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1

State-preserving nonlinear model reduction procedure

Yunfei Chu, Mitchell Serpas and Juergen Hahn

*

Artie McFerrin Department of Chemical Engineering

Texas A&M University

College Station, TX 77843-3122, USA

Tel: +1-979-845-3568

Fax: +1-979-845-6446

hahn@tamu.edu (J. Hahn)

ABSTRACT

Model reduction has proven to be a powerful tool to deal with challenges arising from large-

scale models. This is also reflected in the large number of different reduction techniques that

have been developed. Most of these methods focus on minimizing the approximation error,

however, they usually result in a loss of physical interpretability of the reduced model. A new

reduction technique which preserves a non-prescribed subset of the original state variables in

the reduced model is presented in this work. The technique is derived from the Petrov-

Galerkin projection by adding constraints on the projection matrix. This results in a

combinatorial problem of which states need to be selected. A sequential algorithm has been

developed based on the modified Gram-Schmidt orthogonalization procedure. The presented

technique is applied to two examples where the reduction error is found to be comparable to

the traditional POD method. At the same time, the technique has the advantage that the

physical interpretation of the remaining states is retained.

Keywords:

Model reduction, Nonlinear dynamics, Petrov-Galerkin projection, Proper-orthogonal

decomposition, Mathematical modeling, Systems engineering

2

1. Introduction

Model-based techniques have become increasingly popular in process control, monitoring,

and optimization. Part of this trend is that an increasing number of detailed models have been

built in a variety of different areas, e.g. chemical processes (Skogestad and Morari, 1988;

Maria 2004), biochemical networks (Dano et al., 2006; Conzelmann et al., 2008), or

combustion systems (Wang and Frenklach, 1997; Lu and Law, 2009). Furthermore, modeling

large-scale reaction systems is a subject that has been and will continue to be an active

research area (Ho, 2008). However, it is not always possible to verify all aspects of these

detailed models or to make use of these models in an online environment. Model reduction is

one approach to deal with these problems as the complexity of the model is reduced to a level

that is appropriate for a specific application at hand (Moore, 1981; Okino and Mavrovouniotis,

1998; Marquardt, 2001; Ryckelynck et al., 2006; Nagy and Turanyi, 2009).

It is the purpose of this work to introduce a model reduction technique that reduces the

model size while at the same time retains some of the original state variables of the model.

The advantage of such an approach is that the physical interpretation of the remaining states is

unchanged, unlike results that are obtained by many other model reduction techniques (Vajda

et al., 1985; Deane et al., 1991; Hahn and Edgar, 2002a, 2002b; Lall et al., 2002).

The presented technique makes use of a constrained Petrov-Galerkin projection. Similar to

proper-orthogonal decomposition (POD) techniques, the projection matrix is computed from

simulation data which allows the method to be applied to nonlinear models. However, unlike

regular POD algorithms, constraints are added for the computation of the projection matrix to

ensure that a subset of the original state variables are preserved in the reduced model. This

results in a combinatorial problem where states are selected based upon optimizing a least

squares criterion. A sequential algorithm that is based upon a modified version of the Gram-

3

Schmidt orthogonalization procedure is developed in this work to solve the selection problem

with a low computational burden.

The paper is structured as follows: Section 2 provides background information on the

Petrov-Galerkin projection and POD. Section 3 presents the model reduction technique and

some representative results returned by the method are shown in Section 4. Some concluding

remarks are given in Section 5.

2. Petrov-Galerkin projection and proper orthogonal decomposition

Model reduction aims to replace a complex model with one with fewer differential

equations and state variables. The original model is assumed to be of the form

( ) ( ) ( )

( )

,

d

t t t

dt

=x f x u (1)

where

x

n

∈

x

R

is the state vector and

u

n

∈u

R

is the input vector. A common approach for

model reduction is the Petrov-Galerkin projection (Antoulas, 2005), which derives a reduced

model based upon truncation of the model after a linear transformation has been performed

( ) ( ) ( )

( )

T

,

d

t t t

dt

=z L f Kz u (2)

where

z

n

∈

z

R

is the state vector of the reduced model, i.e.

n

z

<

n

x

.

