Lab studies of gas compositions on coal outburst

Coal and gas outburst remains one of the most severe dynamic hazards to many underground coal mining operations worldwide, posing great threats to mine safety and productivity. To understand the influence of gas composition on coal and gas outburst propensity, bulk coal samples were collected from underground coal mine sites in NSW and QLD, and subjected to experimental studies. Isotherm adsorption experiments were carried out using the gravimetric isotherm testing method to investigate the impact of coal seam gas composition on gas adsorption characteristics with a range of coal sample particle sizes, to a maximum gas pressure of 4 MPa, at 35°C. The seam gas composition employed in the tests included 100% CH4, a gas mixture of 50% CH4 and 50% CO2, and 100% CO2. For all test coal samples of different particle sizes, the adsorption capacity of CO2 was observed to be the highest, followed by the CO2/CH4 mixture and CH4. For a given gas content, the equilibrium gas pressure of a CH4 rich coal sample is significantly greater than the equivalent CO2 rich coal sample. Given that gas pressure provides energy to induce outbursts, it is reasonable to suggest that CH4 rich coal contains greater outburst initiating energy. Hysteresis occurs during the CH4 and CO2 sorption and is calculated by an improved hysteresis index (IHI) method. CO2 sorption hysteresis is more significant than CH4 sorption hysteresis, with the ratio of IHI_CO2/IHI_CH4 ranging between 1.50 and 2.25. At equivalent adsorption gas content, the amount of CO2 desorption is less than that of CH4, making it difficult to provide sufficient supply of desorption gas, resulting in low gas desorption energy, which is less conducive to the development of outburst. The research results can provide useful theoretical support for mine site gas management in underground coal mines, particularly those operating in areas with moderate to high composition of CO2 seam gas

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Railway electrification has attracted substantial interest in recent years as a key part of the global effort to achieve transport decarbonization. To improve the energy efficiency of train operations, of particular interest is the optimization of train speed trajectories. However, most studies formulate the problem as a single-objective optimization model and do not take into account train mass uncertainty associated with the passenger load variations. This article formulates a biobjective robust optimization model to minimize both the energy consumption and journey time, in which the robustness against the uncertain train mass is considered and viewed as a decision-maker preference. A novel multiobjective optimization algorithm, namely, p-nondominated sorting genetic algorithm-II (NSGA-II), is proposed, incorporating the original NSGA-II and a proposed preference dominance criterion to handle the DM preference. With the proposed p-NSGA-II, not only all solutions will converge to the optimal Pareto front but also solutions with better robustness in the Pareto front will be automatically selected and retained; meanwhile, the spread of the selected solutions is maintained. The effectiveness of the p-NSGA-II to generate a set of performance-robust driving schemes is verified by numerical case studies. The results show that the p-NSGA-II can achieve up to 40.59% robustness improvement compared to the original NSGA-II

Interpretable Deep Reinforcement Learning with Imitative Expert Experience for Smart Charging of Electric Vehicles

Deep reinforcement learning (DRL) is a promising candidate for realizing online complex system optimal control because of its high computation efficiency. However, the interpretability and reliability problems limit its engineering application in smart grid energy management. This paper for the first time designs a novel imitative learning framework to provide a reliable solution for computation-efficient grid-connected electric vehicles (GEVs) charging management in smart grids. The optimal strategies are derived by a priors optimization model based on vehicle-to-grid (V2G) cost-benefit analysis. With better interpretability and ensured optimality, the derived strategies are used to construct an experience pool for configuring the learning environment. Then, a novel imitative learning mechanism is designed to facilitate the knowledge transfer between expert experience and reinforcement learning model. Further, a novel dual actor-imitator learning network to enable flexible scheduling of V2G power of GEVs. With the dual network structure, the expert experience can be effectively utilized to enhance the training efficiency and performance of the DRL-based V2G coordinator. The effectiveness of the developed method in improving V2G benefit and mitigating battery aging is validated on a demonstrative microgrid in the UK.</p

Factoring Electrochemical and Full-Lifecycle Aging Modes of Battery Participating in Energy and Transportation Systems

Transportation electrification emerges as a pivotal strategy to realize deep decarbonization for many countries, and the central part of this is battery. However, a key challenge often overlooked is the impact of battery aging on the economy and longevity of electric vehicles (EVs). To address this issue, the paper proposes a novel battery full-life degradation (FLD) model and energy management framework that substantially improves the overall economic efficiency of Battery Energy Storage Systems (BESS). In the first stage, battery electrochemical aging features are modeled by learning cell fading rate under various healthy states, capitalized on the Stanford experimental open dataset. Accordingly, a lifecycle degradation model is then developed considering various operational conditions and aging stages to quantitatively assess the effects of depth of discharge, C-rate, state of health, and state of charge. In the second stage, battery electrochemical aging features are integrated into vehicle energy management so that batteries under different fading rates can be flexibly utilized during whole lifecycles. The proposed methods are validated on a practical UK distribution network and a hybrid vehicles hardware-in-the-loop platform. With the proposed methods, EV users can make informed decisions to optimize energy usage and prolong the lifespan of vehicle BESS, thereby fostering a more sustainable and efficient transportation infrastructure.</p

