Water activity (aw) has conveniently been used to design food products because of the ease of evaluating the variable using the chemical engineering technique (Fennema, 1976). Figure 9 illustrates aw as a function of *W*0 for the squid and the salmon. As can be seen from the comparison between the two curves, an identical curve was obtained without detecting any difference between the squid and the salmon. This result strongly indicates a limitation of the chemical engineering technique. To develop a new food product, one should choose another parameter for discriminating among products. To address this issue, a computer simulation method for developing new food design parameters is proposed in the next

> 0 50 100 150 200 250 *W* 0 (%-d.b.)

**5.2 Ambiguous physical meaning of the chemical engineering parameters derived**

To characterize the water species retained in the salmon, dehydration response curves could effectively be employed. Figure 10 illustrates the dehydration response curves as a function of the water content of the salmon. The five samples of *W*0 = 54, 99, 144, 200, and 252%-d.b. were prepared from a sample of *W*0 = 342%-d.b., and each of the samples was treated by a PUP-operation (poultice up: the samples were stored in a dark room at *T* = 2ºC for 24 h before use; this operation was conducted to provide homogeneity of water distribution in the sample). The PUP operation contributes to the stepwise increase of the dehydration rate, as shown in Figure 11, by the curves of *b*' ~ *f* '. Focusing on the five points of *b* ~ *f* on the dehydration curve of *W*0 = 342%-d.b. in Fig. 11(A), the dehydration rates of the five samples clearly demonstrate a stepwise increase, as shown in Fig. 11(B), by the curves of *b' ~ f '* due to PUP operation. One can recognize that curves *b*' ~ *f* ' demonstrate a monotonous decay

**from a computer simulation for the dehydration dynamism of salmon**

**5.2.1 Dehydration dynamism of the water species in the salmon**

○ □

*Dried salmon Dried squid*

**5.1 Unclear discrimination of water activity in foods in the chemical engineering**

**5. Limitation of the chemical engineering technique**

0.5

Fig. 9. aw as a function of *W*0 for the salmon and squid.

0.6

0.7

0.8

aw

0.9

1.0

**technique**

section.

Fig. 10. Dehydration response curves of the salmon (*T*D = 45°C).

Fig. 11. Acceleration behavior of dehydration rate derived from the PUP operation for the salmon (*T*D = 45C).

curve reaching finally to the curve of *a*' with an increase in the elapsed time. Using the dehydration rates obtained, the effective diffusivity of the water species, *De*, can be evaluated as a function of *W*0 by applying Equation (1).

Challenging Evaluation of the Hybrid Technique of

decrease of water species mobility (*De*).

15

restriction strength, and the pre-exponential factor (PF =

20

25

*E*

Fig. 14. *E*D as a function of *W*0 for the salmon.

**curves**

coefficient, *k*W (h-1) and *k*S (h-1).

D(kJ/mol)

30

35

40

45

Chemical Engineering – Proton NMR Technique for Food Engineering 81

Figure 14 demonstrates *E*D as a function of *W*0. The activation energy obtained, the *E*D values, demonstrates again two water species, A1 and A2, divided at *W*0 = 120%-d.b. *E*D in the water species A1 region gave an identical value of 22 (4) kJ/mol and a gradual increase from 20 to 38 (8)kJ/mol with decreasing *W*0. Taking into account the restriction of water species growth that accompanies increased dehydration, one can observe evidence of the activation energy growth (indicating a difficulty of the molecular diffusion of water species in the food tissue matrix due to the growth of the restriction strength) as a result of the

*Species A2 Species A1*

0 50 100 150 200 250 300 350 *W* 0(%-d.b.)

*D*0) of *De* showed steep growth

As for a reason that the activation energy was drastically changed at *W*0 = 120 %-d.b., it can be attributed to a change in the physical structure of the food tissue matrix due to progressive dehydration. The value of *W*0 = 120%-d.b. corresponds exactly to the critical value of C (CC = 10-8s), at which water species A1 and A2 were divided according to their

with decreasing *W*0 (as will be discussed in the upcoming section, see Fig. 16). The steep growth of PF resulted from the drastic change in the porosity and labyrinth factor of the salmon at *W*0 = 120 %-d.b. accompanied by the gradual reduction of *De* decreasing *W*0.

The transient response curves of dehydration in the drying operation of foods have been simulated using various mathematical models (Chhinnan, 1984; Madamba *et al.*, 1996; Thompson *et al.*, 1968; Noomhorm and Verma, 1986). In the present study, the dehydration response curves of the salmon presented in Fig. 10 are simulated using a water tank model as a first approximation. The water species retained in the sample are portioned to two different water tanks and the two tanks are respectively filled by the two different water species, A1 (weakly restricted water species) and A2 (strongly restricted water species), as shown in Figure 15 (Konishi *et al.*, 2001). During the drying operation, the two water species in the tanks are simultaneously drained by regulating the two valves as mass transfer

**5.2.3 Water tank model for the computer simulation of the dehydration response**

#### **5.2.2 Physicochemical mobility (***De***) of the water species**

One can recognize the *De* value as a physicochemical mobility of the water species retained in the foods. Figure 12 illustrates *De* for the salmon as a function of *W*0 and temperature. Although the data are widely scattered, one can roughly recognize two different water species (species A1 and A2) formed in the salmon, depending on the values of *W*0. The two water species were divided at about *W*0 = 120%-d.b., indicating an identical *De* for the water species A1 region between *W*0 = 120 and 360%-d.b. and a gradual decrease of *De* for the water species A2 region with decreasing *W*0 at 35~55ºC. Using a rough evaluation of *De* obtained at the three temperatures, one would roughly construct the Arrhenius plots as shown in Figure 13. The slopes of the straight lines seem to give different activation energies depending on the water content of the samples.

Fig. 12. *De* as a function of *W*0 in the salmon.

Fig. 13. Arrhenius plots of *De* for the salmon.

One can recognize the *De* value as a physicochemical mobility of the water species retained in the foods. Figure 12 illustrates *De* for the salmon as a function of *W*0 and temperature. Although the data are widely scattered, one can roughly recognize two different water species (species A1 and A2) formed in the salmon, depending on the values of *W*0. The two water species were divided at about *W*0 = 120%-d.b., indicating an identical *De* for the water species A1 region between *W*0 = 120 and 360%-d.b. and a gradual decrease of *De* for the water species A2 region with decreasing *W*0 at 35~55ºC. Using a rough evaluation of *De* obtained at the three temperatures, one would roughly construct the Arrhenius plots as shown in Figure 13. The slopes of the straight lines seem to give different activation energies

*Species A2 Species A1*

0 50 100 150 200 250 300 350 *W* 0(%-d.b.)

0.0030 0.0031 0.0032 0.0033 1/*T* D(K-1)

○ △ □ ● ▲ ■

**5.2.2 Physicochemical mobility (***De***) of the water species**

depending on the water content of the samples.

0.E+00

Fig. 12. *De* as a function of *W*0 in the salmon.

Fig. 13. Arrhenius plots of *De* for the salmon.

ln(*De* ) -13.5

1.E-06

2.E-06

*De*

(m2/h)

3.E-06

4.E-06

5.E-06

△ ○ □ *T* D(°C) 35 45 55

Figure 14 demonstrates *E*D as a function of *W*0. The activation energy obtained, the *E*D values, demonstrates again two water species, A1 and A2, divided at *W*0 = 120%-d.b. *E*D in the water species A1 region gave an identical value of 22 (4) kJ/mol and a gradual increase from 20 to 38 (8)kJ/mol with decreasing *W*0. Taking into account the restriction of water species growth that accompanies increased dehydration, one can observe evidence of the activation energy growth (indicating a difficulty of the molecular diffusion of water species in the food tissue matrix due to the growth of the restriction strength) as a result of the decrease of water species mobility (*De*).

Fig. 14. *E*D as a function of *W*0 for the salmon.

As for a reason that the activation energy was drastically changed at *W*0 = 120 %-d.b., it can be attributed to a change in the physical structure of the food tissue matrix due to progressive dehydration. The value of *W*0 = 120%-d.b. corresponds exactly to the critical value of C (CC = 10-8s), at which water species A1 and A2 were divided according to their restriction strength, and the pre-exponential factor (PF = *D*0) of *De* showed steep growth with decreasing *W*0 (as will be discussed in the upcoming section, see Fig. 16). The steep growth of PF resulted from the drastic change in the porosity and labyrinth factor of the salmon at *W*0 = 120 %-d.b. accompanied by the gradual reduction of *De* decreasing *W*0.

#### **5.2.3 Water tank model for the computer simulation of the dehydration response curves**

The transient response curves of dehydration in the drying operation of foods have been simulated using various mathematical models (Chhinnan, 1984; Madamba *et al.*, 1996; Thompson *et al.*, 1968; Noomhorm and Verma, 1986). In the present study, the dehydration response curves of the salmon presented in Fig. 10 are simulated using a water tank model as a first approximation. The water species retained in the sample are portioned to two different water tanks and the two tanks are respectively filled by the two different water species, A1 (weakly restricted water species) and A2 (strongly restricted water species), as shown in Figure 15 (Konishi *et al.*, 2001). During the drying operation, the two water species in the tanks are simultaneously drained by regulating the two valves as mass transfer coefficient, *k*W (h-1) and *k*S (h-1).

Challenging Evaluation of the Hybrid Technique of

respectively. *De*0 (=

**of** *De*

decreasing *W0*.