The transformation matrices K and L need to satisfy several conditions: (1) The matrices

have full column rank, as it would otherwise be possible to further reduce the model by

eliminating linear dependent columns, and (2) the matrix L

T

is a left inverse of the matrix K,

satisfying

T

z

n

=

L K I

(3)

4

where I is an identity matrix of dimension

n

z

. This property ensures that the matrix given by

KL

T

is a projection matrix. Though the left inverse matrix is not unique for a given K, it is

often chosen to be the Moore-Penrose pseudo inverse of K (Li and Rabitz, 1990;

Dokoumetzidis and Aarons, 2009)

(

)

1

T T T

−

+

= =

L K K K K

(4)

which results in a unique choice of L

T

for a given K of full column rank. Using such a

procedure, the model reduction task can be viewed as the computation of a transformation

matrix K so that the reduced model provides an acceptable approximation of the dynamics of

the original model.

Proper orthogonal decomposition (POD) (Sirovich, 1987; Berkooz et al., 1993; Willcox

and Peraire, 2002; Rathinam and Petzold, 2003; Rowley et al., 2004; Romijn et al., 2008)

provides one approach to determine the transformation matrix K via principal component

analysis (PCA) of a matrix of snapshots, i.e. a matrix that contains data points of the states

taken at different points in time derived for a variety of stimulation profiles. In order to

generate a snapshot matrix, different model inputs such as the initial conditions of the state

vector x

0

and the input signals

u

(

t

) need to be determined. The input signal can often be

expressed as a function of a finite set of parameters, e.g., a piecewise constant function, a

polynomial derived from a truncated Taylor series, or a truncated Fourier series expression.

The different input parameters can be concatenated into a vector w and the set of all inputs

under consideration is denoted by Ω. Then, the original model is simulated, generating state

trajectories x(

t

,w) for different conditions w and different points in time

t

. A snapshot matrix

consists of sampling points from the simulated state trajectories, denoted by

( )

(

)

(

)

(

)

1 1 1 1

, , , , , , , , , ,

t w t w

n n n n

t w t w t w t w

=

X x x x x

L L L

(5)

5

where the values of the states are samples at the sampling points

t

j

and for a set of sampling

parameters w

k

corresponding to an input signal.

If POD is used then the transformation matrix K is determined from the optimal data

compression of the snapshot matrix. The state trajectories in

x

n

R

which are recorded in the

snapshot matrix X, can be compressed by a projection onto a subspace of

z

n

R

, expressed as

PX where P is a projection matrix of rank

n

z

. The projection matrix is determined from the

following optimization problem

( )

2

2

F

min

ε

=

= −

P P

P X PX

(6)

where

ε

is the data compression error evaluated by the Frobenius norm,

(

)

2

T

F

trace=

X XX

.

The optimal solution of the data compression can be obtained using PCA. The cross product

matrix of the data matrix is computed to be

( ) ( )

T

T

1 1

, ,

t w

n n

j k j k

j k

t t

= =

= =

∑∑

W XX x w x w (7)

which has an eigenvalue decomposition

T

=W V

Λ

V

(8)

where V is an orthogonal matrix with

T

x

n

=

V V I

and

(

)

2 2 2

1 2

diag , , ,

x

n

λ λ λ

=

Λ

L

. The diagonal

entries are ordered such that

2 2 2

1 2

x

n

λ λ λ

≥ ≥ ≥L

. Let

z

n

V

denote the submatrix of

V

which

contains the columns from 1 to

n

z. Then the projection matrix is given by

T

z z

n n

=

P V V

(9)

and the transformation matrix

K

can be found from

T

z

n

=

K V

(10)

6

It can be seen that the key component of POD involves solving a data compression problem

by projecting the state trajectory data on to a linear subspace.

