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B.CSUKÁS and GY.BÁNKUTI: GENERAL BI-LAYER NET
I.J. of SIMULATION, Vol. 6, No. 6 ISSN:1473-804x online, 1473-8031 print 10
GENERIC BI-LAYERED NET, AS THE NATURAL COMPUTATIONAL MODEL OF CONSERVATION AND
INFORMATION PROCESSES
BÉLA CSUKÁS and GYÖNGYI BÁNKUTI
Institute of Mathematics and Information Technology, University of Kaposvár Guba S. u. !0. Kaposvár, 7400, Hungary
[email protected], [email protected]
Abstract: In Direct Computer Mapping the simple building blocks of the conservational and informational processes are mapped onto the generic “active” and “passive” elements of an executable program. The recently developed Generic Bi-layered Net model provides a common framework for the simulation of the hybrid (continuous and discrete, quantitative and qualitative) balance-based and rule-based processes. The common features of the process models are represented by a bi-layered net of variable structure that also determines the network (ring) structures of the influence routes and flux routes, as well as the Gantt Chart view of the process. The advantage of the new methodology is that the computational model is specified by the very structures and building elements of the process to be modeled. The software implementation of Generic Bi-layered Net model onto an executable program or dynamic database has been applied for the solution of various difficult practical problems. Keywords: generic simulation, hybrid processes, direct computer mapping, Generic Bi-layered Net 1. INTRODUCTION 1.1 ‘A Priori’, ‘a Posteriori’ and knowledge based Models of Processes The computer modeling of the continuous and discrete processes have been evolving in three different ways. The processes are usually described by a set of algebraic, differential and/or integral equations IPDAE (Pantelides, 2001). The ‘a priori’ (white box) models are derived from the simple first principle primitives, and then they are transformed into various sophisticated mathematical constructs. The ‘a posteriori’ (black box) models differ only in the origin, but the numerical solution of the identified mathematical equations is similar. Artificial Intelligence developed various knowledge-based methods without the explicit consideration of domain specific structures and of fundamental conservation laws. The qualitative and heuristic models, described by expert rules, often cannot be linked to any of the previously mentioned methods. Expert systems deal with an abstract set of signs and rules that should somehow be actualized in the engineering applications. The attempts to bridge this gap, with various kinds of qualitative models were not successful enough, because the qualitative knowledge representation evolved on its own, without effective connection to the quantitative modeling. The tools of Computational Intelligence (neural networks, genetic algorithm, fuzzy sets) were derived from the simplified pattern of the biological systems and from the human reasoning.
They cannot be combined easily with the quantitative engineering knowledge. The execution of the hybrid, discrete / continuous models is a difficult question, because the usual integrators do not tolerate the discrete events, while the usual representation of the continuous processes cannot be embedded into the discrete models conveniently. 1.2 General Formal Models of the Processes The general formal models of the systems had be developed before the powerful computers appeared. According to the Kalman’s approach [Kalman et al. 1969], the state space model of the continuous processes
t,g,f),0(x,Y,X,U (1) is described by the transition (f) and output (g) functions in the continuous time t:
( )( ) )t),t(u),t(x(gty
)t),t(u),t(x(ftx==
•
(2)
where, U)t(u ∈ = the input variables, X)t(x ∈ = the state variables and Y)t(y ∈ = the output variables
of the process.
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B.CSUKÁS and GY.BÁNKUTI: GENERAL BI-LAYER NET
I.J. of SIMULATION, Vol. 6, No. 6 ISSN:1473-804x online, 1473-8031 print 11
The abstract automaton representation of the discrete processes describes the same in the discrete time k, as follows:
( )( )
x k 1 f (x(k), u(k),k)
y k 1 g(x(k),u(k),k)
+ =
+ = (3)
1.3 General Net Theory and Special Net Structures (Petri Nets) The General Net Theory [Brauer, 1980] proposed a generalized net model for the description of the structures. Accordingly the triplet
N , , F= Π Θ (4)
is a net iff
F ( ) ( )
dom(F) codom(F)
Π∩Θ =∅⊆ Π×Θ ∪ Θ×Π
Π∪Θ ≠ ∅∪ = Π∪Θ
(5)
