In this paper, the focus is on the formalization of expert knowledge and its use for test design and integration into different machine learning (ML) algorithms. The formalization of expert knowledge from different domains (e.g., expertise regarding plant mechanical Processes 2021, 9, 515 2 of 12 processes and knowledge about thermo-chemical and other physical mechanisms of chemical processes) is particularly challenging and reveals the uniqueness of the presented approach. Due to the lack of data, deep neuronal networks cannot always be trained, and other ML algorithms must be combined to quantify and expand expert knowledge from different domains. Figure 1 shows the continuous learning procedure and the interaction between the different parts of an AI-based hybrid model, i.e., **data** measurement, expert knowledge, and ML algorithms with special metrics for automated evaluation (autoML) [5]. With this procedure, expert knowledge can be continuously quantified, and the understanding of the plant and chemical processes can be expanded.