Chemical Engineering – Proton NMR Technique for Food Engineering 83

evaluated by a curve fitting simulation between the experimental and the calculated curves as shown in Fig. 10. The curve fitting obtained showed favorable agreement with the

**5.2.4 Discrimination of water species A1 and A2 due to the pre-exponential factor (***De***0**

The pre-exponential factor, *De*0, can easily be evaluated by the extrapolation of the Arrhenius plot lines against the perpendicular axes in Fig. 13. Figure 16(A) illustrates *De*0 as a function of *W*. The *De*0 values obtained were of identical value regardless of *W* in the water species A1 region, whereas in the water species A2 region, there was a steep increase with

*Species A2 Species A1*

0 100 200 300

Fig. 16. Pre-exponential factor (*De*0) and the dehydration parameters for the computer

Figure 16(B) illustrates *k*S and *k*W as a function of *W0*. The two values show tendencies similar to *De*0 even though they can be evaluated by the computer simulation. Comparing the two values between *k*S (or *k*W) and *De*0, one can generate a linear relation between the two values as shown in Figure 17. The parameters of *k*S and *k*W roughly demonstrate the equations of *k*S = 0.025(0.008) *De*0 and *k*W = 0.007(0.004) *De*0. Using Equations (8) and (9), one may evaluate β1 = 0.025(0.008) and β2 = 0.007(0.004). Concerning the physical meaning of β1 and β2, since the two parameters were evaluated by the computer fitting simulation, they seem to be meaningless. This evidence indicates a disadvantage of the chemical engineering technique, even though the linear relation between *k*S (or *k*W) and *De*0 would

*W* 0 (%-d.b.)

1 and

*De* **0**

**(A)**

**(B)**

k *S* k*W* 2 are the constants

**)**

*D*0) is the pre-exponential factor of *De*, and

experimental dehydration curves as shown in Fig. 10 by solid curves.

100

simulation (*k*S, *k*W) as a function of *W*0 (*T*D = 45°C) for the salmon.

cause the presumption of a relationship between the two parameters.

1000

*ks or kw* (h-1)

*De0, Apparent pre-exponential*

*factor*

10000

1.E+05

1.E+06

1.E+07

1.E+04

Fig. 15. Schematic explanation of the water tank model proposed as a dehydration of food.

On the material balance equation in the course of the dehydration operation presented in Fig. 10, the water content (*W*), presented as a perpendicular axis of Fig. 10, can be replaced by the water ratio, *W*R = (*W-We)*/(*W*D-*We*), where *We* is the equilibrium water content (% d.b.) and *W*D is the initial water content of the sample. The dynamism of *W*R for the two tanks is expressed by Equation (4).

$$\left(-\frac{d\mathcal{W}\_{RI}}{dt}\right) = \left(-\frac{d\mathcal{W}\_{Rw}}{dt}\right) + \left(-\frac{d\mathcal{W}\_{Rs}}{dt}\right) \tag{4}$$

$$-\frac{d\mathcal{W}\_{Rw}}{dt} = k\_w \cdot \mathcal{W}\_{Rw} \tag{5}$$

$$-\frac{d\mathcal{W}\_{\rm Rs}}{dt} = k\_s \cdot \mathcal{W}\_{\rm Rs} \quad \text{\(\(\prime\)}\tag{6}$$

where *W*RW is the ratio of the weakly restricted water species (=(*W*W-*We*) /(*W*- *W*D)), *W*RS is the ratio of the strongly restricted water species (=(*W*S-*We*) /(*W*- *W*D)), *W*W is the amount of weakly restricted water species (%-d.b.), and *W*S is the amount of strongly restricted water species (%-d.b.).

The differential equation (4) is easily solved and expressed by Equation (7).

$$\begin{split} \mathcal{W}\_{R} &= \frac{\mathcal{W} - \mathcal{W}e}{\mathcal{W}\_{\text{D}} - \mathcal{W}e} = \mathcal{W}\_{\text{R1}} \exp\left[k\_{s} \exp\left(\frac{-E\_{\text{D}}}{R \cdot \left(T\_{\text{D}} + 2\mathcal{T}\right)}\right) \cdot t\right] \\ &+ \mathcal{W}\_{\text{R2}} \exp\left[k\_{w} \exp\left(\frac{-E\_{\text{D}}}{R \cdot \left(T\_{\text{D}} + 2\mathcal{T}\right)}\right) \cdot t\right] \end{split} \tag{7}$$

$$k\_s = \beta\_1 \cdot De^0 \tag{8}$$

$$k\_w = \beta\_2 \cdot De^0 \,, \tag{9}$$

where *W*R1 and *W*R2 are a proportion of the strongly restricted water amount and the weakly restricted water amount, respectively, and *k*S (h-1) and *k*W (h-1) are the mass transfer coefficient of the strongly restricted water species and of the weakly restricted water species,

Fig. 15. Schematic explanation of the water tank model proposed as a dehydration of food.

tanks is expressed by Equation (4).

species (%-d.b.).

On the material balance equation in the course of the dehydration operation presented in Fig. 10, the water content (*W*), presented as a perpendicular axis of Fig. 10, can be replaced by the water ratio, *W*R = (*W-We)*/(*W*D-*We*), where *We* is the equilibrium water content (% d.b.) and *W*D is the initial water content of the sample. The dynamism of *W*R for the two

> *RI Rw Rs dW dW dW dt dt dt*

> > *w Rw*

*s Rs*

*Rw*

*Rs*

The differential equation (4) is easily solved and expressed by Equation (7).

*R R s*

*R w*

2

*D*

1

*dW k W*

*dW k W*

where *W*RW is the ratio of the weakly restricted water species (=(*W*W-*We*) /(*W*- *W*D)), *W*RS is the ratio of the strongly restricted water species (=(*W*S-*We*) /(*W*- *W*D)), *W*W is the amount of weakly restricted water species (%-d.b.), and *W*S is the amount of strongly restricted water

exp exp 273

where *W*R1 and *W*R2 are a proportion of the strongly restricted water amount and the weakly restricted water amount, respectively, and *k*S (h-1) and *k*W (h-1) are the mass transfer coefficient of the strongly restricted water species and of the weakly restricted water species,

*E W k t R T*

*W We E W Wk t W We R T*

(4)

*dt* (5)

*dt* , (6)

*s* 1 *k β De* (8)

*w* 2 *k β De* , (9)

(7)

D

D

D

0

0

D

exp exp 273

respectively. *De*0 (= *D*0) is the pre-exponential factor of *De*, and 1 and 2 are the constants evaluated by a curve fitting simulation between the experimental and the calculated curves as shown in Fig. 10. The curve fitting obtained showed favorable agreement with the experimental dehydration curves as shown in Fig. 10 by solid curves.

#### **5.2.4 Discrimination of water species A1 and A2 due to the pre-exponential factor (***De***0 ) of** *De*

The pre-exponential factor, *De*0, can easily be evaluated by the extrapolation of the Arrhenius plot lines against the perpendicular axes in Fig. 13. Figure 16(A) illustrates *De*0 as a function of *W*. The *De*0 values obtained were of identical value regardless of *W* in the water species A1 region, whereas in the water species A2 region, there was a steep increase with decreasing *W0*.

Fig. 16. Pre-exponential factor (*De*0) and the dehydration parameters for the computer simulation (*k*S, *k*W) as a function of *W*0 (*T*D = 45°C) for the salmon.

Figure 16(B) illustrates *k*S and *k*W as a function of *W0*. The two values show tendencies similar to *De*0 even though they can be evaluated by the computer simulation. Comparing the two values between *k*S (or *k*W) and *De*0, one can generate a linear relation between the two values as shown in Figure 17. The parameters of *k*S and *k*W roughly demonstrate the equations of *k*S = 0.025(0.008) *De*0 and *k*W = 0.007(0.004) *De*0. Using Equations (8) and (9), one may evaluate β1 = 0.025(0.008) and β2 = 0.007(0.004). Concerning the physical meaning of β1 and β2, since the two parameters were evaluated by the computer fitting simulation, they seem to be meaningless. This evidence indicates a disadvantage of the chemical engineering technique, even though the linear relation between *k*S (or *k*W) and *De*0 would cause the presumption of a relationship between the two parameters.

Challenging Evaluation of the Hybrid Technique of

upcoming section.

next section.

Chemical Engineering – Proton NMR Technique for Food Engineering 85

indicating an identical *De* = 2.2(±0.2)×10-6 m2/h in the species A1 region and *De* = 3.5(±1.0) ×10-7 m2/h in the water species A2 region. All the *De* values for the commercial jerkies are distributed in the water species A2 region, indicating an identical *De* = 3.0(±1.8)×10-7 m2/h, which, even though the data are widely scattered, is the same as PH. Regarding the jerky data, although all the samples distributed in the market commonly use the water species A2 not A1 and the effective diffusivity, *De*, falling in the range of 1.0~5.0×10-7 m2/h, C is widely distributed in the range of 10-8 ~ 1.1×10-7 s. Since each of the jerkies used in this study has own taste different from other, the wide distribution of C has an important meaning in developing different tastes in jerky. This idea is also supported in an

As has been demonstrated in Fig. 12, the *De*~*W*0 plots could not clearly discriminate the water species A1 and A2. The *De*~*τC* plots derived from the hybrid technique, on the other hand, are conveniently able to visualize the discrimination between beef and pork and the two water species, A1 and A2, as shown in Figure 19. In particular, the boundary of the two water species is clearly recognized as C*τC* =10-8 s, indicating an advantage of the hybrid technique. Focusing on the behavior of BA, BH, and PH, the *De* values gave a constant value in the two water species regions: in the A1 region, as 3.1(±0.5) ×10-6, 4.7(±0.1) ×10-6, and 2.2(±0.3) ×10-6 m2/h, respectively; in the A2 region, as 1.8(±0.5) ×10-6, 2.0(±0.4) ×10-6, and 7.0(±1.0) ×10-7 m2/h, respectively, even though *τC* is significantly changed from 10-8 to 4.5×10-8 s. These results suggest the water species A2 has the interesting characteristic of being able to make a wide shift of the molecular mobility, *τC*, without changing in the *De* values. This result indicates that, by changing the value of *τC* as a design parameter for food products, the character of various meats can be significantly altered. The hardness of various meats and salmon, for example, can also be controlled by a change in *τC*, as shown in the

**6.2 Clear discrimination of the two water species due to diffusivity (***De***)**

1.0E-08

0.0E+00 2.0E-08 4.0E-08

Fig. 19. Comparing behavior of *De* among BA, BH, and PH as a function of τC at 70C.