where Π, Θ and F represent the sets of the states, of the transitions and of the functional relations (links), respectively. Many net models, like the early appeared and very innovative Petri Net [Petri, 1962], as well as the various State-Transition Nets belong to the above family. 1.4 Idea of Direct Computer Mapping of the Process Models Many recently used engineering methods had been established before the onset of powerful computers. Modeling starts either from the consideration of changes in characteristic measures, or from the rules and signs. Next this is transformed into mathematical construct. It usually cannot be solved, should be discretisized, and finally, the computer executes simple arithmetical steps. Figure 1. The idea of the Direct Computer Mapping In Direct Computer Mapping DCM (Csukás and Bánkuti, 2003a), we can map the simple building blocks of the conservational and informational
processes onto the generic “active” and “passive” elements of an executable program (see Fig.1.). The balance elements and the signs, as well as the elementary transitions and the rules can be described by brief uniform programs, executed by the same kernel algorithm. Direct Computer Mapping of process models allows the computer to know explicitly about the very structures and bounds of the physical world. In this knowledge representation, the model is organized rather by the transitions, than by the state. The key issue is that the computational software (and hardware) can copy the natural structure and building elements of the investigated problem. 2. GENERIC BI-LAYERED NET MODEL OF COMPLEX PROCESSES The recently developed Generic Bi-layered Net model (Csukás and Bánkuti, 2003b) is a theoretically established and practically validated powerful realization of the Direct Computer Mapping. It is a special case of the General Net Theory on the one hand, as well as an explicitly structured, generic combination of the state space model and of the abstract automaton. The generic, bi-layered net model can be defined by the ten-tuplet of
t,r,,,Y,X,G,B,A,P ΨΦ (6) where GB,A,P ∪ is a net. The communication channels B and G determine the passive→active
( ) ( ) ( )τ×τ⊂τ APB (7) ( ) ( ) ( )( ) ( )τ∈ττ=τ Ba,pb ijij (8) ( ) ( ) ( )( ) ( )τ∈ττ∀τ∃ ba,p|b jij
ij (9)
( ) ( ) ( )( ) ( )τ∈ττ∀τ∃ ba,p|b jiji
j (10)
and active→passive data flows
( ) ( ) ( )τ×τ⊂τ PAG (11) ( ) ( ) ( )( ) ( )τ∈ττ=τ Gp,ag jiji (12) ( ) ( ) ( )( ) ( )τ∈ττ∀τ∃ gp,a|g ijiji (13) ( ) ( ) ( )( ) ( )τ∈ττ∀τ∃ jjiij gp,a|g (14)
respectively. Index j designates the ordered sets of the existing output ( bj (9)) and input ( jg (14))
connections for the j-th passive element. Similarly, index i defines the ordered sets of the existing output ( gi (18)) and input ( ib (10)) connections for the i-th active element.
Problemtobe solved
Simple Eqs. or rules
Mathematical construct
Decomposition
Numerical method
Executableprogram
Directmappingof Bi-layeredGeneric Net
Abstraction
Problemtobe solved
Simple Eqs. or rules
Mathematical construct
Decomposition
Numerical method
Executableprogram
Directmappingof Bi-layeredGeneric Net
Abstraction
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B.CSUKÁS and GY.BÁNKUTI: GENERAL BI-LAYER NET
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Variable τ denotes the optional points or intervals of the continuous or discrete time t, declaring the existence of the respective elements and relations. The passive elements P are associated with state variables jX and with an operator, describing the change of the state:
[ ][ ]⎥⎦
⎤⎢⎣
⎡=ψΨ∈ψ∈→∀
bxgy
;;XXpjj
jjijjj
j (15)
where jX contains any structured data set, and
operator jψ describes how jy changes the state via
the channels jg .
The active elements A are characterized by the operator iϕ , providing a mapping. This determines how the output changes yy iji ∈ , carried by gi are calculated from the coordinated input readings
iij xx ∈ that comes from the passive elements
through the channels ib : [ ][ ]⎥⎦
⎤⎢⎣
⎡=ϕΦ∈ϕ→∀
gybx
;aii
iiiii
i (16)
The operators Ψ∈ψ i and Φ∈ϕi may be anything from a simple input/output mapping to a brief program, calculating the elementary process or the rule. Variable r designates the geometrical and/or the property coordinates of the distributed systems and/or population balances. As an example, the GBN implementation of a simple hybrid automaton see the so-called Single Switch Server problem ([Perkins and Kumar, 1989]; [Agarwal et al., 2002]) in Fig. 2.
Figure 2. The Single Switch Server problem
This is one of the simplest discrete / continuous, switched, hybrid dynamic system. The suppliers send materials in the buffer tanks A, B and C at constant flow rates of VA, VB and Vc, respectively.
Machine can process any one material at a time at rate VMA, VMB and VMC. The level of materials changes in the buffers, however there are determined minimal and maximal levels. A material specific setup time is incurred each time machine switches to different material. The goal is to design a switching strategy, which satisfies various single or multiple objectives, e.g. maximal production, minimal setup and waiting time, minimal buffer levels, etc. There is an obvious interaction between the continuous and discrete (logic) components, i.e. it is a simple prototype of the hybrid dynamic systems. The GBN representation of the given problem can be seen in Fig. 3.
