Intelligent Parameter Adaptation for Chemical Processes

Simple item page
dc.contributor.authorSozio, John Charlesen
dc.contributor.committeechairVanLandingham, Hugh F.en
dc.contributor.committeememberRony, Peter R.en
dc.contributor.committeememberBay, John S.en
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2014-03-14T20:41:44Zen
dc.date.adate1999-07-23en
dc.date.available2014-03-14T20:41:44Zen
dc.date.issued1999-07-08en
dc.date.rdate2000-07-23en
dc.date.sdate1999-07-20en
dc.description.abstractReducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of "survival of the fittest". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-072099-122832en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-072099-122832/en
dc.identifier.urihttp://hdl.handle.net/10919/34089en
dc.publisherVirginia Techen
dc.relation.haspartetd.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTennessee Eastmanen
dc.subjectdecentralized process controlen
dc.subjectgenetic algorithmen
dc.subjectfuzzy logicen
dc.titleIntelligent Parameter Adaptation for Chemical Processesen
dc.typeThesisen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
etd.pdf
Size:
481.39 KB
Format:
Adobe Portable Document Format
Download

Collections

Masters Theses