Coordination Funds

本研究单位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，深度学习在化学过程工程的关键领域开辟了以前未探索的途径，例如异常检测、状态预测、决策制定和自主过程。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少，更糟糕的是，在公开文献中无法获得。因此，该研究单位将进行专门的实验（在连续和不连续的化学过程中），以生成所需的大型数据集。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来完成，包括过程模拟。新的算法和数据将与研究部门创造的知识一起公开。在第一个资助期内，该研究单位将专注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，例如减轻危害和保护环境。除了探测之外，我们还考虑了异常的探测和解释，以及探测器的验证。本课题组所开发的时间序列深度异常检测新方法，不仅在化工领域，而且在许多其他领域都有应用价值。这个研究单位建立在凯泽斯劳滕工业大学最近建立的一个独特的结构上，通过任命两位初级教授，一位是计算机科学教授，一位是化学工程教授，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.