Advanced autonomous model-based operation of industrial process systems (Autoprofit): Technological developments and future perspectives

Advanced autonomous model-based operation of industrial process systems (Autoprofit) : Technological developments and future perspectives

QC 20170126
Model-based operation support technology such as Model Predictive Control (MPC) is a proven and accepted technology for multivariable and constrained large scale control problems in process industry. Despite the growing number of successful implementations, the low level of operational efficiency of MPC is an existing problem, specifically the lack of advanced maintenance technology. To this end, within the EU FP 7 program, a project (Autoprofit (1)) has been executed to advance the level of autonomy and automated maintenance of MPC technology. Taking linear model-based technology as a starting point, in the project a philosophy has been developed for autonomous performance monitoring, diagnosis, experiment design, model adaptation and controller re-tuning, that is driven by economic criteria in each step, working towards an operation support system in which effective maintenance and adaptation of MPC controllers becomes feasible. In this development, challenging research questions have been addressed in the areas of on-line performance monitoring and diagnosis, least costly experiment design, automated adaptation of models, and auto-tuning, and new fundamental techniques have been developed. Although a full fledge and industrially proven (semi-)automated system is not yet realised, parts of the on-line system have been implemented and validated on real life cases provided by the industrial partners, showing that the formulated objectives are within reach.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
Model based operation support system
Autonomous maintenance
Performance diagnosis
Hypothesis testing
System identification
Experiment design
Randomized algorithms
(Auto)Tuning
Controller matching
Extremum seeking

By the author/editor: Özkan, Leyla; Bombois, Xavier; Ludlage, Jobert ...; Rojas, Cristian ...; Hjalmarsson, Håk ...; Modén, Per Erik; show more...; Lundh, Michael; Backx, Ton C. P. ...; Van den Hof, Pau ...; show less...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Control Engineer ...

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.