Coordination Funds

该研究单元旨在建立化学过程工程中的深度学习方法。我们研究的中心假设是，深度学习在对化学过程工程至关重要的领域开辟了以前从未探索过的途径，如异常检测、状态预测、决策和自主过程。到目前为止，这种方法的发展一直受到阻碍，因为来自化工厂的数据通常很稀少，更糟糕的是，公开文献中无法获得这些数据。因此，该研究单位将(对连续和不连续的化学过程)进行专门的实验，以产生所需的大型数据集。由于这些实验既耗时又昂贵，数据扩充是必不可少的，我们将通过基于学习和基于知识的方法相结合，包括过程模拟来实现这一点。新的算法和数据将与研究单位创造的知识一起公开。在其第一个供资期间，研究股将侧重于应用深度学习来检测化学过程中的异常，这是一个异常检测至关重要的领域，例如，对于减灾和环境保护而言。除了探测，我们还考虑了异常的探测和解释，以及探测器的验证。该研究单位开发的时间序列深部异常检测新方法不仅在化工领域有应用价值，而且在其他许多领域也有应用价值。该研究单位建立在德州大学凯泽斯劳滕大学最近建立的独特结构之上，任命了一批初级教授，一名是计算机科学，一名是化学工程，我们希望建立一个长期的合作关系。

This research unit aims to establish deep-learning methods in chemical process engineering. The central hypothesis of our research is that deep learning opens up previously unexplored avenues in areas critical to chemical process engineering, such as anomaly detection, state prediction, decision making, and autonomous processes. So far, the development of such methods has been hampered because data from chemical plants is generally sparse and, to make matters worse, unavailable in the open literature. This research unit will therefore conduct dedicated experiments (on both continuous and discontinuous chemical processes) to generate the required large datasets. As those experiments are time-consuming and costly, data augmentation is essential, which we will accomplish through a combination of learning-based and knowledge-based methods, including process simulation. The new algorithms and data will be made publicly available, along with the knowledge created by the research unit. During its first funding period, the research unit will focus on the application of deep learning to detect anomalies in chemical processes, a field where anomaly detection is of paramount importance, e.g., for hazard mitigation and environmental protection. Besides detection, we also consider the exploration and explanation of anomalies, as well as the verification of detectors. The novel methods for deep anomaly detection on time series developed by the research unit will be not only useful in chemical engineering but also in many other fields. The research unit builds on a unique structure that has recently been established at TU Kaiserslautern by the appointment of a tandem of junior professors, one in computer science and one in chemical engineering, a collaboration that we want to establish on a long-term basis.