Figure 3. The GBN model of the SSS problem Two examples for the passive elements and one example for an active element are the followings:
∑+=∆
⎥⎦
⎤⎢⎣
⎡===∆=∆
Ψ→
i1iAA
A71A11
144A111A11
yM:M
MxMxyMyM
:p (17)
Wait Setup C Prod B Prod A Prod St
StxyStySt
:pi10i
101010111010
∨∨∨∨=⎥⎥⎦
⎤
⎢⎢⎣
⎡=∀
==Ψ→
(18) In the network view of the net we can interpret the alternating, connected, ordered set of the communication channels { }
1nnnn32222111 jiijjiijjiij g,b,g,b,g,b + (19) They are called influence routes, which determine a special network structure. The influence routes carry the influence. E.g. the perturbation of the content
1jX of the element 1jp affects the content 1njX + of
the element 1njp + , according to the influence:
a9
p6p9
fC cC
a8
p5p8
fB cB
a4 a6a5a10
p10
ST
a1 a2 a3
p7
a7
fA cA
p4M ΣP
p12
Σx
p13
a12
Σt
p14
A B C
p1 p2 p3
a11
p11
TS
p15
a9
p6p9
fC cC
a9
p6p9
fC cC
a9
p6p9
fC cC
p6p9
fC cC
a8
p5p8
fB cB
a8
p5p8
fB cB
a8
p5p8
fB cB
p5p8
fB cB
a4 a6a5a4 a6a5a10
p10
ST
p10
STSTSTST
a1 a2 a3a1 a2 a3a1 a2 a3
p7
a7
fA cAfA cAfA cA
p4MM ΣP
p12
ΣP
p12
Σx
p13
Σx
p13
a12
Σt
p14
A B C
p1 p2 p3
A B CA B C
p1 p2 p3p1 p2 p3
a11
p11
TS
p11
TSTSTS
p15
MA MB MC
VA VB VC
VMAVMB VMC TCl
Process
LNLP
UPUN
MA MB MC
VA VB VC
VMAVMB VMC TCl
Process
LNLPLNLP
UPUNUPUPUNUN
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B.CSUKÁS and GY.BÁNKUTI: GENERAL BI-LAYER NET
I.J. of SIMULATION, Vol. 6, No. 6 ISSN:1473-804x online, 1473-8031 print 13
( ) ( ){ }1nn222111 jii2jji1ij
y,...,tx,y,tx+
∆∆∆∆
(20) where ij x∆ and ji y∆ refer to the perturbation of the state and the change, respectively. The sensitivity and its special forms, such as observability and controllability can be studied by means of the influence route network. The minimal (generating) influence routes are the basic edges. The maximal influence routes are the transferring routes and the complete loops. The simplified structure of the influence routes is a special ring,
where the two algebraic operations are the concatenation and the common part. As we have emphasized, the existence of elements A, P, B and G, as well as the contents X and Y of the communication channels depend on the time while τ denotes the well-defined points or intervals of the continuous or discrete time t, when the given element, channel or sign does exist. This temporal behavior of the system of variable structure can be seen from the Gantt Chart view (see Fig. 4).
Figure 4. The Gantt Chart view of a hybrid model
3. BALANCE PROCESSES AND CONSER-VATIONAL PROCESSES, AS A SPECIAL CASE 3.1 Balance Processes An important special case of net (6) is the class of balance processes, where the basic part of the state
jX is a measure and, the operator jψ summarizes the simultaneous rates. Depending on the discrete or continuous time, operator jψ generates also the appropriate
( ) [ ] [ ] ∑=∆∆
≈=ψi
jijjjj
jj yt
pX
td
pdXy (21)
difference or differential equations, called balance equations. In the balance model the descendent of the mappings, iϕ can be divided into two disjunct
parts, corresponding to the increases (+) and decreases (-) of the characteristic measures:
[ ] [ ] [ ]−−++ ∪= gygygy iiiiii (22) The active elements of the balance process models describe the various transportations and transformations. 3.2 Conservational Processes Conservational process is a special case of the balance process, where there are constant measures C determined by the model specific conservation laws. Simultaneously all of the measures M can be combined from these constant measures, according to the respective stoichiometry S, i.e.:
CSM|SC ⋅=∃∃ (23) where S is the stoichiometry matrix of the passive elements.
t
Gannt ChartView
t
Gannt ChartView
t
Gannt ChartView
Gantt Chart View
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B.CSUKÁS and GY.BÁNKUTI: GENERAL BI-LAYER NET
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To get a sophisticated definition let C be a measure in the space of the geometric co-ordinates x,y,z and of other parameters (dimensions) 1 2 nd ,d ,...,d . This measure can change in continuous or discrete time t. Let us mark the finite, closed region in the above space with v and its volume with V. The not necessarily finite and closed ‘environment’ of region v, which will be indicated with u\v, is called universal complement. Let [ t ti j, ] be a finite time interval. vC (t) and u\vC (t) mark the measure C associated with the region v and with its universal complement at a time. The constant measures are characterised by the following axiomatic properties: Axiom 1. The change in the constant measure in any finite and closed region v during any time interval [t ti j, ] is accompanied by the identical change of the same measure in the universal complement with an opposite sign, i.e.
( ) ( )( ) ( ) ( )( ){ }i j \ i \ ji, ji j
C t C t C t C tν ν µ ν µ νν
<
∀ ∀ − = − −
(24) Axiom 2.The constant measures are bounded in any finite and closed region, i.e.
