The lithium-ion battery (LIB) has the advantages of high energy density, low self-discharge rate, long cycle life, fast charging rate and low maintenance costs. It is one of the most widely used chemical energy storage devices at present. However, the safety of LIB is the main factor that restricts its commercial scalable application, specifically in hazardous environments such as underground coal mines. When a LIB is operating under mechanical and electrical abuse such as extrusion, impact, overcharge and overheating, it will trigger thermal runaway and subsequently cause fire or even an explosion. According to the relevant requirements in IEC60079, the explosion-proof protection of LIB can be adapted to the working environment of high dust and explosive gas environments such as in the mining face of coal production. This paper presents an overview of the LIB-relevant technology, thermal runaway, safety and applications in the general mining industry with implications to establish a theoretical and technical basis for the application of high-capacity LIBs in the industry. These then promote intelligent, safe and efficient production not only for the coal mine industry but also for non-coal applications.

A three-dimensional model for the simulation of concentration polarisation in a full-scale spiral wound reverse osmosis (RO) membrane element was developed. The model considered the coupled effect of complex spacer geometry, pressure drop and membrane filtration. The simulated results showed that, at a salt concentration of 10,000 mg/L and feed pressure of 10.91 bar, permeate flux decreased from 27.6 L/(m2 h) (LMH) at the module inlet to 24.1 LMH at the module outlet as a result of salt accumulation in the absence of a feed spacer. In contrast, the presence of the spacer increased pressure loss along the membranes, and its presence created vortices and enhanced fluid velocity at the boundary layer and led to a minor decrease in flux to 26.5 LMH at the outlet. This paper underpins the importance of the feed spacer’s role in mitigating concentration polarisation in full-scale spiral wound modules. The model can be used by both the industry and by academia for improved understanding and accurate presentation of mass transfer phenomena of full-scale RO modules by different commercial manufacturers that cannot be achieved by experimental characterization of the mass transfer coefficient or by CFD modelling of simplified 2D flow channels.

This paper studies the precise position control of the hydraulic cylinder in the hydraulic support. The aim of this paper is to develop a method of hydraulic cylinder position control based on pressure and flow coupling, which takes the coupling feedback of load and flow into account, especially in the scene of cooperative control under the condition of multiple actuators and variable load. This method solves the problems of slow movement and sliding effect of hydraulic support in the traditional time-dependent hydraulic position control, as well as better realizes the intelligent and unmanned development of the fully mechanized mining face. First, based on the flow continuity equation and Newton Euler dynamic equation, the flow and stroke control model with the input and output pressure of hydraulic cylinder is established. Then, the effectiveness and correctness of the control model are verified by the comparison between the hydraulic system simulation software, AMESim, and the experiment. Finally, a test system is built. When the system pressure is large than 10 MPa, the error between the data determined by the fitting algorithm and the actual detection data is within 5%, which verifies the effectiveness of the theory and simulation model.

This paper aims at the characteristics of nonlinear, time-varying and parameter coupling in a hydraulic servo system. An intelligent control method is designed that uses self-learning without a model or prior knowledge, in order to achieve certain control effects. The control quantity can be obtained at the current moment through the continuous iteration of a strategy–value network, and the online self-tuning of parameters can be realized. Taking the hydraulic servo system as the experimental object, a twin delayed deep deterministic (TD3) policy gradient was used to reinforce the learning of the system. Additionally, the parameter setting was compared using a deep deterministic policy gradient (DDPG) and a linear–quadratic–Gaussian (LQG) based on linear quadratic Gaussian objective function. To compile the reinforcement learning algorithm and deploy it to the test platform controller for testing, we used the Speedgoat prototype target machine as the controller to build the fast prototype control test platform. MATLAB/Coder and compute unified device architecture (CUDA) were used to generate an S-function. The results show that, compared with other parameter tuning methods, the proposed algorithm can effectively optimize the controller parameters and improve the dynamic response of the system when tracking signals.

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