%0 Book Section %1 OpreaMihalacheEtAl16c7 %A Oprea, Mihaela %A Mihalache, Sanda Florentina %A Carbureanu, Madalina %B New Approaches in Intelligent Control %C Cham %D 2016 %E Nakamatsu, Kazumi %E Kountchev, Roumen %I Springer %K 01821 springer paper ai factory process algorithm rules knowledge processing admin %P 207--240 %R 10.1007/978-3-319-32168-4_7 %T Knowledge-Based Intelligent Process Control %V 107 %X In the last decades, the number of process control applications that use intelligent features has increased. This is mainly due to the complex and critical character of the process to be controlled. The intelligent process control systems works better than conventional control schemes in the domains of fault diagnosis (detection, cause analysis and repetitive problem recognition); complex control schemes; process and control performance monitoring and statistical process control; real time Quality Management; control system validation, startup and normal or emergency shutdown. Conventional control technologies use quantitative processing while knowledge-based integrates both qualitative and quantitative processing (having as target the increase of efficiency). This chapter presents an overview of intelligent process control techniques, from rule based systems, frame based systems (object oriented approach), hybrid systems (fuzzy logic and neural network). The focus is on expert systems and their extension, the knowledge based systems. Finally, an industrial case study is presented with conclusions to knowledge based systems limitations and challenges associated to real time implementation of the system. %& 7 %@ 978-3-319-32166-0

@incollection{OpreaMihalacheEtAl16c7, abstract = {In the last decades, the number of process control applications that use intelligent features has increased. This is mainly due to the complex and critical character of the process to be controlled. The intelligent process control systems works better than conventional control schemes in the domains of fault diagnosis (detection, cause analysis and repetitive problem recognition); complex control schemes; process and control performance monitoring and statistical process control; real time Quality Management; control system validation, startup and normal or emergency shutdown. Conventional control technologies use quantitative processing while knowledge-based integrates both qualitative and quantitative processing (having as target the increase of efficiency). This chapter presents an overview of intelligent process control techniques, from rule based systems, frame based systems (object oriented approach), hybrid systems (fuzzy logic and neural network). The focus is on expert systems and their extension, the knowledge based systems. Finally, an industrial case study is presented with conclusions to knowledge based systems limitations and challenges associated to real time implementation of the system.}, added-at = {2016-09-23T15:07:19.000+0200}, address = {Cham}, author = {Oprea, Mihaela and Mihalache, Sanda Florentina and C\v{a}rbureanu, M\v{a}d\v{a}lina}, biburl = {https://www.bibsonomy.org/bibtex/28a53b81875ff560116f0fd21cbc8c4fd/flint63}, booktitle = {New Approaches in Intelligent Control}, chapter = 7, doi = {10.1007/978-3-319-32168-4_7}, editor = {Nakamatsu, Kazumi and Kountchev, Roumen}, file = {SpringerLink:2016/OpreaMihalacheEtAl16c7.pdf:PDF;Springer Pro:https\://www.springerprofessional.de/new-approaches-in-intelligent-control/10247890:URL}, groups = {public}, interhash = {879d72d359fed50d125866351e6934b1}, intrahash = {8a53b81875ff560116f0fd21cbc8c4fd}, isbn = {978-3-319-32166-0}, issn = {1868-4394}, keywords = {01821 springer paper ai factory process algorithm rules knowledge processing admin}, pages = {207--240}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, timestamp = {2018-04-16T11:41:34.000+0200}, title = {Knowledge-Based Intelligent Process Control}, username = {flint63}, volume = 107, year = 2016 }