( ) ( )( ),t
C ,C | C C t Cν ν ν ν νν∀ ∃ −∞ < ≤ ≤ < +∞ (25)
As a consequence, the constant measures can be transformed simply into the
( ) ( )*ˆ0 C t C tν ν≤ ≤ (26) domain by subtracting the absolute lower bound from each value. Constant measures correspond to the quantities obeying to the existing conservation laws of the investigated system, within the given model hypothesis and regardless of the physical meaning. For example within the model hypothesis of chemistry the number and the mass of the atoms are constant measures that correspond to the conservation law of the given model hypothesis. Although all of this model hypotheses have a limited validity, they give a constructive, sound basis of the problem solving within the scope of the given model. Thinking about the above example, many chemical compounds can be built from the 120 atoms. These number and the mass of these molecules do not satisfy the axioms of the above definition, because these amounts can change within the region by
chemical reaction. However we can write the balance equations of the respective chemical reactions with the help of the stoichiometry that derives these secondary measures from the primary ones obeying the conservation law of the existing model hypothesis. On the other hand there might be a lot of measures that cannot be derived from the constant measures so simply or cannot be deduced from them all. In the following the measures that can be derived as the homogeneous linear function of the constant measures (such as components from atoms in chemistry) are called conservational measures. To define the conservational measures consider a finite closed region v, containing a set of the constant measures C { }1 2, mC ,C ,...,C= . Designate
{ }a b qM M ,M ,..., M= a set of measures in the same region. Measures M are called conservational measures if for any t there is a matrix ( )S t of coefficients ( )i, jr t that satisfies the
( ) ( ) ( )M , t S t C tνν = . (27) equation, where M(v,t) is the quantity of a conservational measure in region v at a time t. If the operator iϕ can be determined by a well-defined single rate iv , then the change of the conservational measures the expression of
TM(t) S v(t) V•
Γ= ⋅ ⋅ (28)
can be written for (where V is the reference measure, e.g. the volume). In the steady state:
T0 S v(t) VΓ
= ⋅ ⋅ (29)
As an example consider in Fig. 5 the GBN model of a simple enzyme reaction of
fumarate (S) + water (I) → malate (W)
where S=C4H4O4 I=H2O W=C4H6O5 while the active elements correspond to the elementary processes of the a1:E+S → ES; a2:ES → E+S; a3:ES+I → ESI; a4:ESI → EW; a5:EW → E+W and a6:E+W → EW reactions, as well as of the a7, a8, a9 = transportations.
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B.CSUKÁS and GY.BÁNKUTI: GENERAL BI-LAYER NET
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Figure 5. GBN representation of an enzyme reaction where e.g.
VMc;yyyM
c:x,c:xy,y,y
:;Sp
SS121118S
S11S81
12111811
∆=++=∆
⎥⎦
⎤⎢⎣
⎡==
ψ→ (30)
tVcckvvy,vy,vykx,cx,cx
:;va
IS33
343333323
335I33ES32333
∆⋅⋅⋅⋅=
⎥⎥
⎤⎢⎢
⎡==−====
ϕ→
(31) The state of the model can be described by the vectors of the constant and conservational measures. The stoichiometry of the changes is characterized by the so called process stoichiometry matrix
S 4 4 4 0ES 4 4 4 0
CI 0 2 1 0
HC M SESI 4 6 5 1
OE 0 0 0 1
EEW 4 6 5 1W 4 6 5 0
⎡ ⎤ ⎡ ⎤⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥⎡ ⎤ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥= = =⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎣ ⎦⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥⎣ ⎦ ⎣ ⎦
(32)
(33)
(34) 3.3 The Ring of the Balance Routes In the balance process models the alternating, connected, ordered set of the communication channels { }+−+−+−
+1nnnn32222111 jiijjiijjiijg,g,...,g,g,g,g
(35) is called flux route. Flux routes determine another network structure, which carry the constant and conservational measures. For example, if we modify the value of measure
1jX in element 1jp , then this
change effects on measure 1njX + of the element
1njp + , i.e.:
( ) ( ) ( ) ( ){ }nji1ij2ji1ij ty,...,ty,ty,ty n1n222111 +−+− ∆∆∆ − (36)
The −+∆ /jiy values refer to the dispersion of the
changes in the rate of subsequent processes (multiplied by the stoichiometric coefficients). The minimal (generating) flux routes are the basic edges. The maximal flux routes are transferring routes and the complete loops. The simplified structure of the flux routes is a special ring, where the two algebraic operations are the concatenation and the common part. In steady state the flux routes describe a Kirchoffian network. 4. INFORMATIONAL PROCESS, AS A SPECIAL PART OF CONSERVATIONAL PROCESS It is to be noted that a part of the above conservational model, responsible for the enzymatic control, can be replaced *for a simplified model of rules and signs, respectively. This is illustrated in Fig. 6. where the signs are symbolized by circles and the rules are represented by bar nodes.