*Species A1 Species A2*

*τ* C(s)

○

△

□

B A

B H

P H

0.0E+00

1.0E-06

2.0E-06

*De*

(m2/h)

3.0E-06

4.0E-06

5.0E-06

Fig. 17. Dehydration parameters (*k*s and *k*w) as a function of pre-exponential factor (*De*0)(*T*D=45°C) for the salmon.

#### **6. Advantages derived from the hybrid technique**

#### **6.1 Visualized correlation between molecular mobility (C) and physicochemical mobility (***De***)**

Physicochemical mobility, *De*, which is evaluated by the chemical engineering technique (Fig. 12), and molecular mobility, C, which is evaluated by the proton NMR technique (Fig. 8), are important parameters for characterizing the water species retained in foods as the diffusion rate in the three dimensional space of the food tissue matrix and as the rotation time of water molecules, respectively. The two parameters (*De* and C) for the pork meat (PH) and 12 jerkies are interestingly related, as shown in Figure 18. This clearly shows an advantage of the hybrid technique. *De* for PH demonstrates a drastic change at C = 10-8 s,

Fig. 18. *De* as a function of τC for jerkies and PH.

1.E+04 1.E+05 1.E+06 1.E+07 *De* 0 '

Fig. 17. Dehydration parameters (*k*s and *k*w) as a function of pre-exponential factor

**6.1 Visualized correlation between molecular mobility (C) and physicochemical**

Physicochemical mobility, *De*, which is evaluated by the chemical engineering technique (Fig. 12), and molecular mobility, C, which is evaluated by the proton NMR technique (Fig. 8), are important parameters for characterizing the water species retained in foods as the diffusion rate in the three dimensional space of the food tissue matrix and as the rotation time of water molecules, respectively. The two parameters (*De* and C) for the pork meat (PH) and 12 jerkies are interestingly related, as shown in Figure 18. This clearly shows an advantage of the hybrid technique. *De* for PH demonstrates a drastic change at C = 10-8 s,

PJ-4

BJ-4 BJ-6

0.0E+00 5.0E-08 1.0E-07

*τ* C (s)

PJ-3

0.0E+00

Fig. 18. *De* as a function of τC for jerkies and PH.

2.0E-07

4.0E-07

6.0E-07

*De* (m2/h) 8.0E-07

1.8E-06

1.0E-06

2.0E-06

1.2E-06

2.2E-06

PJ-5 PJ-6

BJ-2 BJ-3

*Species A1 Species A2*

BJ-1 BJ-5

PJ-1 PJ-2

Reference-PH

k *S* k*W*

1.E+01

**6. Advantages derived from the hybrid technique**

1.E+02

1.E+03

*ks* or *kw*(h-1)

(*De*0)(*T*D=45°C) for the salmon.

**mobility (***De***)**

1.E+04

1.E+05

indicating an identical *De* = 2.2(±0.2)×10-6 m2/h in the species A1 region and *De* = 3.5(±1.0) ×10-7 m2/h in the water species A2 region. All the *De* values for the commercial jerkies are distributed in the water species A2 region, indicating an identical *De* = 3.0(±1.8)×10-7 m2/h, which, even though the data are widely scattered, is the same as PH. Regarding the jerky data, although all the samples distributed in the market commonly use the water species A2 not A1 and the effective diffusivity, *De*, falling in the range of 1.0~5.0×10-7 m2/h, C is widely distributed in the range of 10-8 ~ 1.1×10-7 s. Since each of the jerkies used in this study has own taste different from other, the wide distribution of C has an important meaning in developing different tastes in jerky. This idea is also supported in an upcoming section.

#### **6.2 Clear discrimination of the two water species due to diffusivity (***De***)**

As has been demonstrated in Fig. 12, the *De*~*W*0 plots could not clearly discriminate the water species A1 and A2. The *De*~*τC* plots derived from the hybrid technique, on the other hand, are conveniently able to visualize the discrimination between beef and pork and the two water species, A1 and A2, as shown in Figure 19. In particular, the boundary of the two water species is clearly recognized as C*τC* =10-8 s, indicating an advantage of the hybrid technique. Focusing on the behavior of BA, BH, and PH, the *De* values gave a constant value in the two water species regions: in the A1 region, as 3.1(±0.5) ×10-6, 4.7(±0.1) ×10-6, and 2.2(±0.3) ×10-6 m2/h, respectively; in the A2 region, as 1.8(±0.5) ×10-6, 2.0(±0.4) ×10-6, and 7.0(±1.0) ×10-7 m2/h, respectively, even though *τC* is significantly changed from 10-8 to 4.5×10-8 s. These results suggest the water species A2 has the interesting characteristic of being able to make a wide shift of the molecular mobility, *τC*, without changing in the *De* values. This result indicates that, by changing the value of *τC* as a design parameter for food products, the character of various meats can be significantly altered. The hardness of various meats and salmon, for example, can also be controlled by a change in *τC*, as shown in the next section.

Fig. 19. Comparing behavior of *De* among BA, BH, and PH as a function of τC at 70C.

Challenging Evaluation of the Hybrid Technique of

labyrinth factor, and ΔS is activation entropy.

0.0E+00

5.0E-04

*δD*

0(m2 h) /

1.0E-03

1.5E-03

2.0E-03

0

a function of C.

The *δD*0 ~

demonstrated a drastic reduction at C

Chemical Engineering – Proton NMR Technique for Food Engineering 87

χ, and ∆S. Since the *δD*0 value can be evaluated from the extrapolation of the Arrhenius plots against the perpendicular axes in Fig. 13, those evaluated for BA, BH, and PH can be plotted as a function of *τC*, as shown in Figure 21. Three *δD*0 curves obtained demonstrate a steep decay at the C*τC* = 10-8 s. Based on the rough evaluation of the steep decay for the three curves to be 1/30 ~1/150, the reduction of the *δD*0 values should be attributed to a simultaneous change in the ε value, which becomes smaller while the χ value becomes larger and the ∆S value becomes smaller because of the steep change in the physical structure of the BA, BH, and PH's tissue matrix at the C*τC* = 10-8 s derived from dehydration.

where *k* is Boltzmann constant, *h* is Planck constant, *δ*(=*ε*/*χ*) is diffusibility, *ε* is porosity, *χ* is

*Species A1 Species A2*

*E k T S E De D exp exp exp R T h R RT*

D D 273

D D D

0.0E+00 1.0E-08 2.0E-08 3.0E-08 4.0E-08 τC (s)

*C* plots again demonstrate the hybrid technique to be a useful tool for

*C* in the water species A2 region. This reduction of *E*D

Fig. 21. Comparing behavior of the pre-exponential factor between BA, BH, and PH at 50C as

Figure 22 illustrates the optical microscope photographs of the cross-section for BH. Comparing Figs. 22(A) and (B), one can clearly recognize that a large number of pores (complicated structures) appeared in the sample of *W*0 = 54%-d.b. rather than that of *W*0 = 127%-d.b., indicating a growth of the labyrinth factor (*χ*). This growth of *χ* contributes to the reduction of *δ* and is supported by the experimental evidences of the steep reductions of

discriminating between the two water species and among the kind of foods. The drastic change of *δD*0 should derive the change of water species from A1 to A2, suggesting the diffusion mechanism change between the two regions. This idea is strongly supported by experimental evidence as shown in Figure 23. All *E*D's of water species A2 for BA, BH, and PH

strongly suggests a change in the diffusion mechanism from the water species A1 region.

both *δD*0 in Fig. 21 and *De* in Fig. 19 in the water species A2 region.

273 273

○ *BA* △ *B H* □ *P H*

(10)

#### **6.3 Clear discrimination of the two water species by hardness**

Figure 20 demonstrates the hardness of BA, BH, PH, and salmon as a function of *τC*. The hardness of all the samples shows a drastic increase at C*τC* = 10-8 s with increasing *τC* in the water species A2 region. This clear discrimination between the water species A1 and A2 is an advantage of the hybrid technique. One may recognize that the *N*P value in the water species A1 region commonly showed 1.0(±0.5) ×106 N/m2 regardless of *τC* in all the samples, whereas in the water species A2 region and at the range of *τC* = 3×10-8 ~ 6×10-8 s, it varied widely depending on the samples, as with *N*P = 1.5×107 for BA, 1.3×107 for BH, and 1×107 N/m2 for PH at *τC* = 5.0×10-8 s. From these results, one can recognize that it is possible to design food products with different *N*P values without changing the *τC* values, and it's possible to design products with different *τC* values without changing the *N*P values. Keeping *N*P = 1.0×107 N/m2, for example, one can have different *τC* values such as *τC* = 2.9×10-8 s for BA, *τC* = 3.5×10-8 s for BH, and *τC* = 4.8×10-8 s for PH. Although the physical meaning of this *τC* value shift is still unclear, it can be attributed to the taste difference between BA, BH, and PH because concrete evidence that the *τC* value changes depending on the kind of food, the concentration of seasonings, and the kind of seasonings has been obtained. Details of these results will be reported in an upcoming work (Konishi and Kobayashi, 2011).

