A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Design and Optimization of New Smoke Exhaust Pipe for Mining Trucks

In order to improve the maintenance efficiency,extend the use time,ensure that the exhaust emission meets the standard,forthe 830E truck heating bucket exhaust pipe design defects,the current single smoke exhaust system is transformed into atime period,convertible smoke exhaust system.After the transformation,it can not only realize the side row to prevent direct corrosion of the box bucket in summer,but also realize the heating of the box bucket at low temperature in winter to prevent snow and ice and frozen blocks from sticking to the box bucket and the materials transported.After the transformation can save a lot of manpower,material resources,financial resources,improve the service life.

Cycle Time Analysis of Open Pit Mining Dump Trucks

This study demonstrates a practical cycle time analysis of dump truck haulage system of “Ukhaa Khudag” open-pit coal mine located in Umnugobi Province, Mongolia. It examines the possibility of minimizing the cycle time of the haulage system as well as factors impacting the speed of the dump truck. The current study divides the open pit mine road for the dump trucks into five sections which are bench road, ramp, surface road, dump road uphill, and dump road. Meanwhile, it investigates the influence of the length, the grade, and the rolling resistance of the road section on the cycle time. The data is analyzed using mathematical regression methods via Microsoft Excel program. For each of the five road sections, we compare the statistical calculations of three regression models: linear, quadratic and exponential;thus, a total of thirty regression models are obtained in this research. Accordingly, the cycle time for each road section is predicted by the most accountable model. The loaded and empty direction of the movement is measured and calculated for each road section, and it appears that the difference between the calculated mean value and the actual cycle time of the models is 0.82 seconds with a relative error of 2.51 percent.

基于TruckSim矿用汽车转向工况侧倾运动特性分析

车辆发生转向时,由于离心力的作用而产生侧倾运动,空载高速转向时该部分作用将更为明显,进而影响车辆平稳运输。根据悬挂系统的特性差异,对所研究矿用汽车前后悬挂的侧倾中心和侧倾轴进行分析;基于以上分析,对转向时的侧倾运动进行分析,获取关键参数的变化模型;根据理论分析结果,基于Matlab/Simulink建立车辆转向工况表征侧倾运动的分析模型;根据悬挂特性和转向特性,分别建立分析模型,与车辆多体动力学模型联合,基于TruckSim建立整车转向特征分析模型;分析在固定转向盘稳态回转试验时,车辆的行驶轨迹;对空载高速转向工况下,车辆内外轮转角特征曲线,悬挂刚度、位移、侧倾刚度等参数变化进行分析;对比分析考虑转向侧倾与否的转角特征及偏差与理论分析之间的差异,以验证分析的可靠性。结果可知:车辆内外侧悬挂缸出现伸缩跳动,此时出现了附加转向效应叠加在车轮上;车轮需要较大的侧偏角,满足侧向力的需要;此时,整车的转向角关系不单单满足Ackerman定理,还增加了轮胎的侧偏角;未考虑转向侧倾时,转角偏差值随着转角的增加而不断增大,最大偏差达到了0.4223°,而考虑时,转角偏差的目标值为0.3618°。因此表明,所建立的分析模型具有较好的分析结果,能够更精确的反应车辆转向过程中,侧倾运动各部分参数的变化,为此类研究提供参考。

A comparison of strategic mine planning approaches for in-pit crushing and conveying, and truck/shovel systems

In a global environment where energy and labor are becoming increasingly expensive, continuous mining systems such as In-Pit Crushing and Conveying(IPCC) systems have been advanced as offering a real alternative to conventional truck haulage systems. The implementation of IPCC systems in hard rock operations in open pit mines however requires different and more comprehensive planning approaches in order to adequately reflect the practical aspects associated with these. This paper investigates the impact that these approaches may have on the implementation of IPCC systems on a basic metalliferous deposit amenable to open pit exploitation. A strategic life of mine plan to provide numerous economic indicators for each approach is analyzed and compared to traditional truck haulage systems. The mine planning and evaluation process highlights the increased overall resource recovery that may accompany the use of IPCC systems. This investigation also provides insights into the issues associated with IPCC and the scale and type of operation and orebody that is likely to provide a feasible alternative to truck haulage.

