Energy Science & Engineering最新文献

Manual interpretation of geophysical logging data can be a tedious and time-consuming task in the case of the nonlinear behavior of well-logging signals. This study aims to enhance lithology classification of reservoir formations through advanced machine learning (ML) and deep learning (DL) techniques, introducing and comparing three novel algorithms, GrowNet, Deep-Insight, and blender, against traditional models like random forest (RF) and support vector machine (SVM). Data from the South and North Viking Graben regions, encompassing 12 lithological facies, was preprocessed through cleaning, normalization, transformation, and imputation of missing values using regression models. The data set was enhanced with physic-based features and balanced using SMOTE and NearMiss algorithms. Deep-Insight converted tabular data into images for a convolutional neural network (CNN), significantly improving classification accuracy compared to conventional models such as decision trees (DTs). GrowNet and blender models leveraged hybrid approaches for enhanced performance. These hybrid approaches successfully addressed data imbalance and enhanced model learning, outperforming classic methods. The GrowNet and blender models for lithology classification successfully increased the penalty score and accuracy compared to the FORCE 2020 competition. Additionally, introducing the class prediction error plot visualizes multiclass classification performance more effectively than using a confusion matrix. These novel models in multiclass classification contribute to the petroleum industry by providing more accurate and reliable lithology classification, thereby improving reservoir characterization and exploration efficiency.

Most of the mine roadways in China are located in layered rock mass. To study the mechanical properties of a layered rock mass, the uniaxial compression test was carried out on the layered composite rock mass composed of sand and paraffin. The results showed that the locations of the high- and low-strength rocks were independent of the strength of the layered composite rock. The main failure site was not affected by the combination mode. Failure was mainly concentrated in the low-strength rock. The strengths of low- and high-strength rocks determined the lower and upper limits of the strength of the layered rock, respectively. When the thickness of the layered high-strength composite rock was >60%, the layered composite rock strength tended to be high; conversely, layered composite rock strength lowered the rock strength, and with increasing thickness, the layered composite rock strength was significantly enhanced. From the perspective of energy conversion, the effect of the thickness of the high-strength rock mass on the strength of the layered composite rock mass was analyzed.

In this study, computational fluid dynamic (CFD) simulation of a vertical axis wind turbine (VAWT) geometry based on the Unsteady Reynolds–Averaged Navier–Stokes equations was investigated. In addition, the relationship between the geometric parameters of the VAWT and the two response variables, that is, moment and lift force, was determined using response surface methodology (RSM). Then, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) was used to solve the multi-objective optimization problem. The results obtained from the RSM showed that the lift force of the turbine is more sensitive to the change in the blade chord length, and the output moment of the turbine is more sensitive to the change in the rotor radius. Using the NSGA-II multi-objective optimization algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, it was determined that among the optimal values of the independent variable, the most optimal response occurs in blade chord length = 0.18 m, rotor radius = 0.4 m, blade pitch angle = −3.27° and number of blades = 4. In these optimal values of the independent variables, the values of the dependent variables, which included the turbine's moment and the blades’ lift force, were obtained as 9.58 N m and 57.89 N, respectively.

Fly ash has emerged as a prominent solid waste in China, leading to various environmental concerns and posing a threat to the health of living organisms, including humans. To enhance the industrial applicability of this waste material, a novel approach has been proposed wherein sand is replaced with fly ash as the primary raw material for wall grouting of coal mine roadways. To address the issue of low compressive strength and to improve the properties of fly ash shotcrete materials, a method employing an alkali activator to stimulate the chemical activity of fly ash has been put forward. The long-term effectiveness of the shotcrete material has been evaluated using compressive strength testing and scanning electron microscopy testing methods. The impact of replacing sand with fly ash on the compressive strength of shotcrete and the activation effect of sodium sulfate (Na₂SO₄), NaOH, and Na₂SiO₃ on fly ash shotcrete have been studied. The results indicate that the compressive strength of fly ash shotcrete shows optimal improvement when the content of Na₂SO₄ is 3%. The ideal ratio of cement to fly ash is 1:3. Therefore, incorporating an appropriate amount of alkaline activator could effectively address the compressive strength issues of fly ash shotcrete materials.

Gas extraction drilling is a necessary measure for managing gas hazards. For soft coal seams where gas extraction drilling holes are prone to collapse, it is believed that drill rod disturbance is the main cause of hole collapse. This study proposes a research approach to reduce wall stress by optimizing the drill rod structure. Through theoretical analysis, numerical simulation, and industrial tests, a stress model for the drill rod inside the hole was established, and a wall stress equation was derived. The effects of various parameters on wall stress were analyzed. The study suggests optimizing the drill rod structure to reduce the disturbance-induced wall stress. SolidWorks was used for drilling stress simulation, and a four-winged concave groove drill rod was developed. After strength verification, comparative industrial tests were conducted. The research results show that as the line density increases, the wall stress of the drilling hole increases. As the length of the suspended section increases, the wall stress initially decreases and then increases. With increasing drilling thrust, wall stress increases linearly, and the growth rate is greater with a larger diameter difference between the drill hole and the drill rod. Numerical simulation results indicate that the critical point maximum stress at the hole entrance, the critical point maximum stress at the hole bottom, and the average stress at the bottom section of the four-winged concave groove drill rod with a concavity of 5 are significantly reduced compared to those of circular and grooved drill rods. Industrial test results show that using the four-winged concave groove drill rod significantly reduces the extent of hole collapse. This study provides a reference for addressing the issue of hole collapse in gas extraction drilling for soft coal seams.

