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以Python为基础的算法,用UNIFAC模型模拟结果来估计NRTL模型参数

Se-Hee Jo¹, Jina Lee¹, Wangyun Won²

¹CJ Blossom Park, CJ BIO Research Institute, 55, Gwanggyo-ro 42beon-gil, Yeongtong-gu, Suwon-Si, Gyeonggi-do 16495, Republic of Korea.

|February 3, 2025

概括

一个新的Python算法使用SMILES字符串估计生物化学品的非随机双液体 (NRTL) 模型参数. 这种方法可以提高生物过程模拟的准确性和效率.

科学领域:

生物化学工程生物化学工程
过程模拟过程模拟
热力学是一种热力学.

背景情况:

生物工艺模拟受到生物化学品有限的物理和化学性质数据的阻碍.
准确的热力学模型,如非随机双液体 (NRTL) 模型,对于可靠的生物过程模拟至关重要.
估计NRTL参数的现有方法可能耗时,并且对于复杂的生物化学系统可能缺乏准确性.

研究的目的:

开发基于Python的算法,用于估计生物化学品的水性二进制系统的NRTL模型参数.
为了直接从简化分子输入线输入规范 (SMILES) 字符串中实现参数估计.
为获得生物过程模拟必需的热力学数据提供更容易获得和更准确的方法.

主要方法:

分子从SMILES字符串中被分解成功能组.
活动系数是使用通用准化学功能组活动系数 (UNIFAC) 模型来计算的.
使用差异演化算法 (DEA) 和纳尔德-米德方法 (NMM) 和UNIFAC模拟结果回归NRTL参数.

主要成果:

该算法成功估计了37种常见的生物化学物质 (氨基酸,有机酸,糖) 的NRTL参数.
与Aspen Plus相比,开发的算法显示出更高的准确性,平均绝对残余的百分比明显较低 (0.05-16.69%对比0.09-326.77%).
DEA和NMM方法在NRTL参数估计方面产生了可比且高度准确的结果.

结论:

公司内部的Python算法提供了一种简单有效的方法,用于从SMILES字符串中估计NRTL参数.
该工具可及时访问准确的热力学数据,这对于推进生物过程模拟至关重要.
该算法支持过程优化,能源消耗估计和生物化学工程中的生命周期评估.

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