Data Generation and Knowledge-based Augmentation: Batch Distillation

数据生成和基于知识的增强：批量蒸馏

• 批准号: 498964862
• 负责人: Professor Dr. Michael Bortz
• 金额: --
• 依托单位: Lehrstuhl für Thermodynamik (LTD)
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498964862?language=en
• 关键词: Data; Generation; Knowledge; based; Augmentation

Batch distillation is one of the most important processes in the chemical industry. Nevertheless, experimental data on the operation of batch distillation plants that could be used to develop and train machine learning methods is lacking in the open literature. Therefore, such data will be generated in the present project in a laboratory scale batch distillation column, which is equipped with advanced sensors, including an online NMR spectrometer and cameras. Additional data will be generated by simulations of batch distillation processes based on a dynamic physical model. Cases with different anomalies as well as anomaly-free cases will be studied with both methods for a wide range of operating strategies and conditions; and separations of many different fluid mixtures will be investigated, including poorly specified mixtures. The generated data will also comprise information on the uncertainties. Methods from the field of design of experiments will be used to plan the laboratory distillations as well as the simulations. The full data sets will be made publicly available. The relations between the experimental data and the simulation data will be considered in detail and the two types of data will be merged to hybrid data sets. This project will also provide the physical knowledge on batch distillation processes needed in the projects of Research Area A of this Research Unit. Our ambition is to supply the complex and heterogeneous data generated in the project in a way that is optimal for anomaly detection in chemical processes. In the project, a new holistic approach to batch distillation will be explored that could be fruitful far beyond anomaly detection.

间歇精馏是化工过程中最重要的过程之一。然而，在公开文献中缺乏可用于开发和训练机器学习方法的间歇蒸馏装置操作的实验数据。因此，这些数据将在本项目中在实验室规模的间歇蒸馏塔中产生，该塔配备有先进的传感器，包括在线NMR光谱仪和相机。将通过基于动态物理模型的间歇蒸馏过程的模拟产生额外的数据。具有不同异常的情况下，以及无异常的情况下，将研究与这两种方法的广泛的操作策略和条件;和许多不同的流体混合物的分离将进行调查，包括指定的混合物。生成的数据还将包括关于不确定性的信息。实验设计领域的方法将用于计划实验室蒸馏以及模拟。完整的数据集将向公众提供。将详细考虑实验数据和模拟数据之间的关系，并将两种类型的数据合并为混合数据集。本项目还将提供本研究单元研究领域A项目所需的间歇蒸馏过程的物理知识。我们的目标是以最适合化学过程异常检测的方式提供项目中生成的复杂和异构数据。在该项目中，将探索一种新的间歇蒸馏整体方法，其成果可能远远超出异常检测。