150 research outputs found
Critical Behavior of the Sandpile Model as a Self-Organized Branching Process
Kinetic equations, which explicitly take into account the branching nature of
sandpile avalanches, are derived. The dynamics of the sandpile model is
described by the generating functions of a branching process. Having used the
results obtained the renormalization group approach to the critical behavior of
the sandpile model is generalized in order to calculate both critical exponents
and height probabilities.Comment: REVTeX, twocolumn, 4 page
Synthesis, structure, and spectral properties of copper(II), platinum(II) and palladium(II) 1,3-bis(2-alkyltetrazol-5-yl)triazenates
Inversion Symmetry and Critical Exponents of Dissipating Waves in the Sandpile Model
Statistics of waves of topplings in the Sandpile model is analysed both
analytically and numerically. It is shown that the probability distribution of
dissipating waves of topplings that touch the boundary of the system obeys
power-law with critical exponent 5/8. This exponent is not indeendent and is
related to the well-known exponent of the probability distribution of last
waves of topplings by exact inversion symmetry s -> 1/s.Comment: 5 REVTeX pages, 6 figure
Scaling of waves in the Bak-Tang-Wiesenfeld sandpile model
We study probability distributions of waves of topplings in the
Bak-Tang-Wiesenfeld model on hypercubic lattices for dimensions D>=2. Waves
represent relaxation processes which do not contain multiple toppling events.
We investigate bulk and boundary waves by means of their correspondence to
spanning trees, and by extensive numerical simulations. While the scaling
behavior of avalanches is complex and usually not governed by simple scaling
laws, we show that the probability distributions for waves display clear power
law asymptotic behavior in perfect agreement with the analytical predictions.
Critical exponents are obtained for the distributions of radius, area, and
duration, of bulk and boundary waves. Relations between them and fractal
dimensions of waves are derived. We confirm that the upper critical dimension
D_u of the model is 4, and calculate logarithmic corrections to the scaling
behavior of waves in D=4. In addition we present analytical estimates for bulk
avalanches in dimensions D>=4 and simulation data for avalanches in D<=3. For
D=2 they seem not easy to interpret.Comment: 12 pages, 17 figures, submitted to Phys. Rev.
Self-Organized States in Cellular Automata: Exact Solution
The spatial structure, fluctuations as well as all state probabilities of
self-organized (steady) states of cellular automata can be found (almost)
exactly and {\em explicitly} from their Markovian dynamics. The method is shown
on an example of a natural sand pile model with a gradient threshold.Comment: 4 pages (REVTeX), incl. 2 figures (PostScript
The simulation and optimization of steady state process circuits by means of artificial neural networks
Dissertation (Ph.D.) -- University of Stellenbosch, 1993.ENGLISH ABASTRACT: Since the advent of modern process industries engineers engaged in the
modelling and simulation of chemical and metallurgical processes have had to
contend with two important dilemmas. The first concerns the ill-defined nature
of the processes they have to describe, while the second relates to the
limitations of prevailing computational resources.
Current process simulation procedures are based on explicit process models in
one form or another. Many chemical and metallurgical processes are not
amenable to this kind of modelling however, and can not be incorporated
effectively into current commercial process simulators. As a result many
process operations do not benefit from the use of predictive models and
simulation routines and plants are often poorly designed and run, ultimately
leading to considerable losses in revenue.
In addition to this dilemma, process simulation is in a very real way constrained
by available computing resources. The construction of adequate process models
is essentially meaningless if these models can not be solved efficiently - a
situation occurring all too often.
In the light of these problems, it is thus not surprising that connectionist
systems or neural network methods are singularly attractive to process
engineers, since they provide a powerful means of addressing both these
dilemmas. These nets can form implicit process models through learning by
example, and also serve as a vehicle for parallel supercomputing devices. In this
dissertation the use of artificial neural networks for the steady state modelling
and optimization of chemical and metallurgical process circuits is consequently
investigated.
The first chapter is devoted to a brief overview of the simulation of chemical
and metallurgical plants by conventional methods, as well as the evolution and
impact of computer technology and artificial intelligence on the process
industries.
Knowledge of the variance covariance matrices of process data is of paramount
importance to data reconciliation and gross error detection problems, and
although various methods can be employed to estimate these often unknown variances, it is shown in the second chapter that the use of feedforward neural
nets can be more efficient than conventional strategies.
In the following chapter the important problem of gross error detection in
process data is addressed. Existing procedures are statistical and work well for
systems subject to linear constraints. Non-linear constraints are not handled
well by these methods and it is shown that back propagation neural nets can be
trained to detect errors in process systems, regardless of the nature of the
constraints.
In the fourth chapter the exploitation of the massively parallel information
processing structures of feedback neural nets in the optimization of process
data reconciliation problems is investigated. Although effective and
sophisticated algorithms are available for these procedures, there is an ever
present demand for computational devices or routines that can accommodate
progressively larger or more complex problems. Simulations indicate that neural
nets can be efficient instruments for the implementation of parallel strategies
for the optimization of such problems.
In the penultimate chapter a gold reduction plant and a leach plant are modelled
with neural nets and the models shown to be considerably better than the linear
regression models used in practice. The same technique is also demonstrated
with the modelling of an apatite flotation plant. Neural nets can also be used in
conjunction with other methods and in the same chapter the steady state
simulation and optimization of a gravity separation circuit with the use of two
linear programming models and a neural net are described.AFRIKAANSE OPSOMMING: Sedert die ontstaan van prosesingenieurswese, het ingenieurs gemoeid met die
modellering en simulasie van chemiese en metallurgiese prosesse met twee
belangrike dilemmas te kampe gehad. Die eerste het te make met die swakgedefinieerde aard van chemiese prosesse, wat die beskrywing en dus ook die
beheer daarvan kompliseer, terwyl die tweede verband hou met die beperkinge
van huidige berekeningsmiddele.