3. Model reduction by state selection

One drawback of POD-based model reduction procedures is that the states of the reduced

model are a linear combination of the states of the original model, i.e., the physical

interpretation of individual states is lost in the process. While it is possible to reconstruct the

original states from the states of the reduced model via a matrix multiplication, the techniques

based upon POD are generally not designed to preserve properties of the original states. This

drawback forms the motivation behind the technique introduced in this work, as the presented

method results in models that retain the most important states of the original model.

3.1. Problem formulation

The presented technique is derived from the Petrov-Galerkin projection as well as POD.

However, additional constraints are placed on the projection matrix to ensure that the states of

the reduced model retain their physical interpretation.

To simplify the notation used for the state selection procedure, the state variables can be

classified into two sets: a set of states which will be retained in the reduced model,

x

r, and a

set of states which will be eliminated in the process,

x

d. It is desired to retain states such that

the trajectories of the states,

z

, of the reduced model shown in Eq. (2) are the same as the

trajectories of the corresponding states of the original model:

r

=

x z

(11)

An approximation of the states that were reduced in the process can be computed by a linear

combination of the retained states:

7

d r

= =

x Tz Tx

(12)

Combining Eq. (11) and Eq. (12) results in the following structure of

K

used in the Petrov-

Galerkin projection

z

n

=

I

K

T

(13)

The order of the states can be changed via multiplication with a permutation matrix

Π

such that

r

d

=

x

Πx

x

(14)

where

z

n

r

∈x

R

are the states that are retained,

x z

n n

d

−

∈xR are the states that will be reduced,

and the number of states in the reduced order model is

n

z

. The state variables retained in the

reduced model are directly given by the permutation matrix.

Appropriate choices for

Π

in Eq. (14) and T in Eq. (12) can be computed by solving the

following data compression problem

(

)

( )

2

F

,

1

T T T

T

min ,

s.t.

z

n

ε

−

= −

=

=

=

ΠT

ΠPΠX PΠX

I

KT

L K K K

P KL

(15)

where X is the snapshot data matrix calculated according to Eq. (5). This problem is derived

from Eq. (6) by adding the constraints (13) on the projection matrix and including a state

selection procedure via the permutation matrix

Π

.

8

The optimization problem is a mixed integer nonlinear programming (MINLP) which is

nontrivial to solve. However, for a given permutation matrix

Π

, the optimal value of the

transformation matrix T can be calculated analytically by minimizing the objective function

2

F

2

F

2

F

r r

d r

d r

−

= −

= −

ΠX PΠX

X X

X TX

X TX

(16)

where X

r

is the data matrix of the retained states while X

d

is the data matrix of the reduced

states, the solution of which is given by

(

)

1

T T

d r r r

−

=T X X X X (17)

Combining Eqs. (15)-(17) results in the following optimization problem

( )

( )

(

)

2

1

T T

F

min

t w

d n n r r r r

ε

−

×

= −

Π

ΠX I X X X X (18)

where only the permutation matrix

Π

needs to be determined, as it directly affects X

r

and X

d

.

3.2. Computation of data compression error

While it is possible to compute the data compression error

(

)

ε

Π

directly via matrix

inversions and multiplications, this procedure can be computationally expensive for large data

matrices. One alternative is to use a modification of the Gram-Schmidt orthogonalization

procedure which avoids computation of the inverse of a matrix:

Algorithm: Compute the data compression error using a modified Gram-Schmidt

orthogonalization procedure

Step 0. Suppose the selected state variables are indexed by

1 2

, , ,

z

n

i i i

L

. Set the initial

value of the projected row vectors

(

)

0

j j

i i

=

x x

, for

1, ,

x

j n

=

L

and the iteration

9

index

k

= 0.

Step 1. Set

k

=

k

+ 1 and

(

)

( ) ( )

( )

1

T

1

T

2

2

1

, for 1, ,

k

j j

x

j

k

k i

k k k k

i i x

k k

n

k

k i

j k

j k n

ε

−

−

= +

=

= − = +

=

∑

q x

q q

x x I q q

x

L

Step 2. If

k

<

n

z

, return to Step 1.

