

BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Computational Optimisation Group - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Computational Optimisation Group
X-ORIGINAL-URL:https://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20180325T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20181028T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20190331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20191027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20200329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20201025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190425T110000
DTEND;TZID=Europe/London:20190425T120000
DTSTAMP:20260404T022226
CREATED:20190416T132348Z
LAST-MODIFIED:20190416T132348Z
UID:1226-1556190000-1556193600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Data-Driven Methods for Integrated Production Scheduling and Process Control
DESCRIPTION:Title: Data-Driven Methods for Integrated Production Scheduling and Process Control\nSpeaker: Calvin Tsay\nAffiliation: McKetta Dept of Chemical Engineering\, University of Texas at Austin\nLocation: 218 Huxley Building\nTime: 11:00 – 12:00 \nAbstract. Due to the fast-changing market conditions enabled by globalization and modern infrastructures\, industrial production scheduling is often performed over relatively short time intervals to maximize profits. For chemical processes\, plant dynamics and control become highly relevant at these shortened time intervals\, and careful attention is required to ensure computed schedules are feasible when implemented in the physical process. With this motivation\, many recent works focused on integrating dynamic information from the process control layer into production scheduling. Unfortunately\, the resulting optimization problems are high-dimensional and often intractable because of the broad range of time scales involved. \nIn this presentation\, we describe data-driven techniques for learning low-order dynamic models of the behavior of a process and its controller (i.e.\, “closed-loop” behavior). Then\, we formulate an optimal scheduling problem involving the learned\, reduced-order representation of the relevant dynamics. Furthermore\, we present a data-mining approach that exploits historical process data to reduce the dimensionality of the aforementioned dynamic models. Throughout the presentation\, we will focus on applications to the scheduling of industrial air separation units\, which consume immense amounts of electricity\, in response to time-varying electricity prices (an operational strategy termed “demand response”). Several case studies will be presented\, ranging from model-based analyses to a real-world application on an industrial air separation unit.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-data-driven-methods-for-integrated-production-scheduling-and-process-control/
END:VEVENT
END:VCALENDAR