基于CNN-LSTM的非线性模型预测控制器用于批量反应堆中的温度轨迹跟踪
Aishwarya Selvamurugan1, Parthiban Kunnathur Ganesan2, Shashank S Nayak3
1Computer Science Engineering, Sri Eshwar College of Engineering, Coimbatore 641202, Tamil Nadu, India.
ACS omega
|December 2, 2024
概括
本研究介绍了一种CNN-LSTM非线性模型预测控制器 (NMPC) 用于批量反应堆. 该模型优化了冷却液流量,以准确跟踪温度配置文件,提高工业过程的效率和安全性.
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
- 工业中的人工智能
背景情况:
- 批量反应器 (BR) 适用于特种化学品和食品加工,处理复杂的反应和不同的条件.
- 有效的温度控制对于优化聚合反应和确保BRs安全至关重要.
- 传统的控制方法可能会在批处理过程中扎在外热反应的复杂动力学上.
研究的目的:
- 开发和评估基于CNN-LSTM的非线性模型预测控制器 (NMPC),用于批量反应堆中精确的温度配置跟踪.
- 优化冷却液流量,以管理热外反应并提高控制性能.
- 为了提高计算效率,使用带有Sigmoidal权重函数的启发式方法来提高计算效率.
主要方法:
- 使用混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络进行预测建模.
- 使用批量反应堆的开放循环实验数据训练了CNN-LSTM模型.
- 实现了一个非线性模型预测控制器 (NMPC),集成CNN-LSTM模型和启发式优化方法.
主要成果:
- 基于CNN-LSTM的NMPC展示了准确的温度配置跟踪能力.
- 控制器有效地优化了冷却液流量,以管理外热反应热量.
- 启发式方法提高了NMPC模型的计算效率.
结论:
- 开发的基于CNN-LSTM的NMPC为批量反应堆温度控制提供了强大而准确的解决方案.
- 这种方法显示出大规模工业应用的巨大潜力,特别是在制药行业.
- 实施可以提高工艺效率,降低能源消耗,提高运营安全.
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