The visualization of *N*P ~*τC* exhibited in Fig. 20 clearly demonstrates the hybrid technique as a useful tool for discriminating between the two water species and among the kinds of food.

Fig. 20. Comparing behavior of *N*P as a function of C between BA, BH, and PH at 50C.

#### **6.4 Physical meaning of CC = 10-8 s derived from the pre-exponential factor (***De***0 ) of** *De*

Focusing on the boundary (C*τC* = 10-8 s) between water species A1 and A2, one may be interested in what is happening in the food tissue matrix at the C*τC* = 10-8 s. The information for the food tissue matrix can be obtained from Equation (2), and the equation is rewritten by Equation (10). The pre-exponential factor (*δD*0) of Equation (10) consists of a function of ε,

Figure 20 demonstrates the hardness of BA, BH, PH, and salmon as a function of *τC*. The hardness of all the samples shows a drastic increase at C*τC* = 10-8 s with increasing *τC* in the water species A2 region. This clear discrimination between the water species A1 and A2 is an advantage of the hybrid technique. One may recognize that the *N*P value in the water species A1 region commonly showed 1.0(±0.5) ×106 N/m2 regardless of *τC* in all the samples, whereas in the water species A2 region and at the range of *τC* = 3×10-8 ~ 6×10-8 s, it varied widely depending on the samples, as with *N*P = 1.5×107 for BA, 1.3×107 for BH, and 1×107 N/m2 for PH at *τC* = 5.0×10-8 s. From these results, one can recognize that it is possible to design food products with different *N*P values without changing the *τC* values, and it's possible to design products with different *τC* values without changing the *N*P values. Keeping *N*P = 1.0×107 N/m2, for example, one can have different *τC* values such as *τC* = 2.9×10-8 s for BA, *τC* = 3.5×10-8 s for BH, and *τC* = 4.8×10-8 s for PH. Although the physical meaning of this *τC* value shift is still unclear, it can be attributed to the taste difference between BA, BH, and PH because concrete evidence that the *τC* value changes depending on the kind of food, the concentration of seasonings, and the kind of seasonings has been obtained. Details of these results will be reported in an upcoming work (Konishi and

The visualization of *N*P ~*τC* exhibited in Fig. 20 clearly demonstrates the hybrid technique as a useful tool for discriminating between the two water species and among the kinds of food.

*Species A1 Species A2*

0.0E+00 2.0E-08 4.0E-08 6.0E-08 *τ* C (s)

Fig. 20. Comparing behavior of *N*P as a function of C between BA, BH, and PH at 50C.

**6.4 Physical meaning of CC = 10-8 s derived from the pre-exponential factor (***De***0**

Focusing on the boundary (C*τC* = 10-8 s) between water species A1 and A2, one may be interested in what is happening in the food tissue matrix at the C*τC* = 10-8 s. The information for the food tissue matrix can be obtained from Equation (2), and the equation is rewritten by Equation (10). The pre-exponential factor (*δD*0) of Equation (10) consists of a function of ε,

○ *BA* △ *B H* □ *P H Salmon*

**) of** *De*

**6.3 Clear discrimination of the two water species by hardness**

Kobayashi, 2011).

0.0E+00

5.0E+06

1.0E+07

*N*P(N/m2

)

1.5E+07

χ, and ∆S. Since the *δD*0 value can be evaluated from the extrapolation of the Arrhenius plots against the perpendicular axes in Fig. 13, those evaluated for BA, BH, and PH can be plotted as a function of *τC*, as shown in Figure 21. Three *δD*0 curves obtained demonstrate a steep decay at the C*τC* = 10-8 s. Based on the rough evaluation of the steep decay for the three curves to be 1/30 ~1/150, the reduction of the *δD*0 values should be attributed to a simultaneous change in the ε value, which becomes smaller while the χ value becomes larger and the ∆S value becomes smaller because of the steep change in the physical structure of the BA, BH, and PH's tissue matrix at the C*τC* = 10-8 s derived from dehydration.

$$De = \delta D\_0 \cdot \exp\left(\frac{-E\_\mathrm{D}}{R \cdot \left(T\_\mathrm{D} + 273\right)}\right) = \left(\frac{\varepsilon}{\chi}\right) \cdot \left(\frac{k \cdot \left(T\_\mathrm{D} + 273\right)}{h}\right) \left[\exp\left(\frac{\Delta S}{R}\right)\right] \cdot \exp\left(\frac{-E\_\mathrm{D}}{R \cdot \left(T\_\mathrm{D} + 273\right)}\right) \tag{10}$$

where *k* is Boltzmann constant, *h* is Planck constant, *δ*(=*ε*/*χ*) is diffusibility, *ε* is porosity, *χ* is labyrinth factor, and ΔS is activation entropy.

Fig. 21. Comparing behavior of the pre-exponential factor between BA, BH, and PH at 50C as a function of C.

Figure 22 illustrates the optical microscope photographs of the cross-section for BH. Comparing Figs. 22(A) and (B), one can clearly recognize that a large number of pores (complicated structures) appeared in the sample of *W*0 = 54%-d.b. rather than that of *W*0 = 127%-d.b., indicating a growth of the labyrinth factor (*χ*). This growth of *χ* contributes to the reduction of *δ* and is supported by the experimental evidences of the steep reductions of both *δD*0 in Fig. 21 and *De* in Fig. 19 in the water species A2 region.

The *δD*0 ~ *C* plots again demonstrate the hybrid technique to be a useful tool for discriminating between the two water species and among the kind of foods. The drastic change of *δD*0 should derive the change of water species from A1 to A2, suggesting the diffusion mechanism change between the two regions. This idea is strongly supported by experimental evidence as shown in Figure 23. All *E*D's of water species A2 for BA, BH, and PH demonstrated a drastic reduction at C*C* in the water species A2 region. This reduction of *E*D strongly suggests a change in the diffusion mechanism from the water species A1 region.

Challenging Evaluation of the Hybrid Technique of

values accompanied by drastic increases or decreases.

food products requested by the commercial fields.

BJ-3

0.4

0.5

0.6

0.7

aw (-)

**8. Conclusions**

reproducible.

*N*P, and *W*0 .

0.8

0.9

1.0

PJ-5

Fig. 24. aw as a function of τC for the beef and pork jerkies.

BJ-2 BJ-1

*Group-3*

PJ-6

BJ-4

PJ-4

BJ-6

BJ-5

PJ-2

*Group-2*

*Group-1*

PJ-1

0.0E+00 5.0E-08 1.0E-07 1.5E-07 C (s)

The hybrid of the proton NMR technique and the chemical engineering technique was applied to discriminate the 12 commercially distributed jerkies, salmon, BA, BH, and PH and to distinguish between the two water species, A1 and A2, by using four parameters: *De*, *τ*C,

1. The dehydration and adsorption of the water species, A1 and A2, retained in the foods were reversibly repeated and the transformation between the two water species was

2. The proton NMR technique and the chemical engineering technique could respectively not discriminate among the *τ*C ~ *W*0 curves and among the aw ~ *W*0 curves obtained for the foods. Although the computer simulation model proposed by the chemical engineering technique generated a good fitting against the dehydration response

Chemical Engineering – Proton NMR Technique for Food Engineering 89

A1 to A2 and the pore structure of the food tissue matrix provides regulation of ε and χ

Focusing on the three straight lines in Fig. 24, line-1 generates Group-1 (BJ-5 and PJ-2), line-2, Group-2 (BJ-4, BJ-1, PJ-4, BJ-6, and PJ-1), and line-3, Group-3 (PJ-5, BJ-3, BJ-2, and PJ-6). Based on the experimental data presented here, although one cannot demonstrate an exact physical meaning of the three groups' difference, it might be presumed that each group should have the specified seasonings different from others. Concerning the seasonings in the three groups, Group-1 is characterized by soy sauce, Group-2, by a mixed seasoning such as soy sauce, smoke flavoring, and salt, and Group-3, by a mixed seasoning such as salt, soysauce, and miso sauce. Although each of the three groups could not be distinguished by the chemical engineering (the aw ~ *W*0 curves and the *De* ~ *W*0 curves) and proton NMR techniques (the C ~ *W*0 curves), the hybrid technique (the aw ~ *τ*C linear relation) clearly distinguished the characteristics of the seasonings for the 12 different food products. This evidence strongly demonstrates the hybrid technique to be a useful tool to design various

Fig. 22. Optical microscope photographs (× 1000) of the cross-section for PH, (A): 127%-d.b., (b): 54%-d.b.

Fig. 23. Comparing behavior of *E*D among BA, BH, and PH as a function of C at 50~70C.