Step 3. Return the data compression error

z

n

ε ε

=

.

The orthogonalization procedure sequentially constructs a set of orthogonal bases. One

new vector is added to the set of bases at each step and then all remaining vectors are

projected on to the subspace which is orthogonal to the current bases. The projection ensures

that the next basis will be orthogonal to all already computed bases. The value of

k

ε

denotes

the sum of the squared 2-norm of all remaining vectors after the projection.

An important property of the orthogonalization procedure is that the subspace spanned by

the constructed orthogonal bases

1

, ,

k

q q

L

is identical to that spanned by the original vectors

1

, ,

k

i i

x x

L

. One result of this is that the value of

k

ε

is equal to the data compression error

when the states

1

, ,

k

i i

L

are selected. The modified Gram-Schmidt orthogonalization

procedure not only returns the final value of the data compression error but also the values at

each intermediate steps, which will be used to derive a sequential selection procedure.

3.3. Sequential solution of the state selection problem

As has been pointed out, the solution to the combinatorial problem shown in Eq. (18) is

non-trivial. It is generally not possible to perform an exhaustive search as the number of

possible combinations to select

n

z

states for the reduced model from

n

x

states of the original

10

model is

(

)

(

)

! ! !

x z x z

n n n n−

, which increases dramatically as the number of states increases.

Instead, a sequential selection algorithm is presented here which is based on the modified

Gram-Schmidt orthogonalization procedure. This procedure transforms the problem of

selecting multiple states simultaneously into a problem of sequentially selecting individual

states. The state variables are selected one at a time and, at the

k

-th step, the state to be

included is the one which results in the smallest data compression error

k

ε

. While

sequentially determining states to be included cannot ensure that the entire set of states will

result in the smallest overall data compression error, this approach has the advantage that only

(

)

2 1 2

z x z

n n n− +

evaluations are required to obtain a solution.

A diagram summarizing the sequential state selection algorithm is shown in Fig. 1. One

difference between the orthogonalization procedure of the algorithm and the one shown in Fig.

1 is that the projection of the data vectors and the calculation of the data compression error are

conducted using the cross product matrix

W

from Eq. (7) instead of the data matrix

X

from

Eq. (5). The reason for this modification is that the data matrix has a large dimension

(

n

x

×

n

t

×

n

w

), whereas only information about the inner product of the data vectors is required to

perform the state selection procedure. The cross product matrix has a significantly smaller

dimension given by (

n

x

×(

n

x

+1)/2). Additionally, only the upper-triangular entries of the

matrix are needed as

W

is a symmetric matrix.

The vector

s

in the selection algorithm in Fig. 1 records the sequence of selected states.

The procedure in Box 1 moves the entries corresponding to the selected state variables to the

top left corner of

W

. After a state variable is added to the selected set, the data vectors of the

unselected states are projected on to the subspace orthogonal to the one spanned by the data

vectors of the selected state. Since the selection and projection operations, performed at each

11

iteration, will be conducted based on the inner product of the projected vectors, the matrix

W

is updated via the procedure shown in Box 2. The inner product of the projected vectors can

be calculated from the current values as follows: Suppose two data vectors

x

i

and

x

j

are

projected on to the subspace orthogonal to another data vector

x

k

. The inner product of

x

i

and

x

j

after projection is given by

( )( )

T

T T

T T

T T

T

T

k k k k

i j

k k k k

i k j k

i j

k k

− −

= −

x x x x

x I x I

x x x x

x x x x

x x x x

(19)

It should be noted that only the values of the inner products before the projection are required

to update the values of the inner products after the projection (see Box 2). In other words,

each entry of W with indices

ij

after the projection, is updated from the

ij

,

ik

,

jk

, and

kk

entries in W before the projection was performed.

As the data compression error, after the selection has been made, is equal to the sum of the

squared norm of all projected vectors, the compression error is calculated by adding the

corresponding diagonal entries in W via the procedure shown in Box 3 in Fig. 1. The

procedure then determines the addition of which state will result in the farthest reduction of

the compression error, as is shown in Box 4 and the selected state and the corresponding error

value are recorded in Box 5.

