Abstract
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).
Highlights
To master the constantly growing amount of information from sensors and measurement systems, intelligent evaluation and optimization tools are playing an increasingly important role in the field of thermo-chemical processes to reduce costs and improve efficiency
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
This allows expert knowledge to be captured in semantically networked categories in a way that is understood by the computer, which is an essential prerequisite for the meaningful clustering and calibration of AI algorithms
Summary
To master the constantly growing amount of information from sensors and measurement systems, intelligent evaluation and optimization tools are playing an increasingly important role in the field of thermo-chemical processes to reduce costs and improve efficiency. The combination of a data-driven (i.e., empirical) approach with expert knowledge of the plant and of thermo-chemical and physical principles could be a solution to better understand the behavior of the plant and thermo-chemical processes. This approach is referred to as the artificial intelligence (AI)-based hybrid model. The focus is on the formalization of expert knowledge and its use for test design and integration into different machine learning (ML) algorithms. The focus is on the formalization of expert knowledge and its u2soef f1o2r test design and integration into different machine learning (ML) algorithms. AAlolonnggwwitithhaacclolouudd--bbaasseeddIITTssooluluttioionnffrroommEEnngginineeeerrininggDDaattaaIInntteelllilgigeennccee((EEDDIILLttdd.,., PPfifninzzttaal-l-BBeerrgghhaauusseenn,,GGeerrmmaannyy))ccaalllleeddtthhee““EEDDIIhhiivveeIInntteerrnneettoofftthhiinnggss((IIooTT))FFrraammeewwoorrkk”” (s(shhoorrttEEDDIIhhivivee),),ththeepprroocceessssooffccaapptuturrininggeexxppeerrttkknnoowwleleddggeeaannddththeeininteteggrraatitoionnooffpprroocceessss eenngginineeeerrininggffuunnddaammeennttaalsls,,AAII--bbaasseeddmmooddeelilningg,,aannddtthheessuubbsseeqquueennttaapppplliiccaattiioonnoofftthheeAAII-bbaasseeddhyhbyrbidridmomdeoldienlpionwperopwlaenr tpolpaenrtatoiopnercaatniobne accahnievbeed.aIcnhitehvisedpa. pIenr, tthhiesadpvaapnetra, gtehse oafdAvIa-bnatasgedeshyobfriAdIm-boadseedls ihnyibnrdidustmriaoldaeplspliincatiinodnus satrreiadlisacpupssliecda,titohnesexapreeridmiescnutaslsreeds,ultthse aerxepperreimseenntteadl rteosgueltths earrewpirtehsethnetedmtoodgeeltihnegr rweistuhlttsh,eamnoddtehleincgornetsiunlutso,uasndsotfhtewcaornet-ibnausoeuds ssuopftpworatreo-fbtahsiesdpsroucpepsosrtthorof uthgihs pErDoIchesivsethisroduegmhoEnDstIrahtievde.is demonstrated
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