#### **7. A new method for jerky product design using the parameters induced by the hybrid technique**

Our interest is focused on a classification of the 12 jerky products influenced by multifunctional water species. Hills *et al.* (1999) empirically demonstrated a linear relation between the NMR relaxation rates (1/*T*2) and aw. In addition, Hills (1999) derived a theoretical equation to explain the linear relation. Since the relaxation time, *T*2, can be replaced by C using Equation (3), it is proposed that aw should be related to C. Figure 24 demonstrates aw as a function of C for the 12 jerky samples. All the samples fell roughly around three straight lines, indicating the existing of three groups. The three lines started from aw = 0.92, which is an identical value independent of the samples and empirically evaluated, meaning a specified value for all kinds of foods (Konishi and Kobayashi, 2010). The aw = 0.92 value always gave C = 10-8 s independent of the kinds of foods and seasoning species. As has been discussed in the previous section, one can recognize both aw = 0.92 and C = 10-8 s to be critical values for foods in which the water species drastically changes from A1 to A2 and the pore structure of the food tissue matrix provides regulation of ε and χ values accompanied by drastic increases or decreases.

Focusing on the three straight lines in Fig. 24, line-1 generates Group-1 (BJ-5 and PJ-2), line-2, Group-2 (BJ-4, BJ-1, PJ-4, BJ-6, and PJ-1), and line-3, Group-3 (PJ-5, BJ-3, BJ-2, and PJ-6). Based on the experimental data presented here, although one cannot demonstrate an exact physical meaning of the three groups' difference, it might be presumed that each group should have the specified seasonings different from others. Concerning the seasonings in the three groups, Group-1 is characterized by soy sauce, Group-2, by a mixed seasoning such as soy sauce, smoke flavoring, and salt, and Group-3, by a mixed seasoning such as salt, soysauce, and miso sauce. Although each of the three groups could not be distinguished by the chemical engineering (the aw ~ *W*0 curves and the *De* ~ *W*0 curves) and proton NMR techniques (the C ~ *W*0 curves), the hybrid technique (the aw ~ *τ*C linear relation) clearly distinguished the characteristics of the seasonings for the 12 different food products. This evidence strongly demonstrates the hybrid technique to be a useful tool to design various food products requested by the commercial fields.

Fig. 24. aw as a function of τC for the beef and pork jerkies.

#### **8. Conclusions**

88 Advances in Chemical Engineering

(A) (B)

Fig. 22. Optical microscope photographs (× 1000) of the cross-section for PH, (A): 127%-d.b.,

○ *BA* △ *B H* □ *P H*

*Species A1 Species A2*

0.0E+00 2.0E-08 4.0E-08 6.0E-08 τC (s)

Fig. 23. Comparing behavior of *E*D among BA, BH, and PH as a function of C at 50~70C.

**7. A new method for jerky product design using the parameters induced by**

Our interest is focused on a classification of the 12 jerky products influenced by multifunctional water species. Hills *et al.* (1999) empirically demonstrated a linear relation between the NMR relaxation rates (1/*T*2) and aw. In addition, Hills (1999) derived a theoretical equation to explain the linear relation. Since the relaxation time, *T*2, can be replaced by C using Equation (3), it is proposed that aw should be related to C. Figure 24 demonstrates aw as a function of C for the 12 jerky samples. All the samples fell roughly around three straight lines, indicating the existing of three groups. The three lines started from aw = 0.92, which is an identical value independent of the samples and empirically evaluated, meaning a specified value for all kinds of foods (Konishi and Kobayashi, 2010). The aw = 0.92 value always gave C = 10-8 s independent of the kinds of foods and seasoning species. As has been discussed in the previous section, one can recognize both aw = 0.92 and C = 10-8 s to be critical values for foods in which the water species drastically changes from

(b): 54%-d.b.

0

5

10

*E*

**the hybrid technique**

D(kJ/mol)

15

20

The hybrid of the proton NMR technique and the chemical engineering technique was applied to discriminate the 12 commercially distributed jerkies, salmon, BA, BH, and PH and to distinguish between the two water species, A1 and A2, by using four parameters: *De*, *τ*C, *N*P, and *W*0 .

Challenging Evaluation of the Hybrid Technique of

*ε porosity of the food tissue* (-)

**Greek letters**

*C*

analysis.

**11. References**

349.

Chemistry.

*WR the water ratio* (= (W-We)/(WD-We))

*WR1 the proportion of the strongly restricted water amount* (-) *WR2 the proportion of the weakly restricted water amount* (-) *WRS the ratio of the strongly restricted water species* (-) *WRW the ratio of the weakly restricted water species* (-) *WS the amount of strongly restricted water species* (%-d.b.) *WW the amount of weakly restricted water species* (%-d.b.)

*β1 constant evaluated by the curve fitting simulation* (m-2) *β2 constant evaluated by the curve fitting simulation* (m-2)

 *gyromagnetic ratio of proton* (=2.675108 radT-1s-1)

*ħ modified Plank's constant* (=6.6310-34 Js)

*C critical correlation time of water proton* (s)

*0 resonance frequency* (=3.14109 s-1)

*C correlation time of water proton* (s)

*χ labyrinth factor of the meat tissue* (-)

*δ diffusibility* (=ε /χ)(-)

*u.-Technol.*, 19 , 448-456.

**10. Acknowledgment**

*π the ratio of the circumference of a circle to its diameter* (=3.14)

Chemical Engineering – Proton NMR Technique for Food Engineering 91

This work was financially supported by the Cooperation of Innovation Technology and Advanced Research in Evolution Area (City Area) from the Japanese Ministry of Education, Culture, Sports, Science, and Technology. The authors wish to thank Associate Professor Koichi Miura, Kitami Institute of Technology, for his assistance with the proton NMR

Abragam, A., (1963). The Principles of Nuclear Magnetism. *Oxford at the Clarend Press*, p347-

Andreu, J. & Stamatopoulos, A.A. (1986). Durum wheat pasta drying kinetics. *Lebensm. Wiss.*

Belton, P.S.; Gil, A.M.; Webb, G.A., & Rutledge, D., Eds.(2003). Magnetic resonance in food science-latest developments, *The Royal Society of Chemistry, Cambridge*. Chhinnan, M.S. (1984). Evaluation of selected mathematical models for describing thin-layer

Hills, B.P. (1999). NMR studies of water mobility in foods. In"Water Management in the

Hills, B. P., Manning, C. E. & Godward, J. (1999). A multistate theory of water relations in

Design and Distribution of Quality Foods" ISOPOW 7(Y.H. Roos, R.B. Leslie, and

biopolymer systems. In: *Advances in Magnetic Resonance in Food Science* (edited by P. S. Belton, B. P. Hill and G. A. Webb). Pp.45-62. Cambridge, UK: Royal Society of

drying of in-shell pecans. *Trans. ASAE*, 27(2), 610-615.

P.J.Lillford, eds) Technomic Publishing, Lancaster, PA.

Fennema, O.(1976). In principles of food science, Part 1, Marcel Dekker, New York.

curves, the unknown parameters evaluated by the curve fitting, 1 and 2, were recognized as meaningless.

#### **9. Nomenclature**

#### **Greek letters**

90 Advances in Chemical Engineering

3. The hybrid technique proposed demonstrates a clear discrimination between water species A1 and A2, divided at the critical value of C*τ*C = 10-8 s, where the drastic change in the values of *De*, *E*D, and *N*P appeared. The physical meaning of C*τ*C was understood as a drastic change in the pre-exponential factor of *De* derived from the change in the

4. Using the hybrid technique, a variety of food products could easily be designed by changing the value of *τ*C even though the values of aw are identical. The meat jerkies commercially distributed in Japan were reasonably classified into three groups characterized by the three aw ~ *τ*C straight lines. The advantage of the hybrid technique brought a conclusion that the aw ~ *τ*C straight lines give characteristic slopes depending on the seasonings of the 12 jerkies. This could be useful in designing a variety of food

1 and 2, were

curves, the unknown parameters evaluated by the curve fitting,

recognized as meaningless.

products.

**9. Nomenclature**

food tissue matrix due to dehydration.

*BA beef meat produced in Australia* (-) *BH beef meat produced in Hokkaido, Japan* (-) *D moisture diffusion coefficient* (m2/h) *D0 frequency factor of D* (m2/h)

*H Planck constant* (erg. s)

*K Boltzmann constant* (erg/deg)

*Mt magnitude of magnetization vector* (-)

*R gas constant* (=8.314J/K·mol)

*ΔS activation entropy* (kJ/mol)

*ts spin locking pulse length* (s)

*TD drying temperature* (C)

*t drying time* (h)

*NP hardness of meat products* (Newton/m2) *PH pork meat produced in Hokkaido, Japan* (-)

*T2 spin-spin relaxation time of water proton* (s)

*W water content at the drying time t* (%-d.b.)

*We equilibrium water content* (%-d.b.)

*De effective water diffusion coefficient* (m2/h) *De0 pre-exponential factor of De* (PF= D0 , m2/h) *ED activation energy of water diffusivity* (kJ/mol)

*I nuclear spin quantum number of water proton* (= 0.5) (--)

*kS mass transfer coefficient of strongly restricted water* (h-1) *kw mass transfer coefficient of weakly restricted water* (h-1) *La half distance of a-axis of the rectangular sample* (m) *Lb half distance of b-axis of the rectangular sample* (m) *Lc half distance of c-axis of the rectangular sample* (m)

*M0 magnitude of macroscopic equilibrium magnetisation vector* (-)

*r proton-proton distance of water molecule* (= 0.16 nm)

*W0 initial water content at the time of PUP operated* (%-d.b.) *WD initial water content of drying flesh sample* (%-d.b.)

#### **10. Acknowledgment**

This work was financially supported by the Cooperation of Innovation Technology and Advanced Research in Evolution Area (City Area) from the Japanese Ministry of Education, Culture, Sports, Science, and Technology. The authors wish to thank Associate Professor Koichi Miura, Kitami Institute of Technology, for his assistance with the proton NMR analysis.

