一种数据驱动的状态识别方法,用于智能控制联合站出口系统.
Guangli Xu1,2, Yifu Wang1, Zhihao Zhou1
1School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu, 610500, Sichuan, China.
Scientific reports
|January 22, 2025
概括
这项研究引入了一个优化的逆向传播神经网络 (PSO-GWO-BP),用于预测联合站系统中的压力下降. 先进的模型准确地识别异常条件,使智能控制和自适应操作调整成为可能.
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:
- 联合站的智能控制需要准确的系统状态识别和适应性操作调整.
- 目前用于识别异常条件和优化操作的方法往往缺乏精度和适应性.
研究的目的:
- 为联合站出口系统开发一个准确的压力下降预测模型.
- 建立一种智能方法,使用动态值来识别异常的工作条件.
- 增强联合站出口系统的自适应控制能力.
主要方法:
- 将粒子优化 (PSO) 和灰狼优化器 (GWO) 结合在一起的混合优化算法被用于优化反向传播神经网络 (BP) 模型,创建 PSO-GWO-BP 模型.
- 使用PSO-GWO-BP方法建立了一个压力下降预测模型.
- 开发了一种基于动态值的状态识别方法,利用PSO-GWO-BP压力下降预测模型.
主要成果:
- 与传统的水力计算修改 (THCM) 模型和其他机器学习算法相比,PSO-GWO-BP模型显示出更高的预测准确性.
- 拟议的动态值方法成功地发现了联合站的异常工作条件.
- 开发方法的有效性和准确性使用生产和运营数据进行了验证.
结论:
- PSO-GWO-BP模型在联合站出口系统的压力下降预测准确度方面提供了显著的优势.
- 基于动态值的状态识别方法可以智能识别系统运行状态.
- 这种方法提高了检测异常条件的能力,并适应性地调整了操作方案,提高了系统的整体智能.
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