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基于强化学习的发酵生物反应器的温度控制用于乙醇生产
Nagabhushanamgari Rajasekhar1, Thota Karunakaran Radhakrishnan1, Samsudeen Naina Mohamed1
1Department of Chemical Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu, India.
Biotechnology and bioengineering
|June 28, 2024
概括
强化学习,特别是TD3算法,有效地控制了乙醇生物反应器的温度,在最小化错误和处理干扰方面超过了DDPG,以实现高效的能源生产.
科学领域:
- 生物技术是生物技术.
- 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
背景情况:
- 乙醇生产是能源生产的关键工业生物工艺.
- 生物反应器的先进控制系统经常与模型和过程不匹配而扎.
- 强化学习 (RL) 提供了一种数据驱动的方法,可以直接从环境中学习控制政策.
研究的目的:
- 实施和评估用于控制乙醇生物反应器温度的双延迟深确定性政策梯度 (TD3) 算法.
- 将TD3的性能与深度决定性政策梯度 (DDPG) 算法进行比较.
- 在不受约束和受约束的条件下评估控制策略.
主要方法:
- 这项研究使用了一种RL类型的TD3算法来控制非线性生物反应器模型中的反应器温度.
- 用TD3算法测试了各种奖励函数,包括积分绝对误差 (IAE).
- 使用平均平方误差 (MSE) 评估性能,并与DDPG算法进行比较.
主要成果:
- 有IAE奖励的TD3控制器在不受约束的控制中获得了0.22的较低的MSE,而DPG的0.29.
- 在受约束的控制中,TD3的MSE值为0.38,超过DDPG的0.48.
- TD3剂证明了对流速和温度变化的有效排斥干扰.
结论:
- TD3算法为乙醇生产中的生物反应器温度控制提供了强大的和有效的解决方案.
- 基于 TD3 的控制器比 DDPG 提供了更高的性能,特别是在最小化错误和适应过程变化方面.
- 这种RL方法提高了工业生物工艺中的工艺稳定性和效率.
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