实时优化双螺丝颗粒化工艺的能源效率,使用长短期存储器 (LSTM) 网络
Chaitanya Sampat1, Rohit Ramachandran1
1Rutgers-The State University of New Jersey, 98 Brett Road, Piscataway, New Jersey 08854, United States.
概括
这项研究利用长期记忆 (LSTM) 模型和优化算法优化了双螺丝颗粒化 (TSG) 能效. 该工艺实现了27%的能源效率提高,同时保持了颗粒产量.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 化学工程是化学工程的重要组成部分.
- 过程优化 过程优化
背景情况:
- 固体口服剂型的传统制药制造往往是低效的,产生大量的浪费.
- 越来越需要节能制造工艺,符合碳中和目标.
- 在流程优化过程中保持关键质量属性是必不可少的.
研究的目的:
- 为了最大限度地提高双螺丝颗粒化 (TSG) 工艺的能源效率.
- 为了验证LSTM模型与TSG的优化算法相结合的有效性.
- 为了证明在不影响颗粒产量的情况下提高能源效率.
主要方法:
- 使用长期记忆 (LSTM) 模型训练了从TSG运行的时间序列处理数据.
- 采用随机优化算法,在定义的约束下最大限度地提高能源效率.
- 以优化的参数进行实验运行,以验证模型的预测.
主要成果:
- 在经过测试的优化场景中实现了最大27%的能源效率提高.
- 在整个双螺杆颗粒加工过程中成功保持颗粒的产量.
- 证明了结合LSTM和优化方法的能力,以提高TSG的能源效率.
结论:
- 集成LSTM模型和优化算法为提高制药制造能效提供了一个强大的策略.
- 优化TSG参数可以显著降低能源消耗,同时保持产品质量.
- 这种方法支持该行业向可持续发展和碳中和制药生产的努力.
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