Coal Transportation System Research Articles

A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process

In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS.

Influence and optimization of swirler parameters on elbow wear of deep coal fluidization pipeline transportation system

Elbow wear is a major threat to the multiphase pipeline transportation industry, which has a negative impact on the stable operation of the transportation system. In order to find the wear reduction methods of elbows in the fluidization pipeline transportation system for 2000-meter-deep coal resources, the swirler was installed upstream of the elbows, and wear simulations and tests of three kinds of elbows were carried out. The results showed that the maximum wear rate (MWR) of elbows increased and then decreased along the elbow angle. Due to the different directions of gravity, the heavy wear position (HWP) of the horizontal-vertical (H-V) elbow was in front of the vertical-horizontal (V-H) elbow. Because the downstream portion of the horizontal-horizontal (H-H) elbow was still a horizontal pipe, the HWP of the H-H elbow almost covered the whole elbow. The swirler placed upstream of the elbows could make the particles at the elbow move to the intrados of the elbows, resulting in less collision between the particles and the extrados of the elbows, thus reducing the wear of the elbows. The wear reduction effects of swirlers on three different elbows were favorably connected with the guide vane angle (GVA) and negatively correlated with the guide vane length (GVL), decreased first and then increased as the guide vane height (GVH) increased, and were little affected by the guide vane number (GVN) and the guide vane thickness (GVT). The mathematical models between the MWR of the elbows and guide vane parameters were established. By bench tests, the wear reduction effect of three kinds of elbows under the optimal guide vane parameters was 58.4%, 76.9%, and 78.6%, respectively. The errors between the bench test results and the simulation results were around 10%.

Coal flow volume detection method for conveyor belt based on TOF vision

Coal flow volume is the essential basic data support for intelligent speed regulation and energy-saving control of coal mine transportation systems. To accurately measure the coal flow volume of conveyor belts, an innovative coal flow volume detection method for conveyor belts based on TOF vision was proposed in the paper. Both depth and grayscale images of the coal flow were collected by a TOF camera. Then an improved fast marching method based on the grayscale image was used to achieve depth image restoration. The coal flow volume of conveyor belts could be calculated by using the surface fitting method. Experimental results demonstrate that the coal flow detection accuracy of the proposed method can reach 97.35%, and the single-frame image processing time is less than 70.72ms. The proposed method is verified to meet the accuracy and real-time requirements of coal mines.

Fuzzy Fault Tree Analysis and Safety Countermeasures for Coal Mine Ground Gas Transportation System

Fuzzy Fault Tree Analysis and Safety Countermeasures for Coal Mine Ground Gas Transportation System

Design of Motor Condition Monitoring System for Belt Conveyor in Coal Mine

With the increase of the number of mines and the depth of mining coal in China, it is more and more important for the safety performance of underground equipment. On the basis of analyzing the coal mine safety production and transportation system, we design the motor condition monitoring system of coal mine belt conveyor, choose high-precision sensors to collect the signals of voltage, current, temperature, torque and speed of the coal mine belt conveyor motor, and use the CAN communication circuit to transfer the collected data to the computer, so as to realize the condition monitoring of the coal mine belt conveyor motor.

Hierarchical Coordinated Optimization and Energy Management Control for Plug-in Hybrid Electric Heavy-duty Truck Platoon in Coal Mine Transportation System

Hierarchical Coordinated Optimization and Energy Management Control for Plug-in Hybrid Electric Heavy-duty Truck Platoon in Coal Mine Transportation System

A “Hardware-Friendly” Foreign Object Identification Method for Belt Conveyors Based on Improved YOLOv8

As a crucial element in coal transportation, conveyor belts play a vital role, and monitoring their health is essential for the coal mine transportation system’s safe and efficient operation. This paper introduces a new ‘hardware-friendly’ method for monitoring belt conveyor damage, aiming to address the issue of large parameters and computational requirements in existing deep learning-based foreign object detection methods and their challenges in deploying on edge devices with limited computing power. This method is tailored towards edge computing and aims to reduce the parameters and computational load of foreign object recognition networks deployed on edge computing devices. This method improves the YOLOv8 object detection network and redesigns a novel lightweight ShuffleNetV2 network as the backbone network, making the network more delicate in recognizing foreign object features while reducing redundant parameters. Additionally, a simple parameter-free attention mechanism called SimAM is introduced to further enhance recognition efficiency without imposing additional computational burden. Experimental results demonstrate that the improved foreign object recognition method achieves a detection accuracy of 95.6% with only 1.6 M parameters and 4.7 G model computational load (FLOPs). Compared to the baseline YOLOv8n, the detection accuracy has improved by 3.3 percentage points, while the number of parameters and model computational load have been reduced by 48.4% and 42.0%, respectively. These works are more friendly to edge computing devices that tend to “hardware friendly” algorithms. The improved algorithm can reduce latency in the data transmission process, enabling the accurate and timely detection of non-coal foreign objects on the conveyor belt. This provides assurance for the subsequent host computer system to promptly identify and address foreign objects, thereby ensuring the safety and efficiency of the belt conveyor.