Factoring Electrochemical and Full-Lifecycle Aging Modes of Battery Participating in Energy and Transportation Systems

Transportation electrification emerges as a pivotal strategy to realize deep decarbonization for many countries, and the central part of this is battery. However, a key challenge often overlooked is the impact of battery aging on the economy and longevity of electric vehicles (EVs). To address this issue, the paper proposes a novel battery full-life degradation (FLD) model and energy management framework that substantially improves the overall economic efficiency of Battery Energy Storage Systems (BESS). In the first stage, battery electrochemical aging features are modeled by learning cell fading rate under various healthy states, capitalized on the Stanford experimental open dataset. Accordingly, a lifecycle degradation model is then developed considering various operational conditions and aging stages to quantitatively assess the effects of depth of discharge, C-rate, state of health, and state of charge. In the second stage, battery electrochemical aging features are integrated into vehicle energy management so that batteries under different fading rates can be flexibly utilized during whole lifecycles. The proposed methods are validated on a practical UK distribution network and a hybrid vehicles hardware-in-the-loop platform. With the proposed methods, EV users can make informed decisions to optimize energy usage and prolong the lifespan of vehicle BESS, thereby fostering a more sustainable and efficient transportation infrastructure.</p

Acoustic emission response characteristics and numerical simulation of soil failure under uniaxial compression

The burgeoning expansion of urban rail transit has brought the safety of tunnel construction to the forefront. Accidents arising from mechanical failures in the surrounding rock and soil serve as substantial impediments to its progression. This research delves into the acoustic emission (AE) response characteristics and the detrimental effects of uniaxial loads on silty clay. To achieve this, an experimental system was devised to ascertain both mechanical properties and AE responses. A damage model, predicated on cumulative AE counts, was developed, and the principles governing damage evolution were distilled. Following this, the Particle Flow Code (PFC) was employed for numerical simulation. By manipulating mesoscopic parameters, we exerted control over the macroscopic mechanical attributes. This enabled a deep dive into the AE response and the energy shifts during the failure mechanism, offering a mesoscopic lens to understand deformation and failure. Our findings suggest: (1) The AE response during failure can be stratified into five distinct phases, with pronounced AE activity in the loading failure domain, aligning with established engineering practices. (2) The damage model, rooted in cumulative AE counts, adeptly captures the sequential damage evolution, closely mirroring the stress-strain dynamics. (3) PFC effectively simulates internal fractures and the AE dynamics during failure, pinpointing areas of susceptibility for targeted interventions. This research stands as a pivotal reference for engineering stability initiatives, augmenting our ability to foresee and preemptively address potential damages

Quantitative analysis of global terrorist attacks based on the global terrorism database

Terrorist attacks have become a serious source of risk affecting the security of the international community. Using the Global Terrorism Database (GTD), in order to quantitatively study past terrorist attacks and their temporal and spatial evolution the analytic hierarchy process (AHP) was used to classify the degree of damage from terrorist attacks. The various factors influencing terrorist attacks were extracted and represented in three dimensions. Subsequently, using MATLAB for analysis and processing, the grading standards for terrorist attacks were classified into five levels according to the degree of hazard. Based on this grading standard, the top ten terrorist attacks with the highest degree of hazard in the past two decades were listed. Because the characteristics and habits of a terrorist or group exhibit a certain consistency, the K-means cluster analysis method was used to classify terrorists according to region, type of attack, type of target and type of weapon used by the terrorists. Several attacks that might have been committed by the same terrorist organization or individual at different times and in different locations were classified into one category, and the top five categories were selected according to the degree of sabotage inflicted by the organization or individual. Finally, the spatiotemporal evolution of terrorist attacks in the past three years was analyzed, considering the terrorist attack targets and key areas of terrorist attacks. The Middle East, Southeast Asia, Central Asia, and Africa were predicted to be the regions that will be most seriously affected by future global terrorist events. The terrorist attacks in Southeast Asia are expected to become more severe, and the scope of terrorist attacks in Africa is expected to widen. Civilians are the targets most at risk for terrorist attacks, and the corresponding risk index is considerably higher than it is for other targets. The results of this research can help individuals and the government to enable a better understanding of terrorism, improve awareness to prevent terrorism and enhance emergency management and rescue, and provide a solid and reliable basis and reference for joint counterterrorism in various countries and regions