GGB
EWS I WEES ESI
v7 v8 v1 v3v2 v4 v5 v6 v9
p1 p2 p3 p4 p5 p7p6
a7 a8 a1 a2 a4 a5 a9
S I W
v7 v8 v3 v9a6
GGB
EWS I WEES ESI
v7 v8 v1 v3v2 v4 v5 v6 v9
p1 p2 p3 p4 p5 p7p6p1 p2 p3 p4 p5 p7p6
a7 a8 a1 a2 a4 a5 a9
S I W
v7 v8 v3 v9a6
T
1 1 0 0 0 0 1 0 01 1 1 0 0 0 0 0 00 0 1 0 0 0 0 1 0
S 0 0 1 1 0 0 0 0 01 1 0 0 1 1 0 0 0
0 0 0 1 1 1 0 0 00 0 0 1 1 1 0 0 1
Γ
−⎡ ⎤⎢ ⎥− −⎢ ⎥⎢ ⎥−⎢ ⎥
= −⎢ ⎥⎢ ⎥− −⎢ ⎥
−⎢ ⎥⎢ ⎥− − −⎣ ⎦
t∆V
)t(v)t(v)t(v)t(v)t(v)t(v)t(v)t(v)t(v
100111000000111000000110011000001100010000100000000111001000011
)t(M∆
9
8
7
6
5
4
3
2
1
⋅⋅
⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
⋅
⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
−−−−
−−−
−−−
−
=
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Figure 6. An example for the conservation based information process
Examples for the ψ and ϕ mappings of a sign (p6, evaluation of W), and of a rule (a5, calculation of the reaction rate) are as follows:
V/Mccx
y:;cp
WW
W56
666W6
=
⎥⎦
⎤⎢⎣
⎡=
ψ→
)c,c,c(Fkky
cx,cx,cx:;ra
WIS3
355
W56I54S52555
=
⎥⎥
⎤⎢⎢
⎡=
===ϕ→
(37
) 5. INFORMATIONAL PROCESS, AS A SPECIAL SUPPLEMENT OF THE CONSERVATION PROCESS The human made “artificial” processes often do not have the above described self-determined control, but they can be supplied by an informational process, determining the control signs and rules. Fig. 7 AND 8 show an example for the GBN model of a controlled heat exchanger. In the Figures V and W, as well as H and Q refer to the volume and the enthalpy of the inside and outside liquid, respectively. The inlet and outlet flows of the inside and outside agents are signed by the symbols Vb and Vk as well as Wb and Wk respectively. The heat transfer is symbolized by the elementary process Ht.
Figure 7. A conservational / informational process
Figure 8. Plausible visualization of a conservation based information process
GGB
cWS I Wk3cS cI
v7 v8 r1 v3r2 r4 r5 r6 v9
p1 p2 p3 p4 p5 p7p6
a3 a6a7 a8 a1 a2 a4 a5 a9
GGB
cWS I Wk3cS cI
v7 v8 r1 v3r2 r4 r5 r6 v9
p1 p2 p3 p4 p5 p7p6p1 p2 p3 p4 p5 p7p6
a3 a6a7 a8 a1 a2 a4 a5 a9a7 a8 a1 a2 a4 a5 a9
∞
V H Q W
Vb
Vk
Wb
WkHt
u T
m PID
aVb
WbWk
Vk
Ta
XPID
m
uVH
W,Q
∞
V H Q W
Vb
Vk
Wb
WkHt
u T
m PID
aVb
WbWk
Vk
Ta
XPID
m
uVH
W,Q
∞Vk
V H Q W
Vb Wb
WkHt
mb
m
mk
Vb
WbWk
Vk
m
mb
mkT
u
uT
VH
W,Q
∞Vk
V H Q W
Vb Wb
WkHt
mb
m
mk
Vb
WbWk
Vk
m
mb
mkT
u
uT
VH
W,Q
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The temperature T is measured by the thermometer m, and it is compared with the set point a. With the knowledge of this difference, the PID controller calculates the control action u. In the right hand side the Generic Bi-layered Net model of the heat exchanger and the controller are represented by a connected pair of a conservational and an informational process. In the practical realization the informational process is carried out by another physical (electronic / electric, hydraulic or pneumatic) process (i.e. another conservational process). The apparently paradox, but meaningful notion of conservation based informational processes is illustrated in Fig. 8 more plausibly. Here the liquid flow through the jacket of the heat exchanger is controlled by another liquid flow through another vessel. By decreasing the size of the heat exchanger and by the simultaneous increase of the upper right hand side volume, the exciting question appears whether the parts changed their relative position. It means beyond a certain point we recognize that the heat exchanger controls the other unit. From theoretical point of views, this results a new interpretation of the informational process. Accordingly, a given part of the conservational process behaves as an informational process with respect to its complementary part, if this special part consumes and produces significantly less conservational measures, than the complementary process, while, along the feedback influence loops
and transferring influence routes the informational process exerts more influence on the complement, than the completing part on it. The informational process can be a special part of the self-determined natural processes (e.g. neural system, enzyme regulation), or it can be a supplied part of the non-self-determined artificial one (e.g. control systems). The essential feature of the informational process is that it •transports negligible amount of conservational measures with the complementing part and with the environment, •while it has a greater influence on the operation of the complementing part than vice versa. If the above criteria are fulfilled, then it is not necessary to describe the conservational processes for this special subsystem. Instead, we can read, calculate and overwrite the appropriate signs simply. Accordingly, we neglect the conservational process carrying these signs, and deal only with the informational process carried by the vehicle conservation process. 6. CLASSIFICATION OF THE PROCESS MODELS The above described relation between the conservational and informational processes can be overviewed by the classification of the processes according to Fig.9.
