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，对热力学特性的准确知识（例如，具有密度和声音速度的压力和温度）以及相关流体的相行为起着关键作用。在科学中，这种特性是对化学物理行为的基本理解和预测模型的发展所必需的。对于行业而言，热力学特性是设计安全和可持续过程和机械的基础。但是，使用状态方程（EO）的财产计算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是在密集的测量点网格框架内进行的，该框架可提供全面的数据集。然而，为了开发准确的EOS，这种方法是耗时的，而目前尚不清楚所有数据最终是否对模型开发都很重要。结果，所需的时间和财务支出使生成可靠的模型相当有限。考虑到这一点，高度希望通过在所需的范围内限制实验数据的数量并涉及功能形式，从而显着减少模型开发时间，这使得在过程模拟中为应用程序提供了简短的计算时间。因此，研究项目的主要目标是通过实现（1）可解释的机器学习（ML）之间的特定相互作用来解决上述问题，以找到EOS的理想功能形式，（2）最佳实验设计，以找到最合适的测量点和（3）实际实验。可以想象的潜在工作流量如下：从初始热力学性质测量开始，基于ML的EOS建模用于创建第一功能形式。该表格用于预测下一个最有用的测量结果，然后可以用作进一步的EOS建模的输入。何时终止此工作流程是项目研究时间表的固有部分。该项目的一个重要输出是用于热力学测量计划和模型开发的原位软件工具，该工具考虑了测量工作，模型的准确性和解释性。