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一种基于APSO-MPC的螺栓起重优化控制方法,用于边缘计算应用程序
Zhi Qiu1, Lei Zhang2, He Zhang2
1Southwest Petroleum University, Chengdu, Sichuan, China. yallym@163.com.
Scientific reports
|February 5, 2025
概括
适应性颗粒群集优化模型预测控制 (APSO-MPC) 通过优化活塞升降系统来增强页岩气生产. 边缘计算的实施显著改善了数据传输,并减少了服务器负载,以实现高效运行.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 在页岩气井的底孔液体加载显著降低了效率.
- 传统的式提升方法是基于时间的,并且可能是低效的.
- 目前基于模型的优化受到数据传输效率低下和由于远程部署而导致的高服务器负载的影响.
研究的目的:
- 提出一个适应性颗粒群集优化模型预测控制 (APSO-MPC) 用于活塞提升优化.
- 使用边缘计算实现APSO-MPC,以解决远程服务器部署的局限性.
- 为了提高页岩气井的效率和优化液体清除过程.
主要方法:
- 开发了一种自适应粒子集群优化 (APSO) 算法,可以动态调整惯性重量和学习因子.
- 实现了APSO-MPC,使用基于微处理器的边缘计算架构进行本地化控制.
- 对拟议的APSO-MPC系统与传统的绩效评估方法进行模拟.
主要成果:
- 与传统方法相比,APSO-MPC的天然气产量提高了18%.
- 边缘计算的实施导致数据传输效率提高了24%.
- 边缘计算减少了83%的数据包损失,并显著降低了服务器内存和计算延迟.
结论:
- 通过边缘计算实现的拟议的APSO-MPC,为页岩气井的冲压升降优化提供了一个优质的解决方案.
- 边缘计算有效地减轻了与远程优化相关的数据传输问题和服务器负担.
- 这种方法可以大大提高天然气生产效率和运营可靠性.
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