Figure 9. The classification of the GBN processes
GBN processes
Informational processes
Balance processes
Conservational processes
Fictitious processes
SupplementedInformational part
Transformed part
Special part
Special part
Primary inform. process
GBN processes
Informational processes
Balance processesBalance processes
Conservational processesConservational processes
Fictitious processes
SupplementedInformational part
Transformed part
Special partSpecial partSpecial part
Special part
Primary inform. process
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The set of balance processes is a subset of the Generic Bi-layered Net processes. Conservational processes are in a subset of the balance processes. Both of the balance and conservational processes might have a special part that consumes and produces less additive measures, but exerts more influence on the completing part. These special parts can be transformed into the respective informational processes. Marginally the whole balance or conservational process can be mapped into an informational process. Another case is, when the balance or conservational process is supplied with an informational process. In addition there are also primary informational processes. On the other hand, all of the above described informational processes must have a vehicle conservational process that carries the signs and
executes the rules. The brain and the computers can work as conservational processes, themselves. It is to be noted that there might be also fictitious processes, outside of the set of the informational processes. 7. PRACTICAL APPLICATIONS The methodology has been applying for the solution of various difficult practical problems. A couple of practical examples and the difficulties to be overcome are summarized in Table I. In Table II. typical examples for the passive and active elements, describing the various models are illustrated. It is to be noted, that the general kernel program is the same in every application.
Table I. Overview of the simulation based identification, control and design problems, solved with the method
Problem to be solved
Special difficulties
Simulated Moving Bed prep. Chromatography
Cyclically changing initial and boundary conditions
Batch polymerization of directed structures
Partially mixed volume, multiple discrete feeds
Identification of metabolic network models
Large models with roughly estimated initial data set
Optimization of supply / demand chains
Dynamic simulation of hybrid automatons
Macro level simulation of a chicken poultry
Common use of models coming from various fields
Planning and scheduling of an agricultural farm
Dynamic cost analysis and year long cash flow calc.
Quantitative health risk analysis of batch plant
Changing allocation of dan-gerous sources and workers
Table II. Typical examples for the passive and active elements, describing the various applications
Task
Passive and active elements
Preparative chromatography [e. g. Temesvari et al. 2004]
Components of phases and in volume Component transfer, input/output flows
Batch polymerization [e. g. Csukás and Balogh 1998]
Populations in the compartments Feed, reactions, dissolving, heat transfer
Metabolic network [e. g. Csukas and al. 2003]
Metabolites, enzymes, cofactors Reactions, transport processes
Optimization of supply / demand chains [e. g. Csukás and Bánkuti 2003b]
Storage levels, fuzzy constraints, various signs Purchases, processing, sales, rules
Chicken poultry
Parts of the animal, air, feeds, walls Eating, metabolization, transportations
Agricultural farm [ongoing analysis of an agricultural plant]
Plants, feeds, pig populations Harvest, mixing, fattening, purchase, sale
Health risk analysis [ongoing analysis of a pharmaceutical plant]
Workers and components in the space Ventilation, pollution, work
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In Figs. 10-12 the results obtained for the above mentioned hybrid automaton (see Figs. 2 and 3) are summarized. As it can be seen, controlled by the built-in rules a periodic solution evolves rapidly. As a more sophisticated example, in Fig. 13 see a typical part of a generic metabolic model. The conventional models are built from the previously derived “aggregated” blocks (like the various versions of the Michaelis-Menten model), and we are not able to add any new mechanism for the same enzymes correctly. The reasons are that we do not have any access to the enzyme concentration on one hand and, the supplied models can contradict the assumptions, made in the determination of the original models, on the other. As a rough solution, we could determine the effect of the new agents on the parameters of the existing kinetic equations
however it is, in principle, an incorrect way of thinking.
Buffer levels
0
50
100
150
200
250
300
0 50 100 150 200Time, h
Leve
l, kg
Supp_A MSupp_B MSupp_C M
Figure 10. Change of the level in the storage
volumes
Cumulated production
0
1000
2000
3000
4000
5000
6000
7000
8000
0 50 100 150 200Time, h
Prod
uct,
kg
Prod_A MProd_B MProd_C M
Figure 11. Cumulated productions
Evaluation parameter
-1.5
-1
-0.5
0
0.5
1
1.5
0 50 100 150 200
Time, h
Para
met
er
Supp_A MEASSupp_B MEASSupp_C MEAS
Figure 12. Change of the evaluating parameter
In addition the practical realization of this identification can also be questioned. Using published experimental data, we built two different GBN models, as follows [Csukás et al., 2003]: • First, we generated a “simplified, gross” GBN
model (GBN1), based on the published individual enzyme kinetic model, as well as on the published model parameters. In this case the GBN framework played the role of a simple simulating tool.