Nowadays, the seismological monitoring system in China is a valuable tool in the rockburst risk evaluation for deep coal mines. In the past, only parameters, like source location and energy, are widely used to estimate the risk level of rockburst. Sometimes, it is effective; however, some other important physical parameters, such as apparent stress drop, static stress drop, P-wave velocity, and moment tensor, should also be included in order to improve the accuracy of risk assessment. In this study, these parameters are calculated using mine tremor signals recorded in the LW35001 workface of Liangbaosi Coal Mine. These calculations provide an overall identification of periodical stress distribution and rock mass energy-releasing type under high concentrated stress. Via linear moment tensor inversion procedure, the source mechanism of mine tremors and stress state of the rock mass is determined whether it is risk or not to underground roadway. This comprehensive analysis provides a specific guidance for rockburst prevention for coal mine management, that is, knowing when and where measures must be taken to decrease the risk level or induce the occurrence of rockburst under control.

The characteristics of gas seepage in broken coal and rock composed of different particle sizes and grades were investigated in this study. On the basis of Darcy's law and non-Darcy seepage theory, equations of gas permeability in the nonlinear seepage of broken coal and rock, as well as the porosity of broken coal and rock, under triaxial compression were determined. The stress loading path of gas seepage in broken coal and rock was developed. The characteristics of gas seepage in broken coal and rock composed of different particle sizes and grades were analyzed, and the results showed that the gas permeability after compression was proportional to the particle size of the broken coal and rock. Under triaxial compression, the gas permeability of the broken coal and rock composed of graded-particle sizes was lower than that of the broken coal and rock composed of different single-particle sizes. The gas permeability of the broken coal was lower than that of the broken rock mass, and the gas permeability and porosity of the broken coal and rock can be described by the exponential decay function. At a constant porosity, the gas permeability of the broken coal and rock was proportional to the size grading index under triaxial compression. The coefficient of viscosity and gravity of the flow are key factors influencing the flow permeability in broken coal and rock. This study provides a reference for on-site practice such as the efficient extraction of gas in goafs.

The speed of using renewable resources is expanding day by day. Renewable energy systems have many benefits for energy supply that do not include diesel, natural gas, or coal. Despite the many advantages, the use of renewable resources also includes basic challenges. With the presence of these sources, many technical issues must be considered in the network, the most important of which are voltage quality and network losses. The presence of these power plants can reduce fossil fuel costs and help reduce emissions. However, the high-capacity connection of these types of power plants in the transmission networks despite the uncertainty may cause the congestion of transmission lines, increase losses and decrease voltage quality. Therefore, to reduce the need to build transmission lines, energy storage devices can be installed and energy can be stored and returned to the network in certain hours. The purpose of this paper is to build the maximum capacity of wind power plants in the transmission network in such a way that its profitability is guaranteed. For this purpose, in addition to considering the costs related to the power plant, the costs of storage devices and the construction of possible new lines have been considered. Also, improving the technical conditions of the network and reducing the maximum emission after installing these units is considered as a multiobjective function. The problem tested on the standard IEEE test transmission network and the results show that it is possible to determine the maximum profitable capacity of wind power plants.

In recent years, there has been a lot of research and debate on how solar energy can be used instead of conventional sources of heating to power residential heating. In this study, the Trombe wall (TW) technique, based on natural convection and energy storage, was examined to predict mass flow rate, temperature field, and velocity field for the TW system under steady conditions. A numerical simulation model was investigated for further validation using k-ε turbulence and discrete ordinates (DO) radiation models. Independent grid studies were conducted to ensure that there were no changes after varying the grid numbers. The effect of the air gap was carried out to enhance TW thermal performance. CFD simulation shows good agreement with published data in the literature, and the optimum air gap was set at 0.1 m. The results pave the way for future studies to improve passive solar heating systems, which will eventually help move towards more sustainable residential heating solutions.

As China's demand for imported oil continues to grow, large-scale oil storage facilities have become increasingly important. Currently, China primarily uses underground salt cavern spaces and newly excavated underground water-sealed caverns for oil storage, which places high demands on the rock formations. China has abundant and widely distributed gypsum mineral resources, and utilizing abandoned gypsum mines for oil storage could not only turn waste into treasure by controlling underground space but also generate significant economic and social value. This article aims to systematically evaluate the mechanical properties of gypsum rock through long-term immersion tests in crude oil to assess the impact of crude oil immersion on the mechanical performance of gypsum rock and explore the feasibility of using gypsum mines as long-term stable oil storage caverns. The results show that oil immersion treatment reduces the uniaxial tensile strength of gypsum samples, but has little effect on their compressive strength and long-term strength. From a mechanical performance perspective, it is feasible to use gypsum mine voids for crude oil storage.