Application of reliability-centered maintenance for productivity improvement of open pit mining equipment:Case study of Sungun Copper Mine

Equipment plays an important role in open pit mining industry and its cost competence at efficient operation and maintenance techniques centered on reliability can lead to significant cost reduction.The application of optimal maintenance process was investigated for minimizing the equipment breakdowns and downtimes in Sungun Copper Mine.It results in the improved efficiency and productivity of the equipment and lowered expenses as well as the increased profit margin.The field operating data of 10 trucks are used to estimate the failure and maintenance profile for each component,and modeling and simulation are accomplished by using reliability block diagram method.Trend analysis was then conducted to select proper probabilistic model for maintenance profile.Then reliability of the system was evaluated and importance of each component was computed by weighted importance measure method.This analysis led to identify the items with critical impact on availability of overall equipment in order to prioritize improvement decisions.Later,the availability of trucks was evaluated using Monte Carlo simulation and it is revealed that the uptime of the trucks is around 11000 h at 12000 operation hours.Finally,uncertainty analysis was performed to account for the uncertainty sources in data and models.

Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption

The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source.Cross vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption.In this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total resistance.The network was trained and tested using real data collected from a surface mining operation.The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.

Optimization model of truck flow at open-pit mines and standards for feasibility test

In order to increase production efficiency at open-pit mines, on the basis oflinear programming, a practical mathematical model for optimizing truck flow was developed, whichimproved the traditional fixed manual schedule method. The model has advantages from linearprogramming and objective programming, makes most handling points working at full capacity and keepsan optimized ratio between trucks and excavators. For ensuring feasibility of the model inpractical production, four standards for feasibility test were proposed. The model satisfied all thestandards. The application in a large scale open-pit iron mine indicated that the model reduced thenumber of required trucks by 10 percent compared with the fixed manual schedule method.

Health risk of whole body vibration in mining trucks during various operational conditions

Mining machineries are generally exposed to intensive vibrations in harsh mining environment. If vibrations are beyond the tolerable limit, the machine and its operator health will be under the risk. In this work, the vibration of a mining truck at different operational conditions are simulated and discussed. To achieve this aim, three haul roads with low, medium and poor qualities are considered based on the ISO standard. Accordingly, the vibration of a mining truck in different speeds, payload and distribution qualities of materials in the dump body are evaluated in each haul road quality using Trucksim software. The simulation results with statistical discussions indicate that the truck speed and the materials distribution quality have significant effects on the root mean square(RMS) of vertical vibrations. However, the effect of the payload is not considerable on the RMS. Moreover, the accumulation of materials on the rear side of the truck dump body is efficient on the vibrational health risk.

THE DEVELOPMENT AND EXPERIMENT OF A COMPUTER-CONTROLLED TRUCK DISPATCHING SYSTEM IN SURFACE MINE

The development and experiment of a computer-controlled truck dispatching system in Surface mine is presented in this paper.It includes the system overall design, the system working mode design, the selection of the best truck travel path, the development of truck fleet programming, the development of database management system, and the development of truck real time dispatching, etc. The successful experiment is carried out in Huolinhe surface mine, and the satisfied results are obtained. Application of this system can improve the system production and the mine management. This system is the first one in our country at present.

MineSched软件在露天矿车辆需求分析中的应用

露天矿全生命周期内的车辆需求分析是一个庞大而复杂的数学计算问题,利用传统方法求解计算过程相对复杂繁琐,且对实际运输过程中的多因素影响未能综合考虑,存在一定的局限性。为快速准确获得露天矿全生命周期内的车辆需求分析结果,精准预测或评估露天矿山的运输能力问题,以纳米比亚某露天铀矿为例,结合该矿山全生命周期排产的基础信息,在MineSched软件中构建全生命周期排产方案,利用软件中的Haulage功能模块对全生命周期内的车辆需求进行计算分析,以获得全生命周期内的卡车数量,并结合现有卡车数量和采矿承包商现有产能的分配,对全生命周期内的运输能力进行测算和评估,对运输能力缺口提出应对建议。结果表明,利用MineSched软件中的Haulage功能模块开展露天矿车辆需求分析较传统方法求解效率高,且能在真实模拟矿山物料实际运输路线轨迹的同时,综合考虑路况条件、道路坡度、季节变化、设备效率等多因素影响,充分显示了MineSched软件用于露天矿车辆需求分析的精准性和优越性,同时也为露天矿山运输能力的预测和评估提供了新的思路和方法。

