人工智能增强的模型预测控制,通过实验的综合计算建模设计和多变量回归来优化液化天然气回收
Basma Abd El Hakim1, Mahmoud Abdel-Halim Abdel-Goad2, M E Awad3
1Chemical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt. engbasma_khairy@mu.edu.eg.
Scientific reports
|August 10, 2025
概括
这项研究通过使用先进的控制策略来增强液化石油气 (LPG) 的回收. 人工智能增强的模型预测控制 (AI增强的MPC) 显著提高了LPG回收和初始化器柱中的能源效率.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程控制 过程控制
- 人工智能的人工智能
背景情况:
- 由于运营复杂性,初级化器柱在优化液化石油气 (LPG) 回收方面面临着挑战.
- 传统的控制方法往往无法在动态条件下保持峰值效率和稳定性.
研究的目的:
- 开发和评估一个综合框架,将响应表面方法 (RSM) 与模型预测控制 (MPC) 结合起来,以加强液化天然气回收.
- 为了比较传统的MPC与人工智能增强的MPC (AI增强的MPC) 在一个debutanizer列模拟中的性能.
主要方法:
- 使用Aspen HYSYS进行了稳定状态和动态模拟.
- 响应表面方法 (RSM) 从稳定状态模拟中分析了关键过程变量.
- 在MATLAB中开发了一个回归决策树模型,以支持AI增强的MPC实现.
主要成果:
- MPC将液化天然气回收率从99.73%提高到99.85%,并降低了能源消耗.
- 人工智能增强的MPC进一步提高了回收率至99.9%,并实现了更大程度的降低反炉和冷凝器的使用费用.
- 这两种控制系统都有效地管理了料杆流的影响,提高了热稳定性和产品质量.
结论:
- 将RSM与MPC和AI增强的MPC集成的混合框架为优化液化天然气回收提供了一种新的方法.
- 人工智能增强的MPC在首次启动操作中,在提高能源效率,产品质量和动态稳定性方面展示了卓越的性能.
- 拟议的方法为工业液化天然气回收增强提供了强大的解决方案.
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