Abstract Horizontal wells with multistage fracture treatments enabled economic gas production from shale gas reservoirs. Recovery from a typical shale reservoir is usually quite low as numerous factors can affect the ultimate recovery. However, recovery factors inside and outside the stimulated reservoir volume (SRV), especially the variations spatially and temporally, are not yet fully understood as the numerical simulation of shale gas production is still challenging. In this study, a dual-porosity, dual-permeability model was built to investigate ultimate gas recovery and its spatial variations throughout the production period. Parametric studies were conducted to evaluate the effect of matrix permeability, fracture conductivity, fracture half length, and operating conditions on recovery factors. Systematic and comprehensive numerical experiments were carried out to generate a probability distribution of ultimate recovery factors. The results presented in this article provide insight into the impact that key parameters have on these recovery factors in and outside the SRV and the range of recovery factors one can expect from shale gas reservoirs. Fracture conductivity has the most impact on recovery in the SRV while matrix permeability can affect the recovery outside the SRV significantly. The expected recovery in the SRV is 24% in shale reservoirs. The methodology in this study provides a foundation to develop more reliable models in accurately forecasting ultimate gas recoveries spatially for shale gas reservoirs. The new understanding can be applied to optimize field development, well spacing, and infill drilling to increase economic recovery.

Abstract The solid oxide electrolysis cell (SOEC) is recognized as a promising method for hydrogen production, attributed to its high efficiency. Steam is split into hydrogen and oxygen by electrolysis at high temperatures. Electrolysis is inherently an endothermic process; however, it can be transformed into an exothermic process depending on the operating voltage. During the start-up process, the heat reaction is observed to change from endothermic to exothermic around a thermoneutral voltage. In this study, a dynamic model of the solid oxide electrolyzer system was developed, and the behavior of the system during the start-up process was analyzed. A dynamic model of the stack was developed to investigate the behavior of cell temperature and current density. Furthermore, 1D models of heat exchangers and 0D models of blowers were developed and verified against experimental results. These components were systematically organized and simulated. The temperatures of the stack and components during a schedule-based start-up process were investigated. Additionally, the behavior during the load change process, shifting from an endothermic reaction to an exothermic reaction, was examined. It was found that to reach an operating condition above the thermoneutral voltage, additional heat is required for the stack due to its endothermic reaction. The effect of air on the stack was also found to be dependent on the operating voltage of the stack.

Abstract The reduction of NOx emissions is a paramount endeavor in contemporary engineering and energy production, as these emissions are closely linked to adverse environmental and health impacts. The prediction of NOx emission from gas turbines through several integrated data-driven machine learning methods has been evaluated in study. The study compares the performance of ensemble and conventional machine learning models, demonstrating superior accuracy achieved by the ensemble models. Specifically, the Random Forest model achieved an accuracy rate of 91.68%, XGBoost yielded an accuracy of 91.54%, and CATBoost exhibited the highest accuracy at 92.76%. These findings highlight the capability of data-driven machine learning techniques in enhancing NOx emission predictions in gas turbines. The improved prediction by ensembles can be utilized in the development and implementation of more effective control and mitigation strategies in practical applications. Through the application of these advanced machine learning approaches, the gas turbine industry can play a pivotal role in minimizing its environmental impact while optimizing operational efficiency. This study also provides valuable insights into the effectiveness of ensemble machine learning models, advancing our understanding of their capabilities in addressing the critical issue of NOx emissions from gas turbines.