#### **11. References**

**4**

*1Mexico 2UK*

**Modelling Approach for**

*2School of Electrical and Electronic Engineering*

**Redesign of Technical Processes**

*1Information Technology Laboratory, Cinvestav-Tamaulipas*

*University of Nottingham, University Park, Nottingham*

Ivan Lopez-Arevalo1, Victor Sosa-Sosa1 and Saul Lopez-Arevalo2

Nowadays the design and development of new products or modification of existent ones (redesign) is a key and fundamental element to enhance innovation and competitiveness of industrial companies. Design has an increasing importance to differentiate one product from

In general, design is the process of specifying a description of a product that satisfies a set of requirements (Umeda 90). Redesign is the process of changing the description of an existent product to satisfy a new set of requirements (Brown 98). Design engineering includes both design and redesign. In the literature we can find diverse terms to narrow design and redesign, such as preliminary, conceptual, functional, creative, routinary, non-routinary, personified, parametric, innovative, etc., but the characteristic activities of the global design

 Conceptual (re)design, the phase where the global goals, requirements and operation of the product are established based on abstract concepts. The research presented in this

engineering can be divided as follows [Subba-Rao 99], see Figure 1:

**1. Introduction**

Fig. 1. Product design path.

chapter deals with this aspect.

another.

## **Modelling Approach for Redesign of Technical Processes**

Ivan Lopez-Arevalo1, Victor Sosa-Sosa1 and Saul Lopez-Arevalo2 *1Information Technology Laboratory, Cinvestav-Tamaulipas 2School of Electrical and Electronic Engineering University of Nottingham, University Park, Nottingham 1Mexico*

*2UK*

#### **1. Introduction**

92 Advances in Chemical Engineering

Jason, A.C.(1958). A Study of evaporation and diffusion processes in the drying of fish

Kannan, D. and Bandyopadhyay, S. (1995). Drying characteristics of a tropical marine fish

Konishi, Y.; Horiuchi, J., & Kobayashi, M.(2001). Dynamic evaluation of the dehydration

Konishi, Y.; Horiuchi, J., & Kobayashi, M.(2001). Dynamic evaluation of the dehydration

Konishi, Y.;Miura, K. & Kobayashi, M.(2003). Drying efficiency design using multifunctional

Konishi, Y. & Kobayashi, M.(2009). Quantitative evaluation of the design parameters requested in beef and pork drying operation, *AIDIC Conference Series,* 9, 177-186. Konishi, Y., & Kobayashi, M., (2010). Food product design using the water species as a probe molecule. Annual meeting of Japanese Society of Food Engineering. Konishi, Y., Kobayashi, M., & Miura, K. (2010). Characeterization of water species revealed

analysis, *International Journal of Food Science and Technology.*, 45.1889-1894. Konishi, Y. & Kobayashi, M.(2011). Dynamism of the water species as a probe molecule in food, *Chemical Engineering Transactions*, Ed. Sauro Pierucci, 24, 475-480. Labuza, T.P., Tannenbaum, S.R., & Karel,M. (1970), Water Content and Stability of Low moisture and intermediate moisture foods. Food Technol.24, 543-550. Litchfield, J.B. & Okos, M.R. ,1992. Moisture diffusivity in pasta during drying. *J. Food*

Madamba, P. S., Driscoll, R. H. & Buckle, K. A.(1996). The thin-layer drying characteristics of

Noomhorm, A. & Verma, L.R.(1986). Generalized single layer rice drying models. *Trans.*

Rockland, L.B. and Stewart, G.F., Eds.(1981). International symposium on properties of water, water activity: Influences on food quality, *Academic Press Inc., London.*  Schmidt, S.J., Water and Solids Mobility in Foods, (2004). Advances in Food and Nutrition

Thompson, T.L., Peart, R.M. & Foster, G.H. (1968). Mathematical simulation of corn drying

Waananen, K.M. & Okos, M.R. (1996). Effect of porosity on moisture diffusion during

research, volume 48, 2004, Edited by S.L. Taylor, Elsevier

Chemical Industry. *McMillan, London*, pp.103-134.

squid, *AIDIC Conference series,* vol.6, 183-190.

slab. *J.Food Sci. Technol.*, 32(1), 13-16.

*Technology*, 19(7), 1253-1269

1271-1285.

*Engng.*, 17, 117-142.

*ASAE.*, 29(2), 587-591.

garlic slices. *J. Food Engng.*, 29, 75-97.

(A new model). *Trans.ASAE.*,24(3),582.

drying of pasta. *J. Food Engng.*, 28, 121-137.

muscle in fundamental aspects of dehydration of food stuffs, ed. Society of

response curves of foods characterized by a poultice-up process using a fish-paste sausage-I. Determination of the mechanisms for moisture transfer, *Drying*

response curves of foods characterized by a poultice-up process using a fish-paste sausage-II, A new tank model for a computer simulation, *Drying Technology*, 19(7),

dynamics of water molecules in foods-H-NMR analysis of a fish paste sausage and

in the drying operation of Todarodes pacificus Steenstrup using water proton NMR

Nowadays the design and development of new products or modification of existent ones (redesign) is a key and fundamental element to enhance innovation and competitiveness of industrial companies. Design has an increasing importance to differentiate one product from another.

In general, design is the process of specifying a description of a product that satisfies a set of requirements (Umeda 90). Redesign is the process of changing the description of an existent product to satisfy a new set of requirements (Brown 98). Design engineering includes both design and redesign. In the literature we can find diverse terms to narrow design and redesign, such as preliminary, conceptual, functional, creative, routinary, non-routinary, personified, parametric, innovative, etc., but the characteristic activities of the global design engineering can be divided as follows [Subba-Rao 99], see Figure 1:

Fig. 1. Product design path.

 Conceptual (re)design, the phase where the global goals, requirements and operation of the product are established based on abstract concepts. The research presented in this chapter deals with this aspect.

Modelling Approach for Redesign of Technical Processes 95

4. Human designers can understand the process intuitively identifying its functional

The chapter is organised as follows. In section 2 the contextualisation background about redesign and modelling is given. Section 3 describes the proposed modelling approach for redesign. Section 4 shows the experimentation carried out and the obtained results of a first prototype for the proposed approach. Finally, the section 5 presents some conclusions and

The research work related to the process of redesign is huge. Following the different research approaches are presented from a general point of view to more specific one. Some of the most interesting definitions of design that we have found in the literature are

Design is formally a search problem in a large space of objects that satisfy multiple

Design is the task of devising courses of action to change or create better ones (Simon

 Design starts with an intended activity or use (Maher 97b) and uses available knowledge to arrive at a description of an artefact which will produce those results

Defining design is difficult because the term refers both to a product (the object to be designed) and a process (the process of design). The reasoning process involved in design allows moving from a functional concept as a starting point to a product solution. Therefore, the design activity can be seen as an activity of synthesis, which is strongly influenced by

Regarding the process of redesign we have identified the following definitions in the literature: Redesign is considered as design in which there is a priori knowledge on the general and specialised functions to be performed and on the working principles to be selected

 Redesign is an inherent part of most design processes; in which new requirements or new domain knowledge influence the original design process (Brazier 96); but can also

Redesign is part of design, which proposes suitable modifications free for

Independently of the point of view of redesign, three types of redesign can be identified (Dixon 89): parametric redesign, component redesign, and structural redesign. In order to

2. Complex numerical simulators can be used to model the behaviour of the process. 3. The process is already implanted, which means there is a design solution that satisfies

1. The complexity of the process must be high.

the original requirements of such process.

5. The process can be represented by functional abstract concepts.

sections.

**2. Background**

summarised as:

96).

(Gero 90b).

[Salomons 95].

**2.1 The process of redesign**

constraints (Chandrasekaran 90).

the skills and mental models of the designer.

be seen as a family of design methods in itself (Pos 97).

inconvenience of existent artefacts (Kitamura 99).

remarks.

 Detailed (re)design, the phase where the results of the conceptual design are used to physically implement a product.

Design engineering involves a wide range of activities. Design engineering can appear in a broad variety of domains, from the assembly of brakes to complex industrial plants and from simple chips to the most advanced super computers. Both design and redesign consist of two main components: the (re)design process and the (re)design object. The (re)design process involves all the (re)design activities performed over the (re)design object, which is the subject entity to be (re)designed. In engineering domains is common to refer to the (re)design object as the artefact. An artefact is a type of product to denote physical and technical devices. The (re)design process over an artefact is characterised by the map between functional requirements to structural requirements. Design and redesign, both can be considered as a dialectic process between goals (what it is desired) and possibilities (real constraints), directed to the satisfaction of functional specifications and performance (Stephanopoulos 90b).

The industry deals with complex technical processes where their behaviour is only predicted by means of complex numerical simulators. The redesign of a process is sometimes necessary when certain time has passed from its implantation or when they must adapt to economical, technological, or environmental requirements. The redesign is not part of the maintenance stage but must be considered into the process life cycle.

From a general point of view, the redesign is done typically in three steps: design-description acquisition (modelling), problem analysis (diagnosis) and proposal of modifications (generation of alternatives). In real redesign situations, human designers intuitively create mental abstract models by removing superfluous information about the process. Such models are based on functions of the components of the process and its context.

From the early 60's, Artificial Intelligence techniques have been used for design, such as constraint-based systems, case-based reasoning, model-based reasoning, planning, neural networks, and genetic algorithms. Although in these approaches the modelling and simulation of the processes has been solved in acceptable way, another problem has been generated, the used knowledge representations require so detailed information that sometimes it is difficult to understand.

