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基于强化学习的非线性模型预测控制器,用于外套反应堆:使用Jetson Orin的机器学习概念验证
Aishwarya Selvamurugan1, Aromal Vinod Kumar2, Hrishikesh R Palan2
1Department of Computer Science Engineering, Sri Eshwar College of Engineering, Coimbatore 641202, Tamil Nadu, India.
本研究介绍了一种机器学习和非线性模型预测控制 (NMPC) 框架,使用演员关键强化学习 (A2CRL) 进行精确的批量反应堆温度控制,提高安全性和效率.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 批量反应器 (BR) 在制药和特种化学品中至关重要,但管理外热反应和防止热失控仍然具有挑战性.
- 现有的控制方法与批量工艺固有的复杂动态和多样化的操作条件作斗争.
研究的目的:
- 开发和实验验证一个集成的机器学习和非线性模型预测控制 (NMPC) 框架,用于批量反应堆中准确的温度跟踪.
- 通过智能控制来提高工艺安全,效率和降低能源消耗.
主要方法:
- 利用一次性神经网络 (RNN) 进行实验室规模批量反应堆数据的开放循环建模.
- 在NMPC框架内实施了关键行为体强化学习 (A2CRL) 方法论,用于动态重量更新.
- 动态优化冷却液流量,以确保精确的温度调节和稳定性.
主要成果:
- 与现有的深度学习NMPC方法相比,A2CRL增强的NMPC框架证明了控制器性能的提高.
- 实现了精确的温度调节,提高了工艺效率,降低了能源消耗.
- 验证了该框架对工业规模批量反应堆系统的潜力,提高了运行安全.
结论:
- 拟议的A2CRL-NMPC方法为管理复杂的批量反应堆动态提供了强大的解决方案.
- 这种方法平衡了对工业应用的预测准确性和实时计算效率.
- 成功的实验验证强调了其在化学加工中显著提高安全性和减少能源消耗的潜力.
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