Machine Learning and Optimal Experimental Design for Thermodynamic Property Modeling

热力学性质建模的机器学习和优化实验设计

• 批准号: 466528284
• 负责人: Professor Dr. Roland Herzog
• 金额: --
• 依托单位: Interdisziplinäres Zentrum für Wissenschaftliches Rechnen (IWR)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/466528284?language=en
• 关键词: Machine; Learning; Optimal; Experimental; Design

For many tasks in chemical and energy engineering, the accurate knowledge of thermodynamic properties (e.g., pressure and temperature with density and speed of sound) and the phase behavior of the involved fluids plays a key role. In science, such properties are required for the basic understanding of chemical-physical behavior and for the development of predictive models. For industry, thermodynamic properties are the basis for the design of safe and sustainable processes and machinery. However, the quality of property calculations using equations of state (EOS) depends largely on the availability and accuracy of experimental data. Measurements of such data are often carried out within the frame of a dense grid of measurement points, which delivers a comprehensive data set. Nevertheless, with the aim to develop an accurate EOS, this approach is time-consuming, while it is unclear whether all data are ultimately substantial to the model development. As a result, the required time and financial expenditure makes the generation of reliable models rather limited. Considering this, it is highly desirable to significantly reduce the model development time by limiting the amount of experimental data to the required extent and to involve functional forms, which enable short computing times for the application in process simulation. Therefore, the major goal of the research project is to tackle the aforementioned issues by realizing a specific interplay between (1) interpretable machine learning (ML) to find the ideal functional form of the EOS, (2) optimal experimental design to find the most appropriate measurement points and (3) the actual experiment. A potential workflow can be imagined as follows: Starting from initial thermodynamic property measurements, ML-based EOS modeling is used to create a first functional form. This form is used to predict the next most informative measurements, which can then be used as input for further EOS modeling. When to terminate this workflow is inherent part of the project's research schedule. One important output of the project is an in-situ software tool for thermodynamic measurement planning and model development, which considers the measurement effort, model accuracy and interpretability.

对于化学和能源工程中的许多任务，准确了解热力学性质（例如压力和温度、密度和声速）以及所涉及流体的相行为起着关键作用。在科学中，这些特性对于化学物理行为的基本理解和预测模型的开发是必需的。对于工业而言，热力学特性是设计安全且可持续的工艺和机械的基础。然而，使用状态方程 (EOS) 进行性能计算的质量在很大程度上取决于实验数据的可用性和准确性。此类数据的测量通常在密集的测量点网格框架内进行，从而提供全面的数据集。然而，以开发准确的 EOS 为目标，这种方法非常耗时，而且尚不清楚所有数据最终是否对模型开发具有实质性意义。因此，所需的时间和财务支出使得可靠模型的生成相当有限。考虑到这一点，非常希望通过将实验数据量限制在所需的范围内并涉及函数形式来显着减少模型开发时间，从而缩短过程模拟应用程序的计算时间。因此，该研究项目的主要目标是通过实现以下之间的特定相互作用来解决上述问题：（1）可解释机器学习（ML）以找到 EOS 的理想函数形式，（2）最佳实验设计以找到最适当的测量点和（3）实际实验。一个潜在的工作流程可以想象如下：从最初的热力学性质测量开始，基于机器学习的 EOS 建模用于创建第一个函数形式。该形式用于预测下一个信息最丰富的测量结果，然后可以将其用作进一步 EOS 建模的输入。何时终止此工作流程是项目研究计划的固有部分。该项目的一项重要成果是用于热力学测量规划和模型开发的现场软件工具，该工具考虑了测量工作量、模型准确性和可解释性。