Abstract Urban heating and cooling demands account for a significant portion of global energy consumption. It is crucial to explore new energy resources and innovative solutions to meet environmental targets. Urban infrastructures like data centers, water networks, and tunnels move and collect energy, in some cases residual energy, that is valuable because of its proximity to consumers. The novelty of this work lies in the exploration of utilizing the accumulated residual heat that is not used in subway air to improve the energy efficiency of urban areas. The work focuses on the potential use of this residual heat by the installation of heat pumps in subway platforms or tunnels. By installing a heat pump on subway platforms or tunnels, this residual heat can be efficiently utilized for heating demand or producing domestic hot water. A general methodology for assessing the performance of such air source heat pumps in the subway context is presented. The calculation of the coefficients of performance (COP) and seasonal coefficients of performance (SCOP) calculation is outlined, with a detailed example illustrating the methodology. The paper emphasizes a comparative analysis between the proposed system and a traditional heat pump using ambient air, highlighting the advantages of the proposed approach. Finally, the paper concludes with a brief analysis of the different effects of refrigerants on the system performance for the same boundary conditions. Results are promising, increasing the energy efficiency of big cities and reducing CO2 emissions.

Abstract The flamelet model is a commonly used tool for turbulent combustion simulations in the engineering field due to its computational efficiency and compatibility with complex chemical reaction mechanisms. Despite being widely used for decades, the flamelet model still faces challenges when applied to complex flame configurations, such as partially premixed flames, inhomogeneous inlets, supersonic combustion, or multiphase combustion. The principal challenges are posed by the uncertainty of the presumed shapes for probability density functions (PDFs) of the flamelet tabulation variables and the coupled process of turbulent diffusion and chemical reaction in turbulent combustion. Recent progress is reviewed from the viewpoint of the reaction manifold, with connections made to other combustion models, as well as the determination of joint (or conditional) PDFs for flamelet manifold parameters (e.g., progress variable, scalar dissipation rates, etc.). Promising improvements have been outlined in computational efficiency and the accuracy of predicted variable fields in simulating complex combustion systems (such as turbulent inhomogeneous combustion, combustion with multi-regime modes, and two-phase combustion). Advances in computational resources, direct numerical simulation data, artificial intelligence, stochastic simulation methods, and other dimension-reduction combustion models will contribute to the development of more accurate and efficient flamelet-like models for engineering applications.

Abstract Micro-combustion based power generation devices can be considered as future alternatives to batteries in miniature electronic devices. Micro-combustors operating in non-premixed mode are free from flashback but face the challenge of properly mixing fuel and air within a small volume. In this work, the effect of a divergent fuel–air splitter design on the mixing performance and combustion characteristics of H2–air fueled diffusion micro-combustor is studied. The laminar reacting flow is simulated using the finite volume method and a detailed hydrogen kinetic mechanism. Three divergent splitter designs are compared with the commonly used rectangular splitter to study the effect on radiation power, an essential parameter for thermophotovoltaic power generation. The best-performing divergent and base rectangular splitter designs are investigated in detail. The study shows that the micro-combustor with divergent splitter design reduces mixing distance (Lmix) by 5–23% depending on inlet velocity and channel height. With the divergent splitter, the peak value of the heat release rate also increases slightly, implying enhanced combustion. The divergent splitter increases the high-temperature surface area of the outer wall as compared to the rectangular splitter. This leads to the micro-combustor with divergent splitter producing significantly higher radiation power (>10%) than the rectangular splitter for larger channel heights and higher inlet velocities.

Abstract The study aims to investigate the application of the organic Rankine cycle (ORC) in the bioenergy with carbon capture and storage (BECCS) using gasification of sewage sludge. The tool used in the investigation is the aspen plus software with refprop property methods for calculating fluid properties. The reason for this study is that a detailed analysis of the proposed BECCS process flow diagram indicates that a certain amount of waste heat is available in the exhaust gas from the high-to-intermediate pressure gas turbine. Some of this energy can be used by applying expansion in a low-pressure turbine, optionally by applying regenerative water heating, which is then redirected to the combustion chamber, or finally by incorporating the ORC into the main cycle. For the ORC cycle, different configurations are studied, with regeneration and using different working fluids. For the highest efficiency of the cycle, the regenerative heating of high-pressure water is applied and a suitable ORC working fluid with optimal saturation parameters and mass flow is selected. Such modified proposed BECCS power plant hybrid systems with ORC are compared to the reference case with lower pressure expansion. A study of the heat duty and temperature distribution in heat exchangers is carried out. Five ORC fluids were investigated, namely ethanol, refrigerants R236ea, R245fa, R1233zd(E), and water, which gave a net efficiency of the whole power plant of 39.71%, 40.02%, 40.26%, 40.34%, and 39.35% respectively, while the proposed BECCS reference case gave 38.89%.