The algorithm finally returns the sequence of selected states recorded in the vector

[

]

1:

z

n

s

as well as the corresponding data compression errors recorded in the vector

ε

. The

unselected states are recorded in the vector

[

]

1:

z x

n n

+s

.

12

The state selection algorithm determines which states should be retained in the reduced

model from the information of the inner product matrix W and this algorithm forms the core

component of the presented model reduction technique, shown in Fig. 2.

The first part of the procedure generates a data set from simulations for the state selection

algorithm. A set of input signals,

{

}

k

w

, is used to excite the model around its steady state.

The set can be chosen to include specific signals of interest or can be randomly sampled from

a region under consideration. For example, if the upper bound and the lower bound of w are

available, then the values of w

k

can be generated by uniform sampling within this region.

After the set of input signals has been determined, the model is simulated for each input signal

and the state trajectories recorded. The time interval used for the simulation should be

sufficiently long to capture the dynamic behavior of the system, while the sampling interval

should be chosen small enough such that the sampling points represent fairly continuous state

trajectories.

The collected data is used to compute the cross product matrix W according to Eq. (7) and

the states to be retained in the reduced model are selected according to the state selection

algorithm based on the calculated W. As only the cross product matrix W is stored, instead of

the original data matrices, the matrix T is computed by Eq. (20).

[ ] [ ] [ ] [ ]

(

)

1

1: : 1: 1: : 1:

z x z z z

n n n n n

−

= +

T W s s W s s

(20)

The final step of the reduction procedure is to determine the number of states,

n

z

, of the

reduced model. It is desirable to set the number as small as possible while retaining a

reasonably accurate model. As it is not possible to determine the number of states

a priori

for

a nonlinear model, a trial-and-error procedure needs to be applied. Fortunately, the sequential

state selection procedure is computationally inexpensive, which facilitates the use of such a

13

trial-and-error procedure. After determining the number of retained states it is straightforward

to calculate the transformation matrices and conduct the Petrov-Galerkin projection to

compute the reduced model.

However, similar to POD, the data compression error is not identical to the actual model

approximation error. The data compression error is the difference between the state trajectory

data and the projection of these data onto a subspace. Even though the transformation

matrices calculated from data compression are used in the Petrov-Galerkin projection to

derive the reduce model, the state trajectories of the reduced model are not identical to the

projection of the state trajectories from the original model. One result of this is that the data

compression error is just an

a priori

estimate of the reduction error and the actual error, called

the

a posteriori

error in the following, will still need to be computed.

The

a posteriori

model approximation error is

2

F

e

= −

X KZ

(21)

where Z is the data matrix calculated from state trajectories of the reduced model, recorded in

the same way as X. The transformation KZ denotes the computed state values of both the

retained states and the reduced states of the reduced model. If the

a posteriori

error is not

acceptable then the number of selected states needs to be enlarged. On the other hand, if the

a

posteriori

error is small then it may be possible to further reduce the model. As the model

reduction scheme selects states sequentially, it is straightforward to either add another state to

the already selected list of states or to delete one of the states. The changes made to the model

can then be evaluated by recomputing the

a posteriori

error.

14

4. Case studies

4.1. Linear 4-compartment model

The first example deals with a linear 4-compartment model (Sherwin et al., 1974). A

diagram of the model is shown in Fig. 3(a). The differential equations describing the model

are

(

)

( )

10 12 13 14 21 31 41

1 1

12 21

2 2

4

13 31

3 3

14 40 41

4 4

0

0 0

0

0 0

0

0 0

1

k k k k k k kC C

k kC C

d

I

k k

C C

dt

k k k

C C

− + + +

−

= +

−

− +

(22)

where the state variables C

i

correspond to the concentration in the i-th compartment and the

values of the kinetic parameters are shown in Fig. 3(a).

