Energy Science & Engineering最新文献

This study presents a strategy for preventing and controlling rockbursts in deep and vast seams during the mining operation. We analyzed the main factors influencing rockburst by combining the geological structure of the rock mass with field-collected microseismic monitoring data. Determined the disturbance strain sensitive threshold through mechanical experiments on coal's antidisturbance characteristics, the indoor pull-out test of the supporting bolt was carried out. Studied the rockburst support technology and the antirockburst capability of the support system based on the energy method. The key governing variables of regional impact risk in the big buried deep coal seam operating face include high tectonic stress, goaf effect, fault, and fault protection coal pillar. The uniaxial compressive strength of the coal sample in the 3308 working face is 15.11 MPa, the antidisturbance strength is 10 MPa. When the extreme stress is 86.03%, the specimen fails; the ultimate failure strength under disturbance is 13 MPa. The support system's antishock ability is analyzed based on its energy absorption, under the premise of ensuring the support quality and support strength, the antiscour ability of 3308 Tailentry support meets the anti-impact requirements. It is believed that the system offers adequate lateral confining pressure for the internal creep disturbance-sensitive area, enhancing the area's resistance to disturbance. This is crucial in preventing and managing rockburst incidents.

Huff-n-puff (HnF) is a crucial technology for effectively enhancing the oil recovery (EOR) of tight oil reservoirs. Soaking period is the primary platform for injection medium interacting with formation fluid and reservoir rock in HnF. Elucidating the micro-percolation mechanism of the soaking period is immensely significant for guiding oilfield production practices. The present study established a physical simulation method combining HnF experiments with nuclear magnetic resonance to reveal the microscopic percolation mechanisms, including water, fracturing fluid, and surfactant. Furthermore, the impacts of soaking time, HnF cycles, wettability, and pore structure on oil recovery degree were quantified. The results demonstrate the crucial significance of wettability and pore structure in the soaking period. The dominant mechanism during water HnF in reservoirs characterized by well-connected pore networks and minimal clay pores is micropore imbibition, while conversely, macropore displacement plays a predominant role. The oil recovery degree of fracturing fluid HnF primarily relies on mitigating solid-fluid forces within macropores. The surfactant HnF in preferential water- and oil-wet reservoirs is primarily governed by oil films stripped from macropore walls and micropore imbibition, respectively. Specifically, water and fracturing fluid HnF are suitable for shorter soaking time and fewer HnF cycles, whereas the surfactant HnF exhibits an inverse relationship.

Anaerobic digestion of abundant feedstock from biomaterials is a good innovative fossil fuel alternative approach for the synthesis of green fuel (biogas). Rotatable central composite design (CCD) and machine learning (ML) via Python coding were successfully used to design, optimize, and predict the rate of biogas production from stew-rice and eggs digestate with Udara seeds in an anaerobic unit. Two-input parameters, such as inoculation ratio (S/I) and hydraulic reaction time (HRT) were considered, resulting in 13 experimental setups under mesophilic surroundings of 25–34°C. Mixture ratios of substrate/inoculum (S/I) of 0.98:1, 1.5:1, 2.75:1, 2.75:1, 4:1, 1.5: 1, and 4.52:1 were used against 30, 20, 44.14, 15.86, 40, 40, and 30 days HRT as modeled by CCD rotatable to optimize biogas production from crushed Udara seeds with spoilt stew-rice and eggs digestate. From the results, it was observed that the coefficient of determination (R²) of 0.9573 was generated via CCD rotatable whereas, the R² of 1 was generated from the multivariate regression of ML approach. Also, the data and graphs derived via ML were superior to the ones derived from CCD rotatable. However, the maximum output of 4.84 L at 4 mixing ratio and 40 days HRT from CCD rotatable is close to the ML value of 4.89 L under the same input factors, yet ML yielded more. Thus, it is clear that the Python-based ML algorithm approach has the potential to predict biogas output better than CCD rotatable. However, the Gas Chromatography Mass Spectrometry analysis of the highest output produced generated 63.29% biomethane and 26.71% CO₂ by volume and produced a flashpoint of −167°C which is flammable. Thus, the generated biogas via an anaerobic unit can be transmitted into large-scale commercial applications for the betterment of mankind.

Hydrogen, as an energy carrier, is attractive to many stakeholders based on the assumption that the extensive global network of natural gas infrastructure can be repurposed to transport hydrogen as part of a zero-carbon energy future. Therefore, utility companies and governments are rapidly advancing efforts to pilot blending low-carbon hydrogen into existing natural gas systems, many with the goal of eventually shifting to pure hydrogen. However, hydrogen has fundamentally different physical and chemical properties to natural gas, with major consequences for safety, energy supply, climate, and cost. We evaluate the suitability of using existing natural gas infrastructure for distribution of hydrogen. We summarize differences between hydrogen and natural gas, assess the latest science and engineering of each component of the natural gas value chain for hydrogen distribution, and discuss proposed solutions for building an effective hydrogen value chain. We find that every value chain component is challenged by reuse. Hydrogen blending can circumvent many challenges but offers only a small reduction in greenhouse gas emissions due to hydrogen's low volumetric energy density. Furthermore, a transition to pure hydrogen is not possible without significant retrofits and replacements. Even if technical and economic barriers are overcome, serious safety and environmental risks remain.

