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

Freudenmann, Thomas; Gehrmann, Hans-Joachim; Aleksandrov, Krasimir; El-Haji, Mohanad; Stapf, Dieter

Abstract (englisch):
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 NOx 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).

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Verlagsausgabe §
DOI: 10.5445/IR/1000130546
Veröffentlicht am 16.03.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Chemie (ITC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2227-9717
KITopen-ID: 1000130546
HGF-Programm 38.05.01 (POF IV, LK 01) Anthropogenic Carbon Cycle
Erschienen in Processes
Verlag MDPI
Band 9
Heft 3
Seiten Article no: 515
Vorab online veröffentlicht am 12.03.2021
Schlagwörter numerical model; oscillating combustion; NOx reduction; artificial intelligence (AI)
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