The system is stimulated by raising the inflow I

4

from zero to 1.00 mU/min. The system is

simulated from t = 0 to t = 120 min and the states are sampled every 5 minutes. Since the

model is linear, only one input signal is required to compute the cross product matrix W:

40.95 14.54 53.58 106.11

14.54 5.17 19.23 37.55

53.58 19.23 79.24 136.73

106.11 37.55 136.73 275.94

=

W (23)

Applying the state selection algorithm to W, the states are selected in the order C

1

, C

3

, C

4

and C

2

. For example, if one wants to reduce the model from four to three states then C

2

is

reduced in the process and the resulting differential equations of the simplified model are

given by

1 1

3 3

4 4

0.320 0.012 0.099 0.07

0.041 0.020 0.000 0.00

0.250 0.000 0.384 0.99

C C

dC C

dt C C

−

= − +

−

(24)

15

The diagram representing the reduced model from Eq. (24) is shown Fig. 3(b). Similarly, if

only two states are to be retained then the reduced model consists of the states C

1

and C

3

.

When the 3-state model (Fig. 3(b)) is constructed the reduced state is the concentration of

compartment 2. However, the reduced model is not simply obtained by removing the

compartment 2 as the values of the rate constants are also changed, due to the Petrov-Galerkin

procedure (2). It can be seen from the input vector in Eq. (24) that the inflow rate to

compartment 4 is decreased while the inflow into compartment 1 is slightly increased and a

sink term is added to compartment 3. Similar changes occur if the model is further reduced,

(Fig. 3(c) and Fig. 3(d)).

To check the model approximation error the a priori error and the a posteriori errors are

displayed in Fig. 4 along with the state trajectories for each reduced model. From the

reduction error it can be seen that the reduced models with 3 states or 2 states approximate the

original model well, however, the model with only 1 state results in significantly larger errors.

4.2. Binary distillation column

The second example deals with a distillation column that has 30 trays for the separation of

a binary mixture. The state variables are given by the liquid compositions on the 30 trays, in

the reflux drum, and in the reboiler. The column is assumed to have a constant relative

volatility of 1.6, and symmetric product compositions. The feed stream is introduced at the

column on stage 17 and has a composition of 0.5. The reflux ratio is the adjustable input, u,

with a nominal value of 3.0 and a range from 1.0 to 5.0. A detailed description of the model

can be found in the literature (Benallou et al., 1986; Hahn and Edgar, 2002a).

A data set is created by simulating the model for different values of the input u ranging

from 1.0 to 5.0 in 0.04 increments. The matrix W is computed according to Eq. (7) from the

16

generated data sets. The a priori approximation error (data compression error) and the a

posteriori approximation error (model approximation error) are listed in Table 1 for different

numbers of retained states. The threshold value of the reduction error, used to determine the

number of states retained in the reduced model, is set to 2%. If 3 states are retained in the

model then the a priori is equal to 0.65%. However, the a posteriori error for this case is

2.20% according to Eq. (21) and it requires 4 states to have an a posteriori error of 1.03%.

The selected 4 states for the reduced model are the concentration of one component in the

reflux drum, and on the 3

rd

, 8

th

, 22

nd

tray. All other 28 states can be adequately expressed by

linear combinations of these 4 states. To visualize the performance, Fig. 5 shows the

difference between the original model and the reduced model when the input is changed from

the original value to 2.0 in one case and to 4.0 in a second simulation. In both cases, the

reduced model is able to accurately approximate the original model.

For comparison purposes, the approximation errors returned by this model reduction

technique are compared to those for a model that is reduced by POD. The results are

summarized in Table 1. Not surprisingly POD performs slightly better as it represents the

optimal solution to the data compression problem given by Eq. (6). However, the differences

between the model derived by the presented technique and the one reduced by POD are very

small for this case. Furthermore, the presented technique has the advantage that the physical

interpretation of all the states is retained, while this is not the case when POD is used. Though

not shown in this work, the same conclusion can be drawn for the previous example.

