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For mine dust deflagration accidents deflagration accident that occur frequently all over the world and are likely to lead to profound consequences, apart from the traditional methods of dust suppression and dust removal, the use of flame retardants can play an important role in suppressing the occurrence and development of mine dust deflagration accidents. Therefore, this paper mainly summarizes the mechanism of various typical flame retardants to suppress mine dust explosion and the changes in the occurrence and development process of their reaction and summarizes the advantages and disadvantages of various typical flame retardants that can be used to suppress mine dust explosion, the factors that affect the flame retardancy of flame retardants, the practicability of new nano-flame retardants and the feasibility of synergistic action of various flame retardants. Figure GA. An overview of flame retardant for mine dust deflagration. The three typical flame retardants can play a role in suppressing mine dust deflagration accidents, and the synergistic effect of flame retardants provides more feasibility for the use of flame retardants. [Display omitted] [ABSTRACT FROM AUTHOR]

• A safe reinforcement learning method for smart home energy management is proposed. • The operation of the components in a multi-energy smart home is modeled as a CMDP. • The approach minimizes total energy cost and avoids constraints violations. This paper proposes a data-driven approach for multi-energy management of a smart home with different types of appliances, including battery energy storage system (BESS), thermal energy storage system (TES), micro combined heat and power system (mCHP), electrical heat pump (EHP), rooftop photovoltaics (PV) and electrical vehicle (EV). Firstly, home energy management (HEM) is formulated as a cost minimization problem with hard constraints to optimize energy generation, storage, and consumption. Secondly, this paper proposes a safe reinforcement learning (SRL) approach with Primal-Dual Optimization (PDO) policy search-based algorithm to solve the HEM problem. Unlike existing DRL methods, the proposed approach learns to minimize costs from accumulated cost functions and automatically tunes the cost function coefficients to achieve zero constraints violation. Besides, a dynamic electricity price forecasting model based on CNN-LSTM neural network is designed to deal with the uncertainties in future prices. To verify the performance of the proposed HEM method, simulations are conducted using Singapore wholesale electricity price data. Numerical results demonstrate that the proposed method has a better ability to minimize energy costs and satisfy constraints compared to existing methods. [ABSTRACT FROM AUTHOR]

This study explores impact of novel parallel S-type flow channels on ammonia decomposition and thermal-electric performance of direct ammonia solid oxide fuel cells (DA-SOFC). Four channel arrangements were numerically studied using single cell models: conventional unbent channels (UC–SOFC) and proposed single S-type channels (SSC–SOFC), double S-type channels (DSC-SOFC), triple S-type channels (TSC-SOFC). Cells with parallel S-type channel configurations outperformed unbent channel. Particularly, TSC-SOFC demonstrated the highest electrical performance, followed by DSC-SOFC, SSC-SOFC, and UC-SOFC, with power density improvements of 33.6%, 28.9%, and 28.9%, respectively, compared to UC-SOFC. Further results show that S-type channels exhibit self-heating effects, namely heat released from electrochemical reactions in the rear supplies ammonia decomposition in the front part of channel, enhancing H 2 concentration and electrochemical reactions. Quantitatively, back-heat supplied to primary ammonia decomposition region increases by 33.3% in TSC-SOFC compared to UC-SOFC. Moreover, higher velocities in S-type channels enhance species diffusion towards rib underside, finally reducing concentration polarization and, improving power density. Besides of electrical performance, recycling waste heat from exhaust gases also improve the temperature uniformity cell, potentially benefiting the stability and lifetime of cell. [Display omitted] • Mathematical modeling of large-scale DA-SOFC single cells. • Cell performance optimization considering in-cell ammonia decomposition. • Promoting in-cell ammonia endothermic decomposition via a novel parallel S-type channel. • Both self-heating feedback and enhanced velocity toward the underside of the ribs are observed in the novel channel. [ABSTRACT FROM AUTHOR]