Assessment of dimethyl sulphide odorous emissions during coal extraction process in Coal Mine Velenje

Underground coal extraction at Coal Mine Velenje occasionally gives rise to odour complaints from local residents. This manuscript describes a robust quantification of odorous emissions of mine sources and a model-based analysis aimed to establish a better understanding of the sources, concentrations, dispersion, and possible control of odorous compounds during coal extraction process. Major odour sources during underground mining are released volatile sulphur compounds from coal seam that have characteristic malodours at extremely low concentrations at micrograms per cubic metre (μg/m3) levels. Analysis of 1028 gas samples taken over a 6-year period (2008–2013) reveals that dimethyl sulphide ((CH3)2S) is the major odour active compound present in the mine, being detected on 679 occasions throughout the mine, while hydrogen sulphide (H2S) and sulphur dioxide (SO2) were detected 5 and 26 times. Analysis of gas samples has shown that main DMS sources in the mine are coal extraction locations at longwall faces and development headings and that DMS is releasing during transport from main coal transport system. The dispersion simulations of odour sources in the mine have shown that the concentrations of DMS at median levels can represent relatively modest odour nuisance. While at peak levels, the concentration of DMS remained sufficiently high to create an odour problem both in the mine and on the surface. Overall, dispersion simulations have shown that ventilation regulation on its own is not sufficient as an odour abatement measure.

Dynamic inspection and maintenance scheduling for multi-state systems under time-varying demand: Proximal policy optimization

Inspection and maintenance activities are effective ways to reveal and restore the health conditions of many industrial systems, respectively. Most extant works on inspection and maintenance optimization problems have assumed that systems operate under a time-invariant demand. Such a simplified assumption is oftentimes violated by a changeable market environment, seasonal factors, and even unexpected emergencies. In this article, with the aim of minimizing the expected total cost associated with inspections, maintenance, and unsupplied demand, a dynamic inspection and maintenance scheduling model is proposed for Multi-State Systems (MSSs) under a time-varying demand. Non-periodic inspections are performed on the components of an MSS and imperfect maintenance actions are dynamically scheduled based on the inspection results. By introducing the concept of decision epochs, the resulting inspection and maintenance scheduling problem is formulated as a Markov Decision Process (MDP). The Deep Reinforcement Learning (DRL) method with a Proximal Policy Optimization (PPO) algorithm is customized to cope with the “curse of dimensionality” of the resulting sequential decision problem. As an extra input feature for the agent, the category of decision epochs is formulated to improve the effectiveness of the customized DRL method. A six-component MSS, along with a multi-state coal transportation system, is given to demonstrate the effectiveness of the proposed method.

Magnetic Levitation Belt Conveyor Control System Based on Multi-Sensor Fusion

Belt conveyors are critical in coal transportation systems; however, they are supported by rollers, which incur high energy costs. Magnetic levitation support is adopted. The measurement of a magnetic levitation air gap is important, and a displacement sensor is usually used in real-time. To address the problems of poor linearity and low measurement stability of single displacement sensors in magnetic levitation systems, a magnetic levitation support system based on multi-sensor data fusion is proposed. First, an industrial camera sensor is used to collect images, and the suspended air gap is obtained through image processing. Second, using an extended Kalman filter, the photoelectric position sensor and industrial camera sensor are fused to the suspension air gap value in the process of suspension. The electromagnet current is controlled by the PID (proportion integration differentiation) control algorithm, and experiments are carried out. Experimental results showed that, compared with an estimation of the suspension state of two sensors before and after EKF fusion, the root mean square error is reduced by 0.0597 and 0.0081, respectively, compared with the measurement value of a single sensor. Moreover, the fusion data were more robust, thereby meeting the requirements of magnetic levitation control.

Modelling of automated steam coal transportation and enrichment systems for efficient operation and cost reduction of thermal power plants

This paper investigates the efficiency of fuel enrichment to reduce the costs of thermal power plants (TPPs). An objective function is formulated to define and measure the desired results. The annual costs of TPPs and additional costs associated with the use of highly abrasive coal are analyzed. The costs of enrichment, in particular, flotation enrichment, are considered. A cost analysis and a transportation problem are presented to demonstrate the results. In general, this paper provides insight into the financial implications of fuel enrichment strategies for thermal power plants.