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Figure 13. Measured and simulated changes of metabolite concentration in glycolysis
•Next, we synthesized a more detailed model
(GBN2), consisting of each elementary processes, and of each individual enzyme complexes, playing any role in the process. In this version we can exploit all of the capabilities of the GBN model, however, there is an enormous number of parameters to be identified. As an example, consider one of the reactions of the metabolic network. In the usual metabolic models, the transformation
AcAld ⇔ EtOH, (38) ADH catalyzed by the enzyme alcohol dehydrogenase can be modeled by an ordered bimolecular kinetics, with the cofactor binding first. According to the usual approach of the metabolic simulation we are advised to use the rate equation
ivbiaiubiaivuivbia
a
ivuia
v
biaivivu
v
bia
a
ia
ivubiaADH
KKKbuv
KKKabu
KKuv
KKKbvK
KKKapK
KKab
Kv
KKpK
KKbK
Ka1
KKuvV
KKabV
v
++++
+++++++
−−+=
−+
(39) In this rate equation a=EtOH, b=NAD, u=AcAld and v=NADH. V+ and V- are the main (Michaelis-Menten like) rate parameters and there are many equilibrium parameters Kxy. The gross Generic Bi-layered Net model (GBN1) of the reaction (38) contains a single active element, with a program that calculates the rate expression (39). Similarly, we had to build many case specific prototypes for the various types of metabolic reactions. In the detailed Generic Bi-layered Net model (GBN2) of the gross reaction (38) we have seven passive elements for the participating components:
GlcoutGlcinATP
F6P
F16bP
GAP
Glycogen
BPG
P3GA
P2GA
TransHK
PGI
PFKNAD
NADH
ADP ATPADP
ALD
TPINAD
ATP
GAPDH
PGM
ENO
G6P
NADH
NADHPGK
EtOH
PEP
PYR
PDC NADH NAD4ATP3NAD
SuccinateADH
4ADP3NADH
AcAld
ADP
ATPPYK
CO2
DHAP
NADHNAD
G3PDHGlycerol
Glucose uptake
0
20
40
60
80
100
120
0 1000 2000 3000 4000Time, s
Qua
ntity
, mM
Meas_GlcGBN1_GlcGBN2_Glc
Glycerol production
0
5
10
15
20
25
0 1000 2000 3000 4000Time, s
Qua
ntity
, mM
M_Glycerol
GBN1_Glycerol
GBN2_Glycerol
GlcoutGlcinATP
F6P
F16bP
GAP
Glycogen
BPG
P3GA
P2GA
TransHK
PGI
PFKNAD
NADH
ADP ATPADP
ALD
TPINAD
ATP
GAPDH
PGM
ENO
G6P
NADH
NADHPGK
EtOH
PEP
PYR
PDC NADH NAD4ATP3NAD
SuccinateADH
4ADP3NADH
AcAld
ADP
ATPPYK
CO2
DHAP
NADHNAD
G3PDHGlycerol
GlcoutGlcinATP
F6P
F16bP
GAP
Glycogen
BPG
P3GA
P2GA
TransHK
PGI
PFKNAD
NADH
ADP ATPADP
ALD
TPINAD
ATP
GAPDH
PGM
ENO
G6P
NADH
NADHPGK
EtOH
PEP
PYR
PDC NADH NAD4ATP3NAD
SuccinateADH
4ADP3NADH
AcAld
ADP
ATPPYK
CO2
DHAP
NADHNAD
G3PDHGlycerol DHAP
NADHNAD
G3PDHGlycerol
Glucose uptake
0
20
40
60
80
100
120
0 1000 2000 3000 4000Time, s
Qua
ntity
, mM
Meas_GlcGBN1_GlcGBN2_Glc
Glycerol production
0
5
10
15
20
25
0 1000 2000 3000 4000Time, s
Qua
ntity
, mM
M_Glycerol
GBN1_Glycerol
GBN2_Glycerol
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ADH enzyme, AcAld and EtOH compounds, ADH_NADH, ADH_NADH_ AcAld, ADH_NAD_EtOH and ADH_NAD complexes. There are ten (or five combined) active elements for the ten reactions. The full system of equilibrium chemical reactions is the following:
ADH + NADH ⇔ ADH_NADH ADH_NADH + AcAld ⇔ ADH_NADH_AcAld
ADH_NADH_AcAld⇔ADH_NAD_EtOH (40)
ADH_NAD_EtOH ⇔ ADH_NAD + EtOH ADH_NAD ⇔ ADH + NAD In the GBN2 model the majority of the elementary processes can be described by three simple prototypes corresponding to the 2→1, 1→1 and 1→2 simple bilinear/linear, linear/linear and linear/bilinear equilibrium kinetics, respectively. The general program for all of these active elements can be calculated by the
iii
iiiii vuK
kbakv −= (41)
rate expressions, where k is the kinetic parameter, K is the equilibrium constant, a, b, and u, v are the concentrations of input and output components, respectively. In the GBN1 representation of the conventional model we used the published concentrations, rate constants and equilibrium parameters. Regardless to the known parameters of GBN1 model, it could be tuned with serious difficulties. This is caused by the rigid connection of previously and independently prepared, “canned” building blocks. The GBN2 representation of the detailed elementary model was identified with a special multistage identification methodology. First, we started from a broader possibility space, containing ranges for the enzyme concentrations, equilibrium constants, kinetic parameters, initial concentrations, etc., and then the search space was narrowed stepwise. The consecutive steps of the identification were the following:
• Heuristic rules and manual adjustment; • Pre-screening with the help of a multicriteria
genetic algorithm; • Manual refinement with trial and error
method; • Genetic search in a narrow possibility space;
Although model GBN2 has an enormously great number of unknown parameters, it can be identified easier. The most important parameters are the enzyme concentrations, while the roughly estimated equilibrium and kinetic parameters can compensate each other’s error somehow.