The main objective of this work is to obtain a support framework to assist the human designer in the redesign of complex technical processes. The structure of this framework must be based on the common redesign activities performed by human designers on real redesign situations. Therefore, the framework must able to reduce the complexity of the processes to be redesigned, and therefore facilitate the redesign activities.

The framework was obtained integrating model-based reasoning and case-based reasoning techniques. Using model-based reasoning the original process can be modelled hierarchically. Using case-based reasoning alternative process parts can be obtained from other processes, which have to be adapted into the original process. The framework was implemented in the Chemical Engineering domain due to the complexity of the processes involved and the interaction with experts in the area.

The proposed redesign framework has to be able to deal with complex technical processes. In this sense, the type of processes we are referring to follow some assumptions:

The chapter is organised as follows. In section 2 the contextualisation background about redesign and modelling is given. Section 3 describes the proposed modelling approach for redesign. Section 4 shows the experimentation carried out and the obtained results of a first prototype for the proposed approach. Finally, the section 5 presents some conclusions and remarks.

### **2. Background**

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Detailed (re)design, the phase where the results of the conceptual design are used to

Design engineering involves a wide range of activities. Design engineering can appear in a broad variety of domains, from the assembly of brakes to complex industrial plants and from simple chips to the most advanced super computers. Both design and redesign consist of two main components: the (re)design process and the (re)design object. The (re)design process involves all the (re)design activities performed over the (re)design object, which is the subject entity to be (re)designed. In engineering domains is common to refer to the (re)design object as the artefact. An artefact is a type of product to denote physical and technical devices. The (re)design process over an artefact is characterised by the map between functional requirements to structural requirements. Design and redesign, both can be considered as a dialectic process between goals (what it is desired) and possibilities (real constraints), directed to the satisfaction of functional specifications and performance

The industry deals with complex technical processes where their behaviour is only predicted by means of complex numerical simulators. The redesign of a process is sometimes necessary when certain time has passed from its implantation or when they must adapt to economical, technological, or environmental requirements. The redesign is not part

From a general point of view, the redesign is done typically in three steps: design-description acquisition (modelling), problem analysis (diagnosis) and proposal of modifications (generation of alternatives). In real redesign situations, human designers intuitively create mental abstract models by removing superfluous information about the process. Such models are based on functions of the components of the process and its context. From the early 60's, Artificial Intelligence techniques have been used for design, such as constraint-based systems, case-based reasoning, model-based reasoning, planning, neural networks, and genetic algorithms. Although in these approaches the modelling and simulation of the processes has been solved in acceptable way, another problem has been generated, the used knowledge representations require so detailed information that

The main objective of this work is to obtain a support framework to assist the human designer in the redesign of complex technical processes. The structure of this framework must be based on the common redesign activities performed by human designers on real redesign situations. Therefore, the framework must able to reduce the complexity of the

The framework was obtained integrating model-based reasoning and case-based reasoning techniques. Using model-based reasoning the original process can be modelled hierarchically. Using case-based reasoning alternative process parts can be obtained from other processes, which have to be adapted into the original process. The framework was implemented in the Chemical Engineering domain due to the complexity of the processes

The proposed redesign framework has to be able to deal with complex technical processes.

In this sense, the type of processes we are referring to follow some assumptions:

of the maintenance stage but must be considered into the process life cycle.

processes to be redesigned, and therefore facilitate the redesign activities.

involved and the interaction with experts in the area.

physically implement a product.

(Stephanopoulos 90b).

sometimes it is difficult to understand.

#### **2.1 The process of redesign**

The research work related to the process of redesign is huge. Following the different research approaches are presented from a general point of view to more specific one. Some of the most interesting definitions of design that we have found in the literature are summarised as:

Defining design is difficult because the term refers both to a product (the object to be designed) and a process (the process of design). The reasoning process involved in design allows moving from a functional concept as a starting point to a product solution. Therefore, the design activity can be seen as an activity of synthesis, which is strongly influenced by the skills and mental models of the designer.

Regarding the process of redesign we have identified the following definitions in the literature:

Independently of the point of view of redesign, three types of redesign can be identified (Dixon 89): parametric redesign, component redesign, and structural redesign. In order to

Modelling Approach for Redesign of Technical Processes 97

(Bridge 97). Akin (Akin 82) outlines that the representational aspects to determine the utility

 The represented information must be in a level of abstraction suitable for its intention. The contents must be in such a way that they are compatible with the expected results

A substantial amount of research has focused on defining models of design (French 85, Tomiyama 87, Treur 89, Brown 89, Chandrasekaran 90, Gero 90a, Takeda 90a, Alberts 92, Vescovi 93, Ohsuga 97, Brown 97). Most of this research highlights that the modelling of the functionality (or properties) of the design object description is an important aspect of the

It is possible to represent explicit knowledge in (re)design by means of modelling functions of artefacts. This facilitates the systematisation of the reasoning and some tasks of (re)design. The reasoning based on functions allows abstracting information of the design on the same way as it is made in the reasoning of the initial stages of the design. The process of design of an artefact starts with the conceptual or functional design followed by the basic design and the detailed design (Stephanopoulos 90a). Within these, the functional design plays the central role since it guarantees the quality of the design and the innovation of the product (Umeda 97, Culley 99). The idea of function is fundamental in design since the work of the designers is to design artefacts that must achieve explicit functions (Chandrasekaran 00). Functional modelling is useful to model the object of (re)design, this modelling of objects enhances the formulation of (re)design strategies and the overall (re)design process. Functional modelling "hides" sections of the artefact structure at a lower abstraction level

Most of the research work on (re)design considers redesign as a knowledge-intensive field; wherein the processes (e.g., tasks) performed, descriptions of sequencing of processes, descriptions of the information within the system, and knowledge employed to perform a task are explicitly modelled most of the times by means of knowledge-based systems. These modelling frameworks try to model the (re)design so the (re)design object as well as the (re)design process are understandable by humans. To do this, human designers use the object specifications to propose a reasonable (re)design approach need to be understood (Leveson 00). Reasoning strategies employed in (re)design are derived or extensions of the commonly named *problem-solving strategies*. Examples of strategies are hypothesis and test (Hempel 66, White 05), pattern recognition (Doyle 62, Kirsch 64, Mitchell 97), skeletal plan refinement (Friedland 85, Tu 89), heuristic classification (Clancey 85), propose and revise (Goel 89), propose critique modify (Chandrasekaran 90), decision tree search (Raiffa 68, Qi 92), means-ends analysis (Newell 63, Rasmussen 86), and reasoning by analogy (Gick 80,

In the above strategies, the human designer needs to formulate an explicit model of expertise as an integration of two types of models: a domain model and problem solving method. The domain model corresponds to the (re)design object and the problem solving method model corresponds to the (re)design process. Work on domain modelling has only recently attracted the attention of knowledge based system researchers (Stephanopoulos 90a, Schoen 91, Gruber 93, Skuce 93, Sowa 95, Kitamura 98, Fensel 01b, Gomez-Perez 04).

according to the mental representations of the designer. The model must be consistent with the reality that tries to reflect.

facilitating the manipulation of the artefact description.

of a model in design are:

overall design process.

Gentner 83).

perform any of these redesign types, it is essential that some form of knowledge is available that allows the adaptation of existing designs. According to Pos (Pos 97) and based on the previous mentioned definitions of design/redesign, is possible distinguish two general points of view about the relationship between design and redesign, these are:

Adopting any of the above points of view, basically minimal differences can be distinguished. In both cases the important issue is to bridge the gap between a set of requirements and an existing design description. We can see that design starts from scratch, however, redesign starts with an existing design description, which is modified until it fulfills the current requirements. Both points of view can be captured by a single spectrum of problem-solving methods for redesign.

Several authors (Akin 82, Chandrasekaran 93, Eldonk 96, Brazier 96, Bridge 97, Pos 97) state that the required knowledge for redesign is based on the following two principles:

Many systems that solve redesign problems have been described in literature (Mitchell 83, Howe 86, Fischer 87, Daube 89, Goel 91, Smyth 96, Eldonk 96, Kitamura 99). However when one takes a closer look at the different variants of the redesign task, subtle differences exist that have an impact on how the task can be performed and what kinds of knowledge are involved.

There are a variety of research works referring to design or redesign; from (re)design of abstract (for example, components in software engineering) to physical entities (for example, a reactor in chemical engineering), for a general review see (Brown 97), for some details see (Akin 82, Mitchell 83, Howe 86, Fischer 87, Mostow 89, Goel 91, Bras 92, Stroulia 92a, Chandrasekaran 93, French 93, Brazier 96, Eldonk 96, Pos 97, Price 97, Umeda 97, Gero 98, Culley 99, Culley 99, Kitamura 99, Kraslawski 00, Grossmann 00, Arana 01, Maher 01).