5. Conclusion

This paper presents a model reduction technique that is based upon selecting some of the

states of the model to be retained in the reduced model, while the remaining states can be

17

expressed as a linear combination of these retained states. The technique is based on the

Petrov-Galerkin projection and the projection matrix is computed from data derived from the

original model. However, unlike what is done using POD, some constraints are added to the

projection matrix to preserve a subset of original state variables, resulting in a combinatorial

problem. This combinatorial problem is solved sub-optimally using a sequential algorithm

based upon a modified version of the Gram-Schmidt orthogonalization procedure. The

advantage of using a sub-optimal solution is that the solution is computationally inexpensive.

Two examples are shown to illustrate the presented reduction technique.

Acknowledgment

The authors gratefully acknowledge partial financial support from the National Science

Foundation (Grant CBET# 0941313) and the ACS Petroleum Research Fund (Grant PRF#

48144-AC9).

18

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21

Table 1

The a priori approximation error

ε

, and the a posteriori approximation error, e, returned by

the state selection method and POD. The values are listed for several different number of

states preserved in the reduced model and normalized by the norm of all state trajectories of

the original model.

number of states 1 2 3 4 5 6 7

state

selection

ε

% 15.76 3.42 0.65 0.18 0.11 0.07 0.04

e % 19.66 8.81 2.20 1.03 0.95 0.79 0.29

POD

ε

% 15.62 2.81 0.47 0.13 0.08 0.05 0.02

e % 19.75 8.67 2.07 1.03 0.90 0.86 0.33

22

[

]

[

]

[ ] [ ]

[ ] [ ]

, ,

, 1: 1 1 : 1,

, 1: , 1:

l l

l l

l x l x

k k l l

k k l k l l

k l n l l n

↔

+ − ↔ + −

+ ↔ +

W W

W W

W W

[ ] [ ]

[

]

[

]

[ ]

, ,

, , ,

l l

l l

l

k i k j

i j i j k k

= − W W

W W W

[ ]

1

,

x

n

l l

i k

r i i

= +

=

∑

W

(

)

*

*

arg min ,

k

l

l

l

l r= =

W W

[

]

[

]

*

*

,

l

k l k r

↔ =

s s ε

Fig. 1. Diagram of the sequential state selection algorithm. A subset of n

z

state variables is

selected from all n

x

state variables based on W, the cross product matrix of the data matrix.

The notation W[i, j] returns the entry in the i-th row and j-th column of W. The notation ‘

↔

’

refers to the values of two vectors being switched.

23

Fig. 2. Outline of model reduction algorithm

24

(

)

(

)

(

)

(

)

2 1 3 4

0.741 0.010 0.144

C t C t C t C t

= − −

(a) (b)

(

)

(

)

(

)

( ) ( ) ( )

2 1 3

4 1 3

0.325 0.023

2.895 0.232

C t C t C t

C t C t C t

= +

= −

(

)

(

)

( ) ( )

( ) ( )

2 1

3 1

4 1

0.325

1.309

2.591

C t C t

C t C t

C t C t

=

=

=

(c) (d)

Fig. 3. Diagram of compartment models with different number of states; (a): original 4-state

model, (b) reduced model with 3 states, (c) reduced model with 2 states, and (d) reduced

model with 1 state.

25

0 20 40 60 80 100 120

0

1

2

3

4

5

C

1

C

2

C

3

C

4

time, min

concentration, mU

model : 4-state : 3-state : 2- state : 1-state

a priori error 0 0.0002 0.4965 10.120

a pos teriori error 0 0.0169 0.1809 11.727

Fig. 4. Comparison of the states described by the original model and 3 reduced-order models.

26

0 20 40 60 80 100 120

0

0.2

0.4

0.6

0.8

1

time, min

composition

(a)

0 20 40 60 80 100 120

0

0.2

0.4

0.6

0.8

1

time, min

composition

(b)

Fig. 5. State trajectories of the original model (solid line) and the ones recovered from the

reduced model (dash line) in the case of (a) u = 2 and (b) u = 4 (u

ss

= 3).