As it can be seen from the example shown in Fig. 13, the two models gave similar result that fit the experiments. The case study proved that the identification of the detailed elementary model is not hopeless. It is an interesting observation that the detailed model runs faster, because the built-in natural control helps to avoid the stiff behavior. The independently developed greater building blocks of the conventional model do not connect to each other smoothly and this causes more difficulties in the implementation and in the use. The result is straightforward also for the solution of the drug discovery or agent discrimination problems which cannot be solved by the conventional modeling approach neither in principle, nor in practice. Similarly it is not possible to supplement the usual mathematical constructs and numerical algorithms with the new elements and interactions. Consequently, the feasibility of generating and using the detailed generic models is promising for the future developments. 9. CONCLUSIONS In Direct Computer Mapping we map the simple building blocks of the conservational and informational processes onto the generic “active” and “passive” elements of an executable program, directly. Direct Computer Mapping of process models allows the computer to know explicitly about the very structures and bounds of the physical world. The recently developed Generic Bi-layered Net model is a theoretically established realization of the Direct Computer Mapping. It is a special case of the General Net Theory on the one hand, as well as an explicitly structured, generic combination of the state space model and of the abstract automaton. The Generic Bi-layered Net model provides a common framework for the simulation of the hybrid (continuous and discrete, quantitative and qualitative) balance-based and rule-based processes. The common features of the process models are represented by a bi-layered net of variable structure that also determines the network (ring) structures of the influence routes and flux routes, as well as the Gantt Chart view of the process. The advantage of the new methodology is that the computational model is specified by the very structures and building elements of the process to be modeled. The Generic Bi-layered Net model makes possible an interesting, new representation of the informational processes. Accordingly the
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informational process is a special part of the self-determined natural processes, or it can be a supplied part of the non-self-determined artificial process. The essential feature of the informational process is that it • transports negligible amount of conservational
measures with the complementing part and with the environment,
• while it has a greater influence on the operation of the complementing part than vice versa.
If the above criteria are fulfilled, then it is not necessary to describe the conservational processes for this special subsystem. Instead, we can read, calculate and overwrite the appropriate signs simply. Accordingly, we neglect the conservational process carrying these signs, and deal only with the informational process carried by the vehicle conservation process. The method offers robust solution for the hard, hybrid, multidimensional and non-linear problems, as well as supports parallel programming. The software implementation of Generic Bi-layered Net model onto an executable program or dynamic database has been applied for the solution of many difficult practical problems. REFERENCES Brauer, W. Ed. 1980. “Net Theory and
Applications”. Springer Lecture Notes in Computer Science, (84).
Csukás B. and Bánkuti Gy. 2003a. “Direct computer mapping of process models”. In: Foundations of Computer Assisted Process Operations, I. E. Grossmann and C. M. MacDonald, Eds., AIChE INFORMS, pp. 577-581.
Csukás B. and Balogh S.: “Combining Genetic Programming with Generic Simulation Models in Evolutionary Synthesis”. Computers in Industry, 36, 181-197.
Csukás B. and Bánkuti Gy. 2003b. “Generic Bi-layered Net model of conservational and informational processes”. In: C. H. Dagli, A. et al. Eds. Intelligent Engineering Systems through Artificial Neural Networks, 13, ASME Press, New York, pp. 769-774.
Csukás B., Debelak, K. A., Prokop, A, Balcarcel, R. R., Tanner, R. D., Bánkuti Gy, Balogh S. 2003. “Generic Bi-layered Net Model Based Discrimination of Chemical and Biological Warfare Agents”, AIChE Annual Meeting, San Francisco, Manuscript 474f.
Kalman, R., Falb, P. and Arbib, M.. 1969. “Topics in Mathematical System Theory”, McGraw Hill.
Pantelides, C. C. 2001. “New Challenges and Opportunities in Process Modeling”. Proceedings of ESCAPE-11, Coppenhagen, Elsevier.
Petri, C. A. 1962. “Kommunikation mit Automaten”, Schriften des Institut für Instrumentelle Mathematik, 2, Bonn.
Temesvári K., Aranyi A., Csukás B., and Balogh S. 2004. “Simulated Moving Bed Separation of a Two Components Steroid Mixture”. Chromatographia,
ACKNOWLEDGEMENT The work was supported in part by Hungarian Scientific Research Fund T 037-297. BIOGRAPHY Béla Csukás was born in Keszthely, Hungary. He studied chemical engineering and process control at the University of Veszprém and obtained CSc/PhD degree in 1985. He worked for research institutes, industrial R&D and universities. Now he is Professor of Information Technology, leading the Institute of Mathematics and Information Technology at the University of Kaposvar, Hungary. Gyöngyi Bánkuti was born in Nagybajom, Hungary. She studied mathematics and mechanical engineering at the Technical University of Budapest and obtained PhD degree in 1990. She worked for various firms before moving to the University of Kaposvár, Hungary, where she is Associate Professor of Applied Mathematics and Chair of Department of Applied Mathematics and Physics.