Scheduling heterogeneous repair channels in selective maintenance of multi-state systems with maintenance duration uncertainty

Selective maintenance with multiple repair channels has received increasing attention in recent years. The problem aims to jointly identify a subset of units to be maintained and assign the selected maintenance tasks to multiple repair channels. Existing works, however, all assumed that the time durations of maintenance tasks and breaks are deterministic. Due to the randomness of the starting time of future missions and the variation of efficiency of repair channels, these two quantities inevitably possess uncertainty. In this article, by taking account of the uncertainties associated with the time durations of maintenance tasks and breaks, a new selective maintenance model with multiple heterogeneous repair channels is formulated. The new selective maintenance problem decides not only a subset of units to be maintained and the corresponding maintenance tasks, but also the number of repair channels with their specific skill levels and the sequences of maintenance tasks in each repair channel. The objective is to maximize the probability of a system successfully completing the next mission subject to a limited maintenance budget. The resulting optimization problem is resolved via a double-loop algorithm embedded with an ant colony optimization. A five-unit system and a multi-state coal transportation system are presented to demonstrate the effectiveness of the proposed method.

Research Status and Development Trend of Unmanned Driving Technology in Coal Mine Transportation

Unmanned driving technology has always been one focus of mine smart transportation, which is a crucial component of smart mines. However, the descriptions of both the intelligent transportation system and its industrial application are not comprehensive. In order to have an all-encompassing and comprehensive understanding of both the intelligent coal mine transportation system and its industrial application with the intelligent system, this paper summarizes and analyzes the current research status of unmanned driving technology in mines and the industrial application of current mining transportation vehicles. It begins by outlining the current research state of unmanned driving technology in mines and then assesses the advancement of unmanned driving technology. The unmanned transportation system in mines is also introduced, together with its components for perception, location, path planning, vehicle control, and multi-vehicle scheduling. In addition, each component is described in combination with the current artificial intelligent unmanned technology individually. Then, some typical categories of intelligent industrial vehicles are introduced for learning about the conditions of their actual application. There are almost four hundred coal mines that have researched unmanned driving technology, and some companies have applied the unmanned technology to realize transportation with an efficiency enhancement of 70~80%. Finally, currently existing challenges and future research are analyzed and proposed. This review may provide more comprehensive knowledge of the intelligent coal mine, accelerating the development of intelligent technology and helping to build a new management and control model.

Hybrid Discrete Differential Evolution and Deep Q-Network for Multimission Selective Maintenance

The multimission selective maintenance problem (MSMP) for repairable systems has received increasing attention in recent years. The problem amounts to selecting a subset of feasible maintenance actions, in view of the resource limitations. For considering the realistic case of the imperfect maintenance, this article introduces a hybrid imperfect maintenance model, which is more realistic to evaluate the system reliability. The challenge of solving such kind of problems lies not only in the reliability estimation, but also in the solution method of the maintenance selection. Such decision-making problem can be effectively formulated using the Markov decision process, but it is difficult to apply current methods for solving the engineering systems with large action decision spaces. In order to solve this issue, this work puts forth a novel hybrid algorithm for the MSMP in a large multicomponent system. In the proposed method, a discrete differential evolution algorithm is developed for searching the optimal maintenance action in large-scale discrete action spaces and the deep Q-network method is utilized to approximate the effectiveness of maintenance actions and facilitate the agent training. The experiments, based on a large-scale coal transportation system, verify the effectiveness of the proposed method compared with LSDQN and differential evolution.

Innovative Measures and Application of Coal Mine Electromechanical Transportation System Technology

Innovative Measures and Application of Coal Mine Electromechanical Transportation System Technology

Selective maintenance and inspection optimization for partially observable systems: An interactively sequential decision framework

Selective maintenance is an important condition-based maintenance strategy for multi-component systems, where optimal maintenance actions are identified to maximize the success likelihood of subsequent missions. Most of the existing works on selective maintenance assumed that after each mission, the components’ states can be precisely known without additional efforts. In engineering scenarios, the states of the components in a system need to be revealed by inspections that are usually inaccurate. Inspection activities also consume the limited resources shared with maintenance activities. We, thus, put forth a novel decision framework for selective maintenance of partially observable systems with which maintenance and inspection activities will be scheduled in a holistic and interactively sequential manner. As the components’ states are partially observable and the remaining resources are fully observable, we formulate a finite-horizon Mixed Observability Markov Decision Process (MOMDP) model to support the optimization. In the MOMDP model, both maintenance and inspection actions can be interactively and sequentially planned based on the distributions of components’ states and the remaining resources. To improve the solution efficiency of the MOMDP model, we customize a Deep Value Network (DVN) algorithm in which the maximum mission success probability is approximated. A five-component system and a real-world multi-state coal transportation system are used to demonstrate the effectiveness of the proposed method. It is shown that the probability of the system successfully completing the next mission can be significantly increased by taking inspections into account. The results also demonstrate the computational efficiency of the customized DVN algorithm.

