Hybrid Models for Efficient Control, Optimization, and Monitoring of Thermo-Chemical Processes and Plants

• Main Authors: Thomas Freudenmann, Hans-Joachim Gehrmann, Krasimir Aleksandrov, Mohanad El-Haji, Dieter Stapf
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Processes
• Subjects: numerical model; oscillating combustion; NO_x reduction; artificial intelligence (AI)
• Online Access: https://www.mdpi.com/2227-9717/9/3/515

This paper describes a procedure and an IT product that combine numerical models, expert knowledge, and data-based models through artificial intelligence (AI)-based hybrid models to enable the integrated control, optimization, and monitoring of processes and plants. The working principle of the hybrid model is demonstrated by NO_x reduction through guided oscillating combustion at the pulverized fuel boiler pilot incineration plant at the Institute for Technical Chemistry, Karlsruhe Institute of Technology. The presented example refers to coal firing, but the approach can be easily applied to any other type of nitrogen-containing solid fuel. The need for a reduction in operation and maintenance costs for biomass-fired plants is huge, especially in the frame of emission reductions and, in the case of Germany, the potential loss of funding as a result of the Renewable Energy Law (Erneuerbare-Energien-Gesetz) for plants older than 20 years. Other social aspects, such as the departure of experienced personnel may be another reason for the increasing demand for data mining and the use of artificial intelligence (AI).