With the EDI hive IoT framework, an efficient test design and evaluation of the **data** guaranteed that all influencing parameters and the interactions between them were presented and evaluated. In this case, the AI-based hybrid model was used to examine the reduction potential of oscillating the coal mass flow to reduce NO x emissions. The campaign consisted of a screening trial, where the **data** were used to identify relevant influencing parameters. Based on these results, a second trial was initiated to further investigate the optimal test settings and the reproducibility of the test setup. The formalized expert knowledge, measurement data, and the decisions for all iterations were captured sustainably through the AI-based hybrid model procedure, which is supported by the EDI hive. With the statistical planning of the trial and the use of statistical analysis algorithms, it was possible to identify significant influences, such as the best oscillation frequency. In this case, it was found to be 1.8 Hz for coal oscillation to reduce the NO x concentration to 380 mg/Nm 3 .