基于Python的算法,用于计算数据库中不可用的物质组成的水性混合物的物理性质
Jina Lee1, Se-Hee Jo1, Chungyup Lee1
1CJ BIO Research Institute, CJ CheilJedang Corp., Suwon-si, Gyeonggi-do 16495, Republic of Korea.
ACS omega
|May 5, 2025
概括
一个新的Python算法预测了AspTech软件的水性物理特性,显示了密度和热容量的良好准确性,但在高度下具有粘度和导热率的限制.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 热力学是一种热力学.
背景情况:
- 准确预测水性物理性质对于化学过程设计和模拟至关重要.
- 商业软件中的现有模型,如AspenTech,可能缺乏所有相关物质的参数.
- 开源解决方案可以提高财产预测工具的灵活性和可访问性.
研究的目的:
- 开发和验证基于Python的开源算法,用于预测水体的物理性质.
- 确保与阿斯科技的电解质特性模型兼容.
- 为估计商业数据库中无法使用的参数提供替代方案.
主要方法:
- 开发了一个Python算法,利用来自UNIFAC模拟的纯组件属性预测和NRTL参数.
- 对37种二进制水性混合物的实验数据验证了算法.
- 将预测的特性 (密度,热容量,粘度,导热率) 与实验值进行比较.
主要成果:
- 实现的平均绝对百分比误差 (MAPE) 为密度为2.88%,热容量为0.355%,粘度为12.1%,导热率为10.1%.
- 确定了某些物质在高度下准确反映密度和粘度趋势的限制.
- 证明了不准确的粘度和导热率预测影响了落膜蒸发器模拟中的传热系数计算.
结论:
- 开发的算法提供了一个有价值的工具,用于估计商业数据库中不存在的模型的初始物理属性参数.
- 建议在使用高度系统或粘度和导热率等属性的算法时谨慎使用,因为可能存在重大错误.
- 可能需要进一步细化,以提高特定具有挑战性的条件和属性的精度.
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