Accurate solar photovoltaic (PV) generation forecast is critical to the reliable and economic operation of a modern power system. In practice, due to various faulty issues in the sensor, communication, or database system, the historical and online measurement data may not be always complete, and the missing data could dramatically degrade the forecasting model's accuracy. To solve this problem, this paper proposes an integrated missing-data tolerant model for probabilistic PV power generation forecasting. Taking historical PV generations as input, this model is based on a recursive long short-term memory network (Rec-LSTM), which can provide multi-step ahead forecasting of the probability distribution of PV generation. The unobserved input data will be imputed recursively based on the model output at the previous time step. During the training process, the imputations and forecasting values are iteratively updated by the negative log-likelihood loss function. As a salient advantage, this method can deal with data missing scenarios at both offline and online stages. Numerical experiments are conducted on two one-year datasets from Australia and Singapore, respectively. Probabilistic forecasting for both large-scale and small-scale building-level PV power generation is tested at the time resolution of 15 mins. Testing results show the proposed method can achieve superior probabilistic prediction accuracy as well as strong robustness under various data missing scenarios, compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Study Design: A prospective study.Objective: The aim of this study was to investigate the relationship between diffusion tensor imaging (DTI) derived parameters of compressed nerve roots at subregions and the corresponding clinical symptoms to evaluate the patients with intraspinal lumbar disc herniation (LDH)-related lumbosacral radiculopathy pre- and postoperatively.Summary Of Background Data: It is crucial to explore whether magnetic resonanve imaging (MRI) can quantitatively evaluate intraspinal LDH-related lumbosacral radiculopathy before and after surgery.Methods: In all, 66 patients underwent MRI scans and Clinical assessment before and after percutaneous transforaminal endoscopic discectomy (PTED). Pre- and postoperative findings of the related lumbar disk and nerve tractography were compared with two-way contingency table analysis. The embedded paired t test toolbox was applied to respectively compare the fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of nerves at the symptomatic and asymptomatic sides in three subregions pre- and postoperatively. The correlation of clinical Japanese Orthopedic Association (JOA) scores and FA/ADC values of nerves at three sub-regions was analyzed by stepwise multiple linear regression analysis.Results: The postoperative FA values were significantly higher than the corresponding preoperative values (P < 0.001), while comparable ADC values were found. Using tractography, a notable improvement of compressed nerve was revealed after surgery (61 cases, 92.4%). Additionally, multiple linear regression analysis identified significant associations between JOA scores and FA values of the compressed nerves with the greatest effect at the proximal region.Conclusion: The FA values at subarticular zone can reflect the microstructural changes of the corresponding compressed nerves and well associate with clinical symptoms. Therefore, the DTI parameter FA can be considered an effective tool in clinic to quantitatively evaluate intraspinal LDH-related lumbosacral radiculopathy before and after PTED surgery.Level of Evidence: 3. [ABSTRACT FROM AUTHOR]

Ammonia (NH 3) is carbon-free and is known as a promising renewable fuel to achieve carbon reduction and carbon neutrality. However, ammonia presents much lower fuel reactivity compared to hydrocarbon fuels. Thus, ammonia is often blended with high reactivity fuels in practical combustion facilities, and natural gas is a good candidate due to its low carbon emissions and massive reserves. Methane is often used to represent natural gas in previous experimental and simulation studies, while the larger alkanes in natural gas can affect the fuel reactivity of ammonia/natural gas blends. This study aims to explore the effects of fuel components on the auto-ignition behaviors of ammonia/natural gas blends. As methane and ethane are the most important species in natural gas, a blend of 90 % methane and 10 % ethane by volume is used to represent natural gas in this study. A low-pressure shock tube was used to measure the ignition delay times (IDTs) of ammonia/methane, ammonia/ethane, and ammonia/natural gas blends in 'air' mixtures, with different ammonia blending ratios, at stoichiometric condition, at 1 atm and over the temperature range 1200 – 1800 K. The experimental results show that the IDTs of fuel blends increase with ammonia blending ratios. Meanwhile, 10 % ethane in natural gas can influence the fuel reactivity of ammonia/natural gas blends. A detailed kinetic model proposed in our previous study is used to simulate these new IDTs in addition to experimental data available in the literature. Overall, the kinetic model can predict well the auto-ignition behavior of ammonia/methane, ammonia/ethane, and ammonia/natural gas blends over a wide range of experimental conditions. The simulation and sensitivity analyses results show that the interaction reaction between ammonia and ethane, C 2 H 6 + ṄH 2 〈=〉 Ċ 2 H 5 + NH 3 , is important for predicting the fuel reactivity of ammonia/ethane blends below 1600 K at the studied conditions. [ABSTRACT FROM AUTHOR]

• Accurate process models for kW-level ammonia SOFC systems are developed. • Effect of off-gas recirculation on ammonia SOFC system is studied with energy flow diagram. • An optimization strategy by off-gas steam separation is proposed for ammonia SOFC system. • Comprehensive analyses of efficiency, power density and thermal safety are conducted. Solid oxide fuel cell (SOFC) power system fueled with the carbon-free fuel ammonia is a promising technique to reduce CO 2 in traffic and transportation sector. This study constructed zero-dimensional steady-state models for the SOFC stack and corresponding balance of plant (BOP) components, and conducted efficiency, power density and thermal safety analysis on a kW level ammonia SOFC system. The effects of off-gas recirculation strategy on system performance were firstly studied. The results show that in the basic ammonia SOFC system without off-gas recirculation, the fuel and air stream entering the afterburner are strictly determined by the stack operating parameters such as stack fuel utilization and flow rate. This makes its net electrical efficiency be not only low but also impossible to be independently and flexibly adjusted via controlling the fuel thermally consumed in the afterburner. The use of anode off-gas recirculation (AOGR) greatly improves the net electrical efficiency and operating flexibility via decoupling the system fuel utilization from the stack fuel utilization. By contrast, cathode off-gas recirculation (COGR) exhibits adverse effects, which reduces the net electrical efficiency and is not conducive to the thermal stability of the afterburner. In the end, a new system architecture design (SOFC-AOGR-SS) is proposed, which adopts both AOGR and steam separation in the anode recirculated off-gas using the vaporization cooling of liquid ammonia. The proposed system could operate at extended stack fuel utilization range of 0.67–0.80 and achieve a net electricity efficiency of 51.94–56.50% without external cooling water. Moreover, with additional external cooling water, the stack fuel utilization range and the net electricity efficiency of system could be extended to 0.58–0.80 and 51.84–57.15% respectively. [ABSTRACT FROM AUTHOR]