Abstract To increase the waste heat recovery (WHR) efficiency of gas boiler and decrease NOx emissions, a flue gas total heat recovery (FGTHR) system integrating direct contact heat exchanger (DCHE) and combustion air humidification (CAH) is put forward. The experimental bench and technical and economic analysis models are set up to simulate and evaluate the WHR performance and NOx emissions in various operation situations. The results show that when the air humidity ratio elevates from 3 g/kgdry air to 60 g/kgdry air, the dew point temperature increases by 7.9 °C. When the flue gas temperature approaches the dew point temperature, the rate of improvement of the FGTHR system's total heat efficiency notably rises. With spray water (SW) flowrate and temperature of 0.075 kg/s and 45 °C, the WHR efficiency relatively increases by up to 8.4%. The maximum sensible and latent heat can be recovered by 4468 w and 3774 w, respectively. The flue gas temperature can be reduced to 46.55 °C, and the average NOx concentration is 39.6 mg/m3. Compared with the non-humidified condition, the NOx and CO2 emissions relative reduction of the FGTHR system are 61.2% and 8.7%. The payback period of FGTHR system is 2 years. Through simulation, it can be concluded that the decrease in exhaust flue gas temperature and velocity, as well as the increase in exhaust flue gas humidity, has a negative impact on the diffusion of NOx in the atmosphere.

Abstract Frac hits were not unfamiliar in tight gas development. During the hydraulic fracturing process in infill well, due to closely spaced wells and parent well depletion, operators often encounter fracture interference between the parent well and infill well, resulting in frac hits. This phenomenon typically has a significant impact on the productivity of both infill and parent wells. However, effectively mitigating and minimizing the negative effects of frac hits continues to pose a formidable challenge. Therefore, we developed a new frac hits evaluation and management workflow, aiming to investigate the mechanism of frac hits between infill well and parent well and improve the performance of the infill well while avoiding frac hits. The results indicate that an increased extent of parent well depletion leads to higher surrounding rock pressure and stress depletion. The stress deflection region near the fracture tip of the parent well attracts the propagation of infill well fractures, resulting in frac hits and significantly affecting the performance of the parent well. Consequently, optimizing the timing of hydraulic fracturing in the infill well is beneficial to mitigate parent well depletion, controlling frac hits and enhancing gas well productivity. This research provides important insights for mitigating parent–infill well interference in the development of tight gas reservoirs and establishes a solid foundation for future studies.

Abstract Vehicle fuel evaporative emissions are an important source of volatile organic compounds (VOCs), causing serious pollution to the environment. Plug-in hybrid electric vehicles (PHEVs) often use high-pressure fuel tank to seal the fuel vapor generated by running loss (RL), hot soak loss (HSL), and diurnal breathing loss (DBL) in the fuel tank, resulting in increased refueling emissions. With the widespread use of PHEVs, the issue of refueling emissions cannot be ignored. In this study, according to the working characteristics of PHEV, the refueling process is divided into depressurization phase and refueling phase, and a mathematical model is established for the fuel vapor emission process. The mathematical model is solved and calculated by using matlab, and compared with the experimental results. The error between experimental and calculated results of refueling emissions is only 2.45%, indicating that the established mathematical model can accurately predict the refueling emissions of PHEVs. The refueling emission experiment activities are carried out, and the influencing factors of PHEV refueling emission are discussed, including initial pressure, ambient temperature, and refueling temperature. The effect of the temperature difference between ambient temperature and refueling temperature on refueling emissions is discussed for the first time, and it is found that refueling temperature has a more significant impact on refueling emissions compared with ambient temperature. When refueling temperature increases to 303 K and 313 K compared to 293 K, refueling emission mass increases by 31.97% and 69.88% respectively.