Final theses

The efficient searching and identification of links in specialized document databases is essential in order to make relevant information quickly accessible to users. This thesis compares different strategies for the development of a RAG (retrieval-augmented generation) agent system which should make this possible. Among other things, the exact composition of the system of generalist and specialist agents and possibilities to effectively generate keywords are to be examined and evaluated with regard to their usefulness. A further aim is to use the system to link relationships between different specialist concepts across different documents in order to provide specialist users with targeted support for their queries.

In chemical batch processes, a chemical product is produced by continuously combining starting materials. The chemical reactions are dependent on unknown environmental influences that cannot be controlled in the laboratory. Despite these uncertainties in the manufacturing process, the aim is to achieve an optimum result, e.g. low number of by-products, avoidance of overheating, minimum time required, etc., in order to ensure the economic efficiency of production.

The aim of this master’s thesis is to use modern machine learning methods to make predictions during the running process about different metrics of the further course of the process (e.g. quality, time requirement, temperature, etc.). The resulting prediction models then form the basis of an agent-based control system that allows the production process to be optimally controlled.

In many industrial processes, time-delayed and variational dependencies, such as those caused by different flow rates or intermediate storage of materials along the production line, lead to challenges in data analysis and predictive modeling. Although theoretical studies indicate that such variations can negatively influence the prediction performance, the question remains whether the prediction performance is sufficient under real conditions. This thesis aims to close this knowledge gap by validating the theoretical findings through practical investigations using real sensor data along the production line. Suitable ML algorithms are to be sought, implemented and their performance evaluated in order to effectively meet the challenges of time-delayed dependencies.

Synthetic data are artificially generated imitations of sensitive data and allow users to train AI and ML models without accessing real, in-house data. In recent years, research into generating this data has become very popular. However, it is unclear to what extent companies in Germany are utilizing the potential of synthetic data and what the challenges are that stand in the way of a broad application of this technology. With this thesis we want to contribute to closing this gap between research and industry by discussing the requirements that companies have for synthetic data.