Thesis (MEng)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: The growth in intelligent control has primarily been a reaction to the realisation that
nonlinear control theory has been unable to provide practical solutions to present day
control challenges. Consequently the chemical industry may be cited for numerous
instances of overdesign, which result as an attempt to avoiding operation near or
within complex (often more economically viable) operating regimes. Within these
complex operating regimes robust control system performance may prove difficult to
achieve using conventional (algorithmic) control methodologies.
Biological neuronal control mechanisms demonstrate a remarkable ability to make
accurate generalisations from sparse environmental information. Neural networks,
with their ability to learn and their inherent massive parallel processing ability,
introduce numerous opportunities for developing superior control structures for
complex nonlinear systems. To facilitate neural network learning, reinforcement
learning techniques provide a framework which allows for learning from direct
interactions with a dynamic environment. lts promise as a means of automating the
knowledge acquisition process is beguiling, as it provides a means of developing
control strategies from cause and effect (reward and punishment) interaction
information, without needing to specify how the goal is to be achieved.
This study aims to establish evolutionary reinforcement learning as a powerful tool
for developing robust neurocontrollers for application in highly nonlinear process
systems. A novel evolutionary algorithm; Symbiotic, Adaptive Neuro-Evolution
(SANE), is utilised to facilitate neurocontroller development. This study also aims to
introduce SANE as a means of integrating the process design and process control
development functions, to obtain a single comprehensive calculation step for
maximum economic benefit. This approach thus provides a tool with which to limit
the occurrence of overdesign in the process industry. To investigate the feasibility of evolutionary reinforcement learning in achieving
these aims, the SANE algorithm is implemented in an event-driven software
environment (developed in Delphi 4.0), which may be applied for both simulation and
real world control problems. Four highly nonlinear reactor arrangements are
considered in simulation studies. As a real world application, a novel batch distillation
pilot plant, a Multi-Effect Batch Distillation (MEBAD) column, was constructed and
commissioned.
The neurocontrollers developed using SANE in the complex simulation studies, were
found to exhibit excellent robustness and generalisation capabilities. In comparison
with model predictive control implementations, the neurocontrollers proved far less
sensitive to model parameter uncertainties, removing the need for model mismatch
compensation to eliminate steady state off-set. The SANE algorithm also proved
highly effective in discovering the operating region of greatest economic return, while
simultaneously developing a neurocontroller for this optimal operating point. SANE,
however, demonstrated limited success in learning an effective control policy for the
MEBAD pilot plant (poor generalisation), possibly due to limiting the algorithm's
search to a too small region of the state space and the disruptive effects of sensor
noise on the evaluation process.
For industrial applications, starting the evolutionary process from a random initial
genetic algorithm population may prove too costly in terms of time and financial
considerations. Pretraining the genetic algorithm population on approximate
simulation models of the real process, may result in an acceptable search duration for
the optimal control policy. The application of this neurocontrol development approach
from a plantwide perspective should also have significant benefits, as individual
controller interactions are so doing implicitly eliminated. / AFRIKAANSE OPSOMMING: The huidige groei in intelligente beheerstelsels is primêr 'n reaksie op die besef dat
nie-liniêre beheerstelsel teorie nie instaat is daartoe om praktiese oplossings te bied
vir huidige beheer kwelkwessies nie. Gevolglik kan talle insidente van oorontwerp in
die chemiese nywerhede aangevoer word, wat voortvloei uit 'n poging om bedryf in of
naby komplekse bedryfsgebiede (dikwels meer ekonomies vatbaar) te vermy. Die
ontwikkeling van robuuste beheerstelsels, met konvensionele (algoritmiese )
beheertegnieke, in die komplekse bedryfsgebiede mag problematies wees.
Biologiese neurobeheer megamsmes vertoon 'n merkwaardige vermoë om te
veralgemeen vanaf yl omgewingsdata. Neurale netwerke, met hulle vermoë om te leer
en hulle inherente paralleie verwerkingsvermoë, bied talle geleenthede vir die
ontwikkeling van meer doeltreffende beheerstelsels vir gebruik in komplekse nieliniêre
sisteme. Versterkingsleer bied a raamwerk waarbinne 'n neurale netwerk leer
deur direkte interaksie met 'n dinamiese omgewing. Versterkingsleer hou belofte in
vir die inwin van kennis, deur die ontwikkeling van beheerstrategieë vanaf aksie en
reaksie (loon en straf) interaksies - sonder om te spesifiseer hoe die taak voltooi moet
word.
Hierdie studie beaam om evolutionêre versterkingsleer as 'n kragtige strategie vir die
ontwikkeling van robuuste neurobeheerders in nie-liniêre prosesomgewings, te vestig.
'n Nuwe evolutionêre algoritme; Simbiotiese, Aanpasbare, Neuro-Evolusie (SANE),
word aangewend vir die onwikkeling van die neurobeheerders. Hierdie studie beoog
ook die daarstelling van SANE as 'n weg om prosesontwerp en prosesbeheer
ontwikkeling vir maksimale ekonomiese uitkering, te integreer. Hierdie benadering
bied dus 'n strategie waardeur die insidente van oorontwerp beperk kan word.
Om die haalbaarheid van hierdie doelwitte, deur die gebruik van evolusionêre
versterkingsleer te ondersoek, is die SANE algoritme aangewend in 'n Windows omgewing (ontwikkel in Delphi 4.0). Die Delphi programmatuur geniet toepassing in
beide die simulasie en werklike beheer probleme. Vier nie-liniêre reaktore ontwerpe is
oorweeg in die simulasie studies. As 'n werklike beheer toepassing, is 'n nuwe
enkelladingsdistillasie kolom, 'n Multi-Effek Enkelladingskolom (MEBAD) gebou en
in bedryf gestel.
Die neurobeheerders vir die komplekse simulasie studies, wat deur SANE ontwikkel
is, het uitstekende robuustheid en veralgemeningsvermoë ten toon gestel. In
vergelyking met model voorspellingsbeheer implementasies, is gevind dat die
neurobeheerders heelwat minder sensitief is vir model parameter onsekerheid. Die
noodsaak na modelonsekerheid kompensasie om gestadigde toestand afset te
elimineer, word gevolglik verwyder. The SANE algoritme is ook hoogs effektief vir
die soek na die mees ekonomies bedryfstoestand, terwyl 'n effektiewe neurobeheerder
gelyktydig vir hierdie ekonomies optimumgebied ontwikkel word. SANE het egter
beperkte sukses in die leer van 'n effektiewe beheerstrategie vanaf die MEBAD
toetsaanleg getoon (swak veralgemening). Die swak veralgemening kan toegeskryf
word aan 'n te klein bedryfsgebied waarin die algoritme moes soek en die negatiewe
effek van sensor geraas op die evaluasie proses.
Vir industriële applikasies blyk dit dat die uitvoer van die evolutionêre proses vanaf 'n
wisselkeurige begintoestand nie koste effektief is in terme van tyd en finansies nie.
Deur die genetiese algoritme populasie vooraf op 'n benaderde modelop te lei, kan
die soek tydperk na 'n optimale beheerstrategie aansienlik verkort word. Die
aanwending van die neurobeheer ontwikkelingstrategie vanuit 'n aanlegwye oogpunt
mag aanleiding gee tot aansienlike voordele, aaangesien individuele beheerder
interaksies sodoende implisiet uitgeskakel word.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/51841 |
| Date | 04 1900 |
| Creators | Conradie, Alex van Eck |
| Contributors | Aldrich, C., Nieuwoudt, I., Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 270 p. : ill. |
| Rights | Stellenbosch University |
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