基于多特征融合的露天矿区道路负障碍检测

随着智慧矿山概念的逐步落实,智能化、无人化逐渐在矿区落实,露天矿卡车无人驾驶日益成为矿山智能化建设的主要内容,为解决露天矿区复杂多变的道路因坑洼、塌陷等路面小部分下陷出现的非规则负障碍而导致矿区无人车、重载卡车侧翻等安全难题,提升矿区安全驾驶系数,提出一种多特征融合的露天矿区道路负障碍检测方法。该方法使用BiFPN特征融合模块,提高小型负障碍检测权重占比;引入空间和通道双注意力机制提高对负障碍边缘的特征提取和特征融合能力,从而提高对道路小尺度负障碍的检测精度;采用SIoU Loss作为模型边界框损失函数并使用K-means++方法优化Anchor以提高负障碍检测模型的收敛速度和边界框定位效果,并基于遗传算法优化超参数让模型更贴合矿区场景,最终实现对矿区道路负障碍的快速精准识别。实验表明该检测模型能快速准确识别复杂背景下露天矿区道路负障碍目标,对道路负障碍目标的检测精度、召回率、平均精确度均值分别达到了96.9%、89.9%、95.3%,且该模型大小仅有12.7 MB。对比其他主流检测网络,该网络模型更适合复杂环境下露天矿区非结构化道路行驶安全需求,且该检测模型的鲁棒性好,可适配于多种情况的露天矿区,为实际环境复杂多变的露天矿区非结构化道路负向障碍检测提供了可行的方法,为露天矿无人卡车安全生产运输提供安全预警。

基于双曲嵌入的露天矿区暗光环境下道路多目标检测模型

露天矿环境特殊,道路场景复杂多变,在光照不足时会导致矿区道路多目标识别不清、定位不准,进而影响检测效果,给矿区无人矿用卡车的安全行驶带来严重安全隐患。目前的道路障碍物检测模型不能有效解决矿区暗光环境对模型检测效果的影响,同时对矿区小目标障碍物的识别也有较大误差,不适用于矿区特殊环境下障碍物的检测与识别。针对上述问题,提出了一种基于双曲嵌入的露天矿区暗光环境下多目标检测模型。首先,在模型的图像预处理阶段引入卷积神经网路Retinex-Net对暗图像进行增强,提高图像清晰度;然后,针对数据集中特征过多而无重点偏好的问题,在加强特征提取部分添加全局注意力机制,聚集3个维度上更关键的特征信息;最后,在检测模型预测阶段引入双曲全连接层,以减少特征丢失,并防止过拟合现象。实验结果表明:(1)基于双曲嵌入的露天矿区暗光环境下道路多目标检测模型不仅对露天矿区暗光环境下的大尺度目标具有较高的分类与定位精度,对矿用卡车及较远距离的小尺度目标即行人也可准确检测与定位,能够满足无人矿用卡车在矿区特殊环境下驾驶的安全需求。(2)模型的检测准确率达98.6%,检测速度为51.52帧/s,较SSD、YOLOv4、YOLOv5、YOLOx、YOLOv7分别提高20.31%,18.51%,10.53%,8.39%,13.24%,对于矿区道路上的行人、矿用卡车及挖机的检测精度达97%以上。

基于ISSA-XGBoost模型的多特征融合露天矿卡车行程时间预测

针对露天矿运输系统卡车行程时间预测问题,提出了一种基于特征选择及改进麻雀算法优化XGBoost的露天矿卡车行程时间预测模型。模型充分考虑了卡车特征、道路特征、气象特征以及时间特征对卡车行程时间的影响,并使用皮尔逊系数法深入分析影响因素的贡献度。针对麻雀算法中全局搜索能力薄弱的问题引入反向学习和螺旋搜索策略,以提高算法的收敛性能。最后,使用改进的麻雀算法对XGBoost的关键参数进行寻优,进而构建露天矿卡车行程时间预测模型。选取国内某大型露天矿卡车调度系统采集的数据进行仿真模拟,并将所提出模型与SVM、BP、RBF和RF等其他机器学习模型进行对比。结果表明:所提出模型的预测误差均低于其他模型,相关系数可达0.9819。开发的模型和分析结果可以极大地帮助决策者规划、运营和管理更高效的露天矿运输系统。

基于DHMM-ESUM的露天矿运输系统车铲比优化研究

在露天矿生产中,如何利用现有的资源条件对电铲与卡车进行合理配比,充分发挥设备的效率,是提高矿山企业生产效益的关键。以安家岭露天矿生产系统收集的数据为基础,首先运用排队理论构建以电铲为采掘中心,卡车为运输工具的闭合排队网络模型,将运输系统分为4个排队子系统,分析各项服务时间的概率分布;基于离散隐马尔可夫模型(discrete hidden Markov model,DHMM)与拓展求和(extended summation,ESUM)算法分析系统作业性能,获取电铲设备的运行状态和班次生产能力。然后,应用蒙特卡洛法建立随机动态规划模型,以产量期望值最大化为目标对露天矿的车铲比进行优化。最后,对生产系统进行仿真建模分析,结果验证了基于DHMM-ESUM对车铲比进行优化的有效性和准确性。

基于Simulink空载高速转向工况车身运动特性分析

针对某矿用汽车悬挂进行分析,获取转向时的侧倾运动中心;对空载高速转向时,车身运动的关键特征参数进行分析,获取特征参数数学模型;采用Simulink搭建空载高速转向时的车身运动控制模型,并与车辆多体动力学模型联合开展运动特性分析;选取稳态回转工况,对转向特性进行分析,获取侧倾角、侧倾位移等参数的变化规律;将转向角与理论分析结果进行对比,并对比转向角偏差,以验证模型的准确性。结果可知:车身在高速工况转向,由于侧倾效应,外侧悬挂出现跳动,叠加了侧倾转角;侧倾运动对转角特性影响较大,分析模型与理论值基本一致,误差值小于5%;对转角偏差影响也较大,不考虑此部分的影响时,偏差增加了近20%。在精细化设计分析时,需要考虑此部分因素的影响。

露天矿作业区无人矿车协同通行决策方法研究

露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模型建模为混合整数线性规划(Mixed Integer Linear Programming, MILP)模型,表述优化目标和问题约束;其次,考虑到求解MILP模型存在难以满足动态决策实时性的问题,基于蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)实现多车冲突消解,核心思想是利用搜索树的推演能力进行多车通行前瞻模拟,计算多车的最优通行优先级,动态调整多车的可行驶距离;此外,根据无人矿车在作业区内的作业特征设计不同的MCTS节点价值函数,实现综合考虑运输效率与作业特征的通行优先级排序;最后,设计作业区4,8,12个停车位场景下的多车通行仿真实验,与基于先到先服务(First-Come-FirstServed, FCFS)的方法进行对比,吞吐量提升22.03%~28.00%,平均停车等待时间缩短31.71%~50.79%。同时,搭建微缩智能车辆的6停车位作业区场景实验平台,多车单次运输作业总用时相比FCFS缩短了18.84%。仿真与微缩智能车辆的实验结果表明,本文提出的方法能够提升露天矿作业区多车运输效率。

物料动态安息角对矿用自卸车厢斗结构影响规律

针对矿用自卸车行驶过程中的厢斗内物料稳定性问题,研究厢斗内物料动态安息角对厢斗结构设计的影响。首先,基于离散元方法,分析煤颗粒在自然状态下的安息角;进一步,建立厢斗简化模型,研究不同安装角度下矿车正常行驶及爬坡过程中物料的动态安息角,并分析物料滑动规律;结果表明:在水平路面行驶工况下物料动态安息角随厢斗安装角度增加而减小,而爬坡过程中由于厢斗内物料的滑动导致动态安息角随安装角度的增加先缓慢减小再变大再快速减小;当安装角度为10°时可有效避免厢斗内物料的滑动;最后,以确保厢斗稳定性装载及不撒料为目标,提出厢斗侧板后端的角度设计公式,为厢斗结构设计提供理论依据。

基于深度强化学习算法的纯电动矿用汽车再生制动策略研究

以载重50 t纯电动矿用汽车为研究对象,提出了一种基于深度强化学习优化算法的再生制动回馈策略.首先建立了纯电动矿用自卸车的数学模型.随后提出了一种考虑载重和坡度变化的基于自动熵调节Soft actor-critic (SAC)和深度确定性策略梯度算法(DDPG)的能量管理策略.其中车速、加速度、车辆质量与道路坡度、动力电池荷电状态(SOC)及充放电倍率作为状态变量;变速箱挡位作为动作变量;动力电池SOC及电池寿命作为奖励函数.仿真结果表明,基于动态规划的控制策略和所提出的基于SAC算法与基于DDPG算法的优化控制策略回馈效率分别提高了18.15%、17.18%和16.63%,电池寿命分别提升了57.31%、56.87%和57.38%.最后通过比较两种基于深度强化学习算法策略的奖励曲线,可以看出与基于DDPG算法的控制策略相比,所提出的基于SAC的能量管理控制策略的收敛速度提升了166.7%.