Thesis (PhD)--University of Stellenbosch, 2004. / 271 Leaves printed single pages, preliminary pages i-xviii and 253 numberd pages. Includes bibliography. List of figures, List of tables. / ENGLISH ABSTRACT: A Neurocontrol Paradigm for Intelligent Process Control using Evolutionary
Reinforcement Learning
Balancing multiple business and operational objectives within a comprehensive
control strategy is a complex configuration task. Non-linearities and complex multiple
process interactions combine as formidable cause-effect interrelationships. A clear
understanding of these relationships is often instrumental to meeting the process
control objectives. However, such control system configurations are generally
conceived in a qualitative manner and with pronounced reliance on past effective
configurations (Foss, 1973). Thirty years after Foss' critique, control system
configuration remains a largely heuristic affair.
Biological methods of processing information are fundamentally different from the
methods used in conventional control techniques. Biological neural mechanisms (i.e.,
intelligent systems) are based on partial models, largely devoid of the system's
underlying natural laws. Neural control strategies are carried out without a pure
mathematical formulation of the task or the environment. Rather, biological systems
rely on knowledge of cause-effect interactions, creating robust control strategies from
ill-defined dynamic systems.
Dynamic modelling may be either phenomenological or empirical. Phenomenological
models are derived from first principles and typically consist of algebraic and
differential equations. First principles modelling is both time consuming and
expensive. Vast data warehouses of historical plant data make empirical modelling
attractive. Singular spectrum analysis (SSA) is a rapid model development technique
for identifying dominant state variables from historical plant time series data. Since
time series data invariably covers a limited region of the state space, SSA models are
almost necessarily partial models.
Interpreting and learning causal relationships from dynamic models requires sufficient
feedback of the environment's state. Systemisation of the learning task is imperative.
Reinforcement learning is a computational approach to understanding and automating
goal-directed learning. This thesis aimed to establish a neurocontrol paradigm for
non-linear, high dimensional processes within an evolutionary reinforcement learning
(ERL) framework. Symbiotic memetic neuro-evolution (SMNE) is an ERL algorithm
developed for global tuning of neurocontroller weights. SMNE is comprised of a
symbiotic evolutionary algorithm and local particle swarm optimisation. Implicit
fitness sharing ensures a global search and the synergy between global and local
search speeds convergence.Several simulation studies have been undertaken, viz. a highly non-linear bioreactor, a
rigorous ball mill grinding circuit and the Tennessee Eastman control challenge.
Pseudo-empirical modelling of an industrial fed-batch fermentation shows the
application of SSA for developing partial models. Using SSA, state estimation is
forthcoming without resorting to fundamental models. A dynamic model of a multieffect
batch distillation (MEBAD) pilot plant was fashioned using SSA. Thereafter,
SMNE developed a neurocontroller for on-line implementation using the SSA model
of the MEBAD pilot plant.
Both simulated and experimental studies confirmed the robust performance of ERL
neurocontrollers. Coordinated flow sheet design, steady state optimisation and nonlinear
controller development encompass a comprehensive methodology. Effective
selection of controlled variables and pairing of process and manipulated variables
were implicit to the SMNE methodology. High economic performance was attained in
highly non-linear regions of the state space. SMNE imparted significant generalisation
in the face of process uncertainty. Nevertheless, changing process conditions may
necessitate neurocontroller adaptation. Adaptive neural swarming (ANS) allows for
adaptation to drifting process conditions and tracking of the economic optimum online.
Additionally, SMNE allows for control strategy design beyond single unit
operations. SMNE is equally applicable to processes with high dimensionality,
developing plant-wide control strategies. Many of the difficulties in conventional
plant-wide control may be circumvented in the biologically motivated approach of the
SMNE algorithm. Future work will focus on refinements to both SMNE and SSA.
SMNE and SSA thus offer a non-heuristic, quantitative approach that requires
minimal engineering judgement or knowledge, making the methodology free of
subjective design input. Evolutionary reinforcement learning offers significant
advantages for developing high performance control strategies for the chemical,
mineral and metallurgical industries. Symbiotic memetic neuro-evolution (SMNE),
adaptive neural swarming (ANS) and singular spectrum analysis (SSA) present a
response to Foss' critique. / AFRIKAANSE OPSOMMING: 'n Neurobeheer paradigma vir intelligente prosesbeheer deur die gebruik van
evolusionêre versterkingsleer
Dit is 'n komplekse ontwikkelingstaak om menigte besigheids- en operasionele
doelwitte in 'n omvattende beheerstrategie te vereenselwig. Nie-lineêriteite en vele
komplekse prosesinteraksies kombineer om ingewikkelde aksie-reaksie verwantskappe
te vorm. Dit is dikwels noodsaaklik om hierdie interaksies omvattend te
verstaan, voordat prosesbeheer doelwitte doeltreffend gedoen kan word. Tog word
sulke beheerstelsels dikwels saamgestel op grond van kwalitatiewe kriteria en word
ook dikwels staatgemaak op historiese benaderings wat voorheen effektief was (Foss,
1973). Dertig jaar na Foss se kritiek, bly prosesbeheerstelsel ontwerp 'n heuristiese
saak.
