长期短期记忆神经网络用于建模动态过程和预测控制:一种基于物理学的混合方法
Krzysztof Zarzycki1, Maciej Ławryńczuk1
1Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
一个新的基于物理的混合神经网络 (PIHNN) 模型准确地模拟了聚合反应堆. 使用此PIHNN的模型预测控制 (MPC) 算法实现了卓越的控制性能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 精确建模复杂的化学过程,如聚合,对于有效的控制至关重要.
- 传统模型经常与聚合反应固有的非线性动力学作斗争.
- 像神经网络这样的数据驱动方法提供了潜力,但可能缺乏物理解释性.
研究的目的:
- 引入一种新的基于物理的混合神经网络 (PIHNN) 模型.
- 开发一个利用PIHNN的计算效率高的模型预测控制 (MPC) 算法.
- 为了验证PIHNN建模和MPC控制策略的有效性.
主要方法:
- 开发了一种PIHNN,将第一原理物理与长短期记忆 (LSTM) 神经网络相结合.
- 采用基于模糊逻辑的数据融合块来结合基于物理和数据的组件.
- 设计了一种计算效率高的MPC算法,利用PIHNN模型的能力.
主要成果:
- PIHNN模型证明了聚合反应堆的高度准确的模拟结果.
- 基于PIHNN的MPC控制器实现了卓越的控制质量.
- 混合方法成功地将物理洞察力与数据驱动学习结合在一起.
结论:
- 拟议的PIHNN为聚合过程提供了强大而准确的建模解决方案.
- 开发的MPC策略为模拟反应堆提供了有效和高效的控制.
- 这种混合方法代表了将AI应用于化学过程控制的重大进步.
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