ABSTRACT Hot water injection has been a simple and promising method of thermally stimulating the extraction of hydrates, which promotes the dissociation of natural gas hydrates and improves gas production. However, the temperature region influenced by injecting hot water requires further research and evaluation. In this study, a computational model of the temperature field in the hydrate reservoir during hot water injection with the finite volume method, considering coupled gas–liquid two‐phase flow, heat conduction, and hydrate dissociation, was developed. The model focuses on hot water injection vertical wells completed with slotted liners in the Shenhu Sea area hydrate reservoir, which can consider the heterogeneity of porosity, permeability, and saturation. It also analyzes the effects of injection volume, injection rate, hot water temperature, and other factors on the variations in temperature and pressure distribution. The results indicate that selecting the appropriate injection volume, the temperature of hot water, and the injection rate can promote hydrate decomposition and expand the range of heat stimulation reservoir temperature. Reservoir heterogeneity leads to heterogeneity of the hydrate dissociation front and temperature influence range, and the influence range of heat stimulation is larger than homogeneous reservoir.
Polypyrrole is successfully introduced to enhance the reaction stability and ionic conductivity of LiNi1/3Co1/3Mn1/3O2 material through an ultrasound dispersion method and applied as cathode materials for lithium-ion batteries. This polymer can significantly advance the electrochemical properties. Expectedly, the 8 wt.% LiNi1/3Co1/3Mn1/3O2/polypyrrole composite has lower mixing degree of Li+/Ni2+, higher c/a value, which delivers the first discharge capacity of 199.2 mAh g−1, which abate to 121.3 mAh g−1 in the 300th cycle at 0.2 C between 2.5 and 4.5 V. Even at 3 C, it continues to reveal a reversible capacity of 86.4 mAh g−1 after 100 cycles. All the consequences implied that the 8 wt.% LiNi1/3Co1/3Mn1/3O2/polypyrrole verified a minor charge transfer resistance and better Li+ diffusion ability, hence establishing preferable rate and cycling performance compared with the primordial LiNi1/3Co1/3Mn1/3O2.
The randomness and fluctuation of large-scale distributed photovoltaic (PV) power will affect the stable operation of the distribution network. The energy storage system (ESS) can effectively suppress the power output fluctuation of the PV system and reduce the PV curtailment rate through charging/discharging states. In order to improve the operation capability of the distribution network and PV consumption rate, an optimal multi-objective strategy is proposed based on PV power prediction. First, the back propagation (BP) neural network with an improved genetic algorithm (GA) is used to predict PV power output. Furthermore, an adaptive variability function is added to the GA to improve the prediction accuracy. Then, the distribution network model containing distributed PV and the ESS is constructed. The optimal object contains network power loss, voltage deviation, and PV consumption. The model is solved based on the improved multi-objective particle swarm optimization (MOPSO) algorithm of Pareto optimality. The probabilistic amplitude is adopted to encode the particles for avoiding local optimal. Finally, the proposed optimal strategy is verified by the IEEE 33-bus distribution network. The results show that the proposed strategy has an obvious effect on reducing the network power loss and voltage deviation, as well as improving the PV consumption rate.
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