Die biologiese prosessering van informasie is fundamenteel verskillend van metodes
wat gebruik word in konvensionele beheertegnieke. Biologiese neurale meganismes
(d.w.s., intelligente stelsels) word gebaseer op gedeeltelike modelle, wat grotendeels
verwyderd is van die onderskrywende natuurwette. Neurobeheerstrategieë word
toegepas sonder suiwer wiskundige formulering van die taak of die omgewing.
Biologiese stelsels maak eerder staat op kennis van aksie-reaksie verhoudings en skep
robuuste beheerstrategieë van swak gedefineerde dinamiese stelsels.
Dinamiese modelle is of fundamenteel of empiries. Fundamentele modelle word
ontwikkel vanaf eerste beginsels en word tipies uit algebraïese en differensiële
vergelykings saamgestel. Modellering vanaf eerste beginsels is beide tydrowend en
duur. Groot databasisse van historiese aanlegdata maak empiriese modellering
aantreklik. Singuliere spektrumanalise (SSA) maak die vinnige ontwerp van empiriese
modelle moontlik, waardeur dominante veranderlikes vanaf historiese tydreekse
onttrek kan word. Aangesien tydreeksdata slegs 'n gedeelte van die prosesomgewing
verteenwoordig, is SSA modelle noodwendig gedeeltelike modelle.
Die interpretasie en aanleer van kousale verhoudings vanaf dinamiese modelle vereis
voldoende terugvoer van omgewingstoestande. Die leertaak moet sistematies
uitgevoer word. Versterkingsleer is 'n ramingsbenadering tot 'n doelwit-gedrewe
leerproses. Hierdie tesis bewerkstellig 'n neurobeheerparadigme vir nie-lineêre
prosesse met hoë dimensies binne 'n evolusionêre versterkingsleer (EVL) raamwerk.
Simbiotiese, memetiese neuro-evolusie (SMNE) is 'n EVL algoritme wat ontwikkel is
vir globale verstelling van die gewigte van ‘n neurobeheerder. SMNE is saamgestel
uit 'n simbiotiese evolusionêre algoritme en 'n lokale partikelswerm-algoritme.
Implisiete fiksheidsdeling verseker 'n globale soektog en die sinergie tussen globale
en lokale soektogte bespoedig konvergensie.Verskeie simulasie studies is onderneem, o.a. die van 'n hoogs nie-lineêre bioreaktor,
'n balmeulaanleg en die Tennessee Eastman beheer probleem. Empiriese modellering
van 'n industriële enkelladingsfermentasie demonstreer die aanwending van SSA vir
die ontwikkeling van gedeeltelike modelle. SSA benader die toestand van 'n
dinamiese stelsel sonder die aanwending van fundamentele modellering. 'n Dinamiese
model van 'n multi-effek-enkelladingsdistillasie (MEBAD) proefaanleg is
bewerkstellig deur die gebruik van SSA. Daarna is SMNE gebruik om 'n
neurobeheerder te skep vanaf die SSA model vir die beheer van die MEBAD
proefaanleg.
Beide simulasie en eksperimentele studies het die robuuste aanwending van EVL
neurobeheerders bevestig. Die gekoördineerde ontwerp van vloeidiagramme,
gestadigde toestand-optimering en nie-lineêre beheerderontwikkeling vereis 'n
omvattende metodologie. Beheerveranderlikes en die koppeling van proses- en
uitvoerveranderlikes is implisiet en effektief. Maksimale ekonomiese aanwins was
moontlik in hoogs nie-lineêre dele van die toestandsruimte. SMNE het besondere
veralgemening toegevoeg tot neurobeheerderstrategieë ten spyte van prosesonsekerhede.
Nietemin, veranderende prosestoestande mag neurobeheerderaanpassing
genoodsaak. Aanpasbare neurale swerm (ANS) algoritmes pas
neurobeheerders aan tydens veranderende proseskondisies en volg die ekonomiese
optimum, terwyl die beheerder die proses beheer. SMNE bewerkstellig ook die
ontwikkeling van beheerstrategieë vir prosesse met meer as een eenheidsoperasie.
SMNE skaal na prosesse met hoë dimensionaliteit vir die ontwikkeling van aanlegwye
beheerstrategieë. Talle kwelvrae in konvensionele aanleg-wye prosesbeheer word
deur die biologies gemotiveerde benadering van die SMNE algoritme uit die weg
geruim. Toekomstige werk sal fokus op die verfyning van beide SMNE en SSA.
SMNE en SSA bied 'n nie-heuristiese, kwantitatiewe benadering wat minimale
ingenieurskennis of oordeel vereis. Die metodologie is dus vry van subjektiewe
ontwerpsoordeel. Evolusionêre versterkingsleer bied talle voordele vir 'n ontwikkeling
van effektiewe beheerstrategieë vir die chemiese, mineraal en metallurgiese
industrieë. Simbiotiese memetiese neuro-evolusie (SMNE), aanpasbare neurale swerm
metodes (ANS) en singulêre spektrum analise (SSA) gee antwoord op Foss se kritiek.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/16030 |
| Date | 12 1900 |
| Creators | Conradie, Alex van Eck |
| Contributors | Aldrich, C., University of Stellenbosch. Faculty of Engineering. Dept. of Process Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | xviii, 253 leaves : ill. |
| Rights | University of Stellenbosch |
Page generated in 0.0149 seconds