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| CO2+O2浸出采铀抽注液量智能控制系统设计与实验 |
| Design and Experiment of an Intelligent Control System for CO2+O2 Leaching Solution Flow in Uranium Extraction |
| 投稿时间：2025-01-09 修订日期：2025-03-19 |
| DOI： |
| 中文关键词: 地浸采铀 数字矿山 抽注液量 深度学习 智能控制 |
| 英文关键词:in-situ leaching uranium mining digital mine extraction and injection fluid volume deep learning intelligent control |
| 基金项目:湖南省省级大学生创新创业训练计划项目（S202310555133） |
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| 中文摘要: |
| 地浸采铀过程中抽注液量平衡调控和智能识别抽注液异常是保障高效浸出和井场安全的重要措施。基于深度学习技术构建了CO2+O2浸出采铀抽注液量智能控制系统，该系统主要包含集成长短期记忆网络的智能数据采集子系统、基于Transformer框架的溶浸液智能配置子系统和基于Matlab算法的抽注液量智能控制子系统。通过内蒙古某铀矿生产数据验证，该系统能够改善数据采集的实时性，稳定浸出剂的pH值，优化抽注比。建立抽注液量智能控制系统有利于实现数字化管控、提高井场生产效能、降低堵塞风险，同时对促进铀矿山数字化和智能化建设进程具有重要意义。 |
| 英文摘要: |
| During the process of in-situ leaching uranium mining, maintaining a balance in the volume of extraction and injection fluids and intelligently identifying anomalies in these fluids is crucial for ensuring efficient leaching and safety at the well site. A smart control system for CO2+O2 leaching uranium mining fluid extraction and injection has been constructed based on deep learning technology. This system primarily consists of an intelligent data acquisition subsystem incorporating Long Short-Term Memory networks, an intelligent lixiviant configuration subsystem based on the Transformer framework, and an intelligent extraction and injection volume control subsystem based on Matlab algorithms.Verification using production data from a uranium mine in Inner Mongolia shows that this system can enhance the real-time nature of data collection, stabilize the pH value of the leaching agent, and optimize the extraction-to-injection ratio. Establishing such an intelligent control system for extraction and injection fluid volumes contributes to achieving digital management, improving the efficiency of well site production, reducing the risk of blockages, and is also significant for advancing the process of digitalization and intelligence in uranium mining operations. |
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