基于多目标优化的露天矿进度计划编制方法研究
Coal Science and Technology（2022）
中国矿业大学（徐州）
Abstract
为寻求特定露天开采程序下的最优进度计划,实现矿山经济效益最大化.采用线性规划方法,以达产时间最短、达产后3 a内总采煤量最大及设备调动次数最少为目标,以设备数量、采剥工程关系及设计煤炭产量为约束条件建立进度计划编制的多目标优化模型,并利用优先级法对模型进行求解.设计了最早投、达产年的计算方法及年度采煤量均衡方法;利用表上作业法和反向动态规划设计了减少调动次数的进度计划编制方法.以布沼坝露天矿由西向东全区开采为例,将电铲数量、年生产能力、回采率、电铲能力、掘沟电铲数和扩帮电铲数作为限制条件,对露天矿进度计划进行编制,结果表明:达产后第3年可开采至第11阶段,并在该年末可采完第11阶段内87％的矿岩量.将第11阶段内与前10阶段的矿岩量表进行整合,作为基建开始至达产后第3年的总采剥工程量,根据年最大化原煤开采量与达产要求煤量的关系,利用优化模型进行迭代求解,得出均衡后的采剥量,利用表上作业法和反向动态规划完成采剥计划编制.优化后的进度计划可实现3 a投产、5 a达产、前8 a原煤开采总量达37.19 Mt、设备调动次数53次.与其他3种未优化的进度计划均值相比,投产时间提前了 0.3 a,达产年提前了 0.6 a,总采煤量增加了 3.7 Mt,总调铲次数减少了 19次,上述量化指标表现出明显优势,验证了本优化方法的效果.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined