As part of the fourth industrial revolution, data analysis and artificial intelligence are being integrated into production processes. In addition, energy consumption and CO2 costs are becoming decisive factors in the resource-oriented management of companies. For energy intensive and hybrid production processes a simulation-based decision support system (DSS) for production planning is validated and further developed for the sand lime brick industry to support the production planning process. The integration of empirical knowledge in the energy-intensive control of steam processes, in which quality-critical product parameters are set via thermodynamically complex relationships, is still part of current research approaches. In this paper, an approach for the mapping of an energetic system behavior in the energy-intensive and hybrid production processes will be discussed using the example of calcium silicate brick (CSB) production. Possibilities for using Discrete event simulation (DES) to increase the energy efficiency of steam processes are summarized and linked to formalized empirical knowledge in artificial Intelligence (AI) approaches.