional wells confirmed its reliability and real-world utility. This study reveals that ML prediction for BHP is an effective alternative for traditional models and pressure gauges, as it is simpler, quicker, more accurate, and more economical. Full article

The effect of the pore–fracture structure on the porosity and permeability affects the production process of tight sandstone gas. In this paper, 12 groups of tight sandstone samples are selected as the object, and the pore–fracture volume of a tight reservoir is quantitatively characterized by a high-pressure mercury injection test. The multifractal and single fractal characteristics of different types of samples are calculated by fractal theory. On this basis, the pore volume variation under stress is discussed through the overlying pressure pore permeability test, and the pore–fracture compressibility is calculated. Finally, the main factors affecting the stress sensitivity of tight sandstone are summarized from the two aspects of the pore structure and mineral composition. The results are as follows. (1) The samples could be divided into types A and B by using the mercury-in and mercury-out curves. There is a significant hysteresis loop in the mercury inlet and outlet curves of type A, and the efficiency of the mercury inlet and outlet in the pores is relatively higher. The mercury removal curve of type B is almost parallel, and its mercury removal efficiency is relatively lower. (2) The applicability of singlet fractals in characterizing the heterogeneity of micropores is higher than that of multifractals. This is because the single fractal characteristics of the two types of samples have significant differences, while the differences in the multifractals are relatively weak. (3) A pore diameter of 100–1000 nm provides the main compression space for the type A samples. A pore distribution heterogeneity of 100–1000 nm affects the compression effect and stress sensitivity of this type B sample. Full article

This study aims to obtain findings on the internal water behavior, the presence of water channels, and the degree of washout due to rainfall infiltration in Japanese municipal solid waste (MSW) final disposal sites. Electrical resistivity tomography (ERT) monitoring and undistributed waste sampling for X-ray computed tomography (X-ray CT) analysis were conducted in the field. The study sites were targeted at Site A, which is mainly composed of non-combustible residues, and Site B, which is mainly composed of incineration ash. The time-dependent resistivity distributions obtained from real-time ERT monitoring were effective for us to understand the water content distribution after water infiltration during water injection tests. As a result, the global flow behavior and the local water channel flow were determined. In addition, X-ray CT analysis of the undisturbed waste samples obtained from the sites clarified the different pore structures, indicating the possibility of more advanced washing out at Site A than at Site B. Furthermore, the soil cover layer and gas extraction wells had a significant effect on the resistivity structure with respect to water flow behavior. Since soil cover layer and gas extraction wells are significant factors affecting waste stabilization by washout, it is suggested that these factors should be considered in the design and maintenance of landfills. Full article

Photovoltaic (PV) power generation is characterized by high fluctuation and intermittency. The accurate forecasting of PV power is crucial for optimizing grid operation and scheduling. Thus, a novel short-term PV power-forecasting method based on genetic algorithm-adaptive multi-objective differential evolution (GA-AMODE)-optimized bidirectional long short-term memory (BiLSTM) is proposed. Firstly, a data preprocessing method, including principal component analysis, a sliding window mechanism, and Gaussian noise injection, is designed to achieve dimension reduction and data robustness. Then, a GA-AMODE-BiLSTM model for PV power forecasting is proposed. GA and AMODE algorithms are integrated to balance global and local searching processes during the optimization of the BiLSTM network’s hyperparameters. Bi-LSTM is more suitable for complex time series tasks involving long-term dependencies and asymmetric relationships. The forecasting method is evaluated by typical indexes and is statistically tested. Comparative experiments using the same dataset across various models have been performed. The results show that the proposed GA-AMODE-BiLSTM model significantly outperforms other models in forecasting accuracy. Additionally, its superior stability and generalization is demonstrated, making the proposed method an effective tool for optimizing the management of renewable energy generation and enhancing the sustainability of energy systems. Full article