An improved method of clutch coordinated control based on the Kalman filter was proposed to solve the problem that the existing mode switching strategy of hybrid electric vehicles could not adapt to engine temperature changes and clutch wear. First, taking advantage of the relationship between the torque transmitted by the clutch and the starting resistance of the engine, combined with the characteristics of the clutch, the clutch wear was roughly calculated. Accordingly, the control strategy of the clutch in the existing mode switching was improved to adapt to the clutch wear. The adaptive control strategy proposed for clutch wear included the fuzzy control module of the initial engagement pressure, the fuzzy inference module of the clutch engaging pressure change, the clutch wear estimation module and so on. Second, the Kalman filter was used to process the results to improve the estimation accuracy of clutch wear. The engine starting resistance related to starting speed and temperature was modeled to enhance the adaptability of the control strategy to engine temperature. Finally, the designed control strategy was verified in simulation. The results show that the improved control strategy can complete the mode switching when the engine temperature is variable and the clutch is worn. The maximum impact degree increased from 5 m/s³ without wear to 8.5 m/s³ with wear, but it is still less than the index limit, and it can be considered that the proposed strategy can achieve the desired control effect. The fuzzy control algorithm proposed enhances the vehicle's ride comfort during mode switching from pure electric driving to hybrid driving.

The effect of particle packing height (10, 30, 50, 70 mm) on the yield and composition of oil shale pyrolysis products is investigated. The results show that the oil yield could decrease 1.0% and the noncondensable gases yield could increase 0.5% as the oil shale packing height increased from 10 to 70 mm. The main hydrocarbon gases are C1–C6 gases, and the increase of packing bed height could decrease the relative content ratio of alkanes to alkenes in hydrocarbon gases. The primary components of the derived oil are aliphatic compounds, aromatic compounds, and compounds containing heteroatoms in the carbon atoms range of C6–C28. And the hydrogen type of shale oil is mainly composed of methylene groups (about 70%) with longer alkyl chains, and CH₃ in aromatics, cycloalkanes, and alkanes. The chemical composition and hydrogen type have certain regularity changes with the changing of packing height, attributing to the effect of temperature gradients between surface and center of packing bed, and the diffusing time of products through the packing bed.

The removal and discharge of coal powders from coalbed methane wellbore is the key to maintaining continuous production. For this reason, based on the submerged jet theory, the force and migration starting conditions of coal powder particles deposited at the bottom of the coalbed methane well are analyzed by analyzing the cohesiveness of coal powders, and a model for the starting and migration of coal powders at the bottom of the well under submerged jet conditions is established; Considering the adhesion between coal dust, the adhesion coefficient is used to characterize the adhesion between particles, a mechanical model of coal dust particles deposited at the bottom of the well is established, and the corresponding calculation formula for the outlet flow rate of the coal dust cleaning nozzle is derived. The fluid mechanics parameters of the bottom of the well coal dust cleaning under different conditions are obtained, and the numerical simulation method is used to simulate the whole process of jet flushing of the bottom of the well coal dust deposited, and the effect of the bottom of the well jet flushing of coal dust deposits is analyzed, providing a theoretical basis for the bottom of the well coal dust flushing process.

To study the flame propagating characteristic and its coupling effect with other parameters in the LPG pipeline, a typical pipeline model with two equal-length branches perpendicular to each other is designed for experiment and simulation. Then, gas explosion scenarios are experimentally tested and numerically simulated, which is followed by the analysis of flame shape changing with time and peak temperature changing with space. Results show that when passing through the bifurcation, flame propagates to vertical branch B in a sharp knife shape affected by the strong vortex, reflected airflow, and compressed pressure wave in the pipeline with a diameter of 0.125 m. At the monitoring point that is 0.4 m away from the bifurcation point, the peak temperature of the vertical branch B is 57.87% bigger than that of the horizontal branch C, and its arrival time is 80% longer than that of the horizontal branch C, due to the existence of flame in vertical branch B. What's more, in both branches, the coupling results between peak temperature and peak velocity agree very well with the growth function, while the coupling results between peak temperature and peak pressure agree well with the decay function, providing aids to the optimal layout design of industrial pipeline branches as well as to the explosion suppression measures.

In this study, the Structural Units-Bonding Matrix will be introduced into the crude oil characteristics analysis, specifically engineered to depict the molecular intricacies of petroleum substances. This methodology synergizes the bonding matrix with structural units, assigning the bonding matrix to the foundational layer, while the structural units adorn the surface layer. Through meticulous analysis of crude oil data, this study identified the core and branched molecular constituents of crude oil, facilitating the digital articulation of these molecules. Leveraging this foundational work, this study developed composition models for five quintessential types of crude oil, enabling the precise prognostication of their macroscopic physical properties. Further, by evaluating the viscosity-temperature characteristics of these selected crude oils, this study established a predictive model for the viscosity–temperature relationship of crude oils, grounded in the quantitative structure–property relationship method. This approach not only augments this study understanding of crude oil molecular composition but also enhances the predictive accuracy of their physical properties, heralding a significant advancement in the field of petroleum molecular science.