Die prosesse wat tans gebruik word om chemiese prosesse te simuleer is
gebaseer op eksplisiete prosesmodelle van een of ander aard. Baie chemiese en
metallurgiese prosesse kan egter nie op 'n eksplisiete wyse gemodelleer word
nie, en kan gevolglik ook nie doeltreffendheid deur kommersiële
prosessimulators beskryf word nie. Die bedryf van baie prosesse vind derhalwe
nie baat by die gebruik van voorspellende modelle en simulasie-algoritmes nie
en aanlegte word dikwels suboptimaal ontwerp en bedryf, wat uiteindelik tot
aansienlike geldelike verliese kan lei.
Prosessimulasie word op die koop toe ook beperk deur die beskikbaarheid van
berekeningsfasiliteite. Die konstruksie van geskikte prosesmodelle hou geen
voordeel in as hierdie modelle nie doeltreffendheid opgelos kan word nie.
Teen die agtergrond van hierdie probleme is dit nie verrassend dat neurale
netwerke 'n besondere bekoring vir prosesingenieurs inhou nie, aangesien hulle
beide hierdie dilemmas aanspreek. Hierdie nette kan implisiete prosesmodelle
konstrueer deur te leer van voorbeelde en dien ook as 'n raamwerk vir parallelle
superrekenaars. In hierdie proefskrif word die gebruik van kunsmatige neurale
netwerke vir gestadigde toestandsmodellering en optimering van chemiese en
metallurgiese prosesse gevolglik ondersoek.
Die eerste hoofstuk word gewy aan 'n kort oorsig oor die simulasie van
chemiese en metallurgiese aanlegte met konvensionele tegnieke, asook die
ontwikkeling en impak van rekenaartegnologie en skynintelligensie in die
prosesnywerhede.
Kennis van die variansie-kovariansie-matrikse van prosesdata is van kardinale
belang vir datarekonsiliasie en die identifikasie en eliminasie van sistematiese
foute en alhoewel verskeie metodes aangewend kan word om hierdie onbekende variansies te beraam, word daar in die tweede hoofstuk getoon dat
die gebruik van neurale netwerke meer doeltreffend is as konvensionele
strategieë.
In die volgende hoofstuk word die belangrike probleem van sistematiese foutopsporing
in prosesdata ondersoek. Bestaande prosedures is statisties van aard
en werk goed vir stelsels onderworpe aan lineêre beperkinge. Nie-lineêre
beperkinge kan nie doeltreffend deur hierdie prosedures hanteer word nie en
daar word gewys dat terugwaarts-propagerende nette geleer kan word om
sulke foute in prosessisteme op te spoor, ongeag die aard van die beperkinge.
In die vierde hoofstuk word die rekonsiliasie van prosesdata met behulp van
massiewe parallelle dataverwerkingstrukture soos verteenwoordig deur
terugvoerende neurale nette, ondersoek. Alhoewel doeltreffende en
gesofistikeerde algoritmes beskikbaar is vir die optimering van die tipe
probleme, is daar 'n onversadigbare aanvraag na rekenaars wat groter en meer
komplekse stelsels kan akkommodeer. Simulasie dui aan dat neurale nette
effektief aangewend kan word vir die implementering van parallelle strategieë
vir dié tipe optimeringsprobleme.
In die voorlaaste hoof stuk word die konneksionistiese modellering van 'n
goudreduksie- en 'n logingsaanleg beskryf en daar word aangetoon dat die
neurale netwerk-modelle aansienlik beter resultate lewer as die linneêre regressie modelle
wat in die praktyk gebruik word. Dieselfde tegnieke vir die modellering
van 'n flottasie-aanleg vir apatiet word ook bespreek. Neural nette kan ook
saam met ander metodes aangewend word en in dieselfde hoofstuk word die
gebruik van twee lineêre programmeringsmodelle en 'n neural net om 'n
gravitasieskeidingsbaan onder gestadigde toestande te simuleer en te optimeer,
beskryf
Dynamically Driven Renormalization Group Applied to Sandpile Models
The general framework for the renormalization group analysis of
self-organized critical sandpile models is formulated. The usual real space
renormalization scheme for lattice models when applied to nonequilibrium
dynamical models must be supplemented by feedback relations coming from the
stationarity conditions. On the basis of these ideas the Dynamically Driven
Renormalization Group is applied to describe the boundary and bulk critical
behavior of sandpile models. A detailed description of the branching nature of
sandpile avalanches is given in terms of the generating functions of the
underlying branching process.Comment: 18 RevTeX pages, 5 figure
Critical Behaviour of the Drossel-Schwabl Forest Fire Model
We present high statistics Monte Carlo results for the Drossel-Schwabl forest
fire model in 2 dimensions. They extend to much larger lattices (up to
) than previous simulations and reach much closer to the
critical point (up to ). They are incompatible with
all previous conjectures for the (extrapolated) critical behaviour, although
they in general agree well with previous simulations wherever they can be
directly compared. Instead, they suggest that scaling laws observed in previous
simulations are spurious, and that the density of trees in the critical
state was grossly underestimated. While previous simulations gave , we conjecture that actually is equal to the critical threshold
for site percolation in . This is however still far from
the densities reachable with present day computers, and we estimate that we
would need many orders of magnitude higher CPU times and storage capacities to
reach the true critical behaviour -- which might or might not be that of
ordinary percolation.Comment: 8 pages, including 9 figures, RevTe
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