Process monitoring in chemical plants using neural networks

The suitability of pattern recognition for process monitoring of chemical plants is discussed. Experiments in a miniplant, a pilot plant and simulation studies are carried out. While selecting the required test series of process variables when giving training for neural networks, one tries to use generalized forms of description to illustrate the system in question. It is therefore possible to combine data records originating from various sources. Thus, on the one hand, non-conforming operating conditions have to be simulated in a laboratory or technical system. On the other hand, simulation results might also be used to provide training on neural nets. This combination in the utilized data material permits you to dispense with preparing new physical-chemical models for each data-driven model. The prepared tool is subsequently used as a prototype for hydrogenation in a production system.