Mine track obstacle detection method based on information fusion

As an important part of the coal mine transportation system, coal mine underground rail transportation undertakes the core transportation task of coal mine underground. Its safe and efficient operation is directly related to the efficiency of coal mine production and transportation. In view of this, this paper proposes a mine track environmental obstacle detection system which integrates camera and Lidar information to realize real-time automatic detection of obstacles in front of the underground track mine cart. The specific research results are as follows: first of all, a point cloud clustering algorithm for the mine environment is designed to extract the obstacle information, and then the YOLOv5 algorithm is used to identify the obstacle information in the image. Finally obstacle information from image and point cloud are fused at the decision-level. The obstacle detection method proposed in this paper can be successfully identified to meet the requirements of the obstacle object detection function of the rail vehicle.

Importance measure construction and solving algorithm oriented to the cost-constrained reliability optimization model

Importance measures are used to prioritize the system components for achieving high efficiency and economy of reliability optimization. The existing importance measures pay more attention to evaluating the impact of component performance changes on the objective function (such as system reliability, cost and so on). Still, these importance measures do not consider the influence of constraints in the system reliability optimization models during the ranking process of components, which may leave the component with the largest importance measure almost no space to improve its reliability. For the cost-constrained reliability optimization model (ROM), this paper proposes a cost-constrained ROM-based importance measure (CRIM) by comprehensively considering the objective function and constraints, and several properties of CRIM are discussed. Then, the CRIM-based genetic algorithm (CRIGA) is developed to solve the cost-constrained ROM. The numerical experiment for different scale systems is implemented to evaluate the performance of CRIGA by comparing it with other optimization algorithms. Experimental results show that CRIGA can get better solutions with faster convergence speed and better robustness. Finally, the case on the coal transportation system of the thermal power plant is introduced to illustrate the application of CRIM and CRIGA in reliability optimization.

Numerical simulation and an experimental test for the pneumatic spray dust reduction of the belt transshipment system

In order to address the problem of dust pollution at the transfer point of the coal transportation system, this paper carries out a numerical simulation on the dust diffusion law of the coal transportation system. The simulation results show that the dust concentration at the three openings in the transshipment point is the highest, and the farther away from the transshipment point, the lower the dust concentration. Depending on the simulation results, a pneumatic spray dust reduction scheme can be proposed by performing a field experiment. Based on the controlling variable experiments and existing studies, the parameters of pneumatic spray can be stated in the following way: the flowing rate should be laid out at 0.7 l/min, with its air velocity being 25 m/s and the working pressure being 0.6 MPa. The entire spray scheme can be verified through the computer simulation process. Thus, it can be calculated and determined that the spray scheme should be installed with four nozzles at the anterior end of the lower belt guide trough, with one nozzle at the back end and two nozzles at the upper belt feeding point. Eventually, the scheme has to be validated by the field experiments. Thus, the consequences of the on-spot experiment and test show that the scheme we have suggested is in a position to reduce the dust concentration effectively in the entire area of the transshipment point, bringing about the dust reduction rate over 90%.

Selective maintenance on a multi‐state system considering maintenance task assignment and operating cost

AbstractFor some engineering systems where there is a finite break between two consecutive missions, maintenance activities can only be performed during the break, and the budget is also limited. Due to limited resources, it is necessary to select a subset of the maintenance activities to be performed, with the aim of meeting the next mission requirement. This is termed as selective maintenance. We propose a selective maintenance model for a multi‐state system in which both maintenance task assignment and operating cost are considered. The algorithm of maintenance task assignment is proposed inspired by the 0–1 knapsack problem to help fulfill the maintenance optimization. The correctness and computationally effectiveness of this algorithm are validated. With the degradation of each component modeled as a homogenous Markov process, the operating cost of the system is modeled based on the Markov reward process and is treated as part of the total cost. Additionally, the randomness of the operating time is considered and the necessity of this consideration is illustrated. The proposed model is applied to a coal transportation system to demonstrate its effectiveness, and the effects of ignoring maintenance task assignment and operating cost are analyzed. The results show that it is essential to take the maintenance task assignment into consideration, especially when the time constraint plays a major limiting role, as failure to do so may lead to poor system optimization. Furthermore, not considering the operating cost will underestimate the requirement of the resources, more so when a higher reliability is required.