Models in design and redesign are particularly important to guarantee they represent the intentions by which they were created. In general, the models are abstractions of the reality that guarantees communication of ideas by joining concepts, aggregations and relations

perform any of these redesign types, it is essential that some form of knowledge is available that allows the adaptation of existing designs. According to Pos (Pos 97) and based on the previous mentioned definitions of design/redesign, is possible distinguish two general

1. Viewing the design as total set which contains redesign as a subset. In order to satisfy this relationship, all the components of the design reasoning process should be satisfied for redesign. However redesign as a specialised subset would not be applicable in the same contexts as the more general notion of design. Here, design is viewed as an iterative process that uses intermediate results to get a final design description that fulfills the requirements. The task of redesign on the basis of a design created earlier produces a new temporary design description that is closer to the specification than the

2. Viewing both design and redesign as independent sets joined by a small common subset. For this relationship to be satisfied there is an expectation that some crossover or overlap will occur, thus only some of the components of design reasoning will be applicable in the redesign context and viceversa. Here, redesign starts with a previously constructed design description and a new set of requirements. The previously constructed design description must now be modified to fulfill the new set of

Adopting any of the above points of view, basically minimal differences can be distinguished. In both cases the important issue is to bridge the gap between a set of requirements and an existing design description. We can see that design starts from scratch, however, redesign starts with an existing design description, which is modified until it fulfills the current requirements. Both points of view can be captured by a single spectrum

Several authors (Akin 82, Chandrasekaran 93, Eldonk 96, Brazier 96, Bridge 97, Pos 97) state

Many systems that solve redesign problems have been described in literature (Mitchell 83, Howe 86, Fischer 87, Daube 89, Goel 91, Smyth 96, Eldonk 96, Kitamura 99). However when one takes a closer look at the different variants of the redesign task, subtle differences exist that have an impact on how the task can be performed and what kinds of knowledge are

There are a variety of research works referring to design or redesign; from (re)design of abstract (for example, components in software engineering) to physical entities (for example, a reactor in chemical engineering), for a general review see (Brown 97), for some details see (Akin 82, Mitchell 83, Howe 86, Fischer 87, Mostow 89, Goel 91, Bras 92, Stroulia 92a, Chandrasekaran 93, French 93, Brazier 96, Eldonk 96, Pos 97, Price 97, Umeda 97, Gero 98, Culley 99, Culley 99, Kitamura 99, Kraslawski 00, Grossmann 00, Arana 01, Maher 01).

Models in design and redesign are particularly important to guarantee they represent the intentions by which they were created. In general, the models are abstractions of the reality that guarantees communication of ideas by joining concepts, aggregations and relations

that the required knowledge for redesign is based on the following two principles:

Maximise existing properties and benefits of the current design.

points of view about the relationship between design and redesign, these are:

former design description.

of problem-solving methods for redesign.

Minimise changes in the current design, and

requirements.

involved.

(Bridge 97). Akin (Akin 82) outlines that the representational aspects to determine the utility of a model in design are:

A substantial amount of research has focused on defining models of design (French 85, Tomiyama 87, Treur 89, Brown 89, Chandrasekaran 90, Gero 90a, Takeda 90a, Alberts 92, Vescovi 93, Ohsuga 97, Brown 97). Most of this research highlights that the modelling of the functionality (or properties) of the design object description is an important aspect of the overall design process.

It is possible to represent explicit knowledge in (re)design by means of modelling functions of artefacts. This facilitates the systematisation of the reasoning and some tasks of (re)design. The reasoning based on functions allows abstracting information of the design on the same way as it is made in the reasoning of the initial stages of the design. The process of design of an artefact starts with the conceptual or functional design followed by the basic design and the detailed design (Stephanopoulos 90a). Within these, the functional design plays the central role since it guarantees the quality of the design and the innovation of the product (Umeda 97, Culley 99). The idea of function is fundamental in design since the work of the designers is to design artefacts that must achieve explicit functions (Chandrasekaran 00). Functional modelling is useful to model the object of (re)design, this modelling of objects enhances the formulation of (re)design strategies and the overall (re)design process. Functional modelling "hides" sections of the artefact structure at a lower abstraction level facilitating the manipulation of the artefact description.

Most of the research work on (re)design considers redesign as a knowledge-intensive field; wherein the processes (e.g., tasks) performed, descriptions of sequencing of processes, descriptions of the information within the system, and knowledge employed to perform a task are explicitly modelled most of the times by means of knowledge-based systems. These modelling frameworks try to model the (re)design so the (re)design object as well as the (re)design process are understandable by humans. To do this, human designers use the object specifications to propose a reasonable (re)design approach need to be understood (Leveson 00). Reasoning strategies employed in (re)design are derived or extensions of the commonly named *problem-solving strategies*. Examples of strategies are hypothesis and test (Hempel 66, White 05), pattern recognition (Doyle 62, Kirsch 64, Mitchell 97), skeletal plan refinement (Friedland 85, Tu 89), heuristic classification (Clancey 85), propose and revise (Goel 89), propose critique modify (Chandrasekaran 90), decision tree search (Raiffa 68, Qi 92), means-ends analysis (Newell 63, Rasmussen 86), and reasoning by analogy (Gick 80, Gentner 83).

In the above strategies, the human designer needs to formulate an explicit model of expertise as an integration of two types of models: a domain model and problem solving method. The domain model corresponds to the (re)design object and the problem solving method model corresponds to the (re)design process. Work on domain modelling has only recently attracted the attention of knowledge based system researchers (Stephanopoulos 90a, Schoen 91, Gruber 93, Skuce 93, Sowa 95, Kitamura 98, Fensel 01b, Gomez-Perez 04).

Modelling Approach for Redesign of Technical Processes 99

The design object is the central "actor" that receives the attention during the overall design process. This can be a model of a component, artefact, process or system. Traditionally, the design object was created by technical drafts; but with the advent of computers, the design object has become a computer model that can be shown, modified and deleted easily. Thus,

Some authors (Rasmussen 86, Douglas 88, Hoover 91, Lind 94, Turton 98, Leveson 00) have observed that abstractions of the design object are important during the design process to

abstractions and refinements are selected opportunistically and are characterised by the

Several research works have been developed about (re)design object manipulation. The most relevant approaches in this issue are model-based design and case-based design.

One of the most used approaches in the manipulation of the (re)design object is model-based design which really is a branch of model-based reasoning (MBR) applied to (re)design. Model-based reasoning constitutes a set of techniques applied in several domains and it is used to create models and reasoning about the domain. Mainly the most used technique from MBR has been *compositional modelling* (Falkenhainer 91, Falkenhainer 92, Nayak and Joskowicz 96), which is an approach to construct a model of an artefact (components, devices, processes, systems, etc.) on the basis of a description of the artefact and a query about the composition of the artefact. The modelling of functions (*functional representation*) (Sembugamoorthy 86, Chittaro 98, Chandrasekaran 00) is crucial in compositional modelling. Functional Representation is a top-down approach to describe functions on devices (function), its structure (structure) and its causal processes (behaviour) of the device that culminate with the achievement of the function. Functional modelling reduces drastically the amount of information if simulation is required (Price 98). The

conceptual, layout, and detailed stages are not distinct steps in the design process.

manipulate design objects. In this sense, Hoover (Hoover 91) has observed that:

the design object evolves through abstractions and refinements.

refinements are made within the framework of abstractions.

approaches of functional modelling can be classified in two groups:

designer focusing on a few aspects of the design object at a time.

agent architectures (Dunskus 95, Berker 96, Lander 97).

Prescriptive models (Salomons 95)

Opportunistic design (French 93)

Human learning process (Gero 04)

**2.4 The design object**

**2.4.1 Model-based design**

The following models of the design process can be distinguished:

Descriptive models (Stephanopoulos 90b, Ohsuga 97, Sumi 97)

Decision support problem (Bras 92, Thornton 93, Ullman 91)

Theorem problem solving process (Takeda 94a)

several models of artefacts have been used in design.

Multiagent design (Wood 01, DeLoach 04).

The problem solving method determines how those entities in the model will be used in the actual problem solving process. Domain specific concepts, relationships, and knowledge pertaining to them are captured in the domain model through ontologies (Chittaro 93, Kitamura 99, Fensel 01b, Kuraoka 03).

Independently of models and strategies employed in the (re)design, it is important that such data and knowledge can be recorded in a consistent manner for the future understanding of the (re)design; this constitutes what is called *(re)design rationale*.

#### **2.2 The role of function in the design process**

Functions in design play the central role since it guarantees the quality of the design and the innovation of the product (Umeda 97, Culley 99). Function is regarded as what a design object is supposed to do; it is a manageable representation of the overall behaviour of the object (Price 98). Some authors define function as an abstraction of its intended behaviour strongly related to its context (Gero 90a, Goel 92, Stroulia 92a, Chittaro 93, Brown 97, Chandrasekaran 00). Initially the human designers think in functions before they are concerned with specific properties. Functions can exist at different levels of abstraction, depending on the design phase that one is in and the current focus of the design interest. In preliminary design phases, functions usually are independent of working principle, whereas in later design phases, when the functions have been detailed, they become more and more dependent on the working principle that has been selected. In the following, a distinction between three levels or categories of functions is made:

#### **2.3 The design process**

The design process is a complex and not yet well understood cognitive process conducted by humans (Salomons 95). The design process is related to the process of actions and decisions that are taken during design in order to arrive at completed product design. Models of design processes provide a structured description of a process of design. The models differ in their underlying formalisations and have been represented in structures such as:

agent architectures (Dunskus 95, Berker 96, Lander 97).

The following models of the design process can be distinguished:

#### **2.4 The design object**

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The problem solving method determines how those entities in the model will be used in the actual problem solving process. Domain specific concepts, relationships, and knowledge pertaining to them are captured in the domain model through ontologies (Chittaro 93,

Independently of models and strategies employed in the (re)design, it is important that such data and knowledge can be recorded in a consistent manner for the future understanding of

Functions in design play the central role since it guarantees the quality of the design and the innovation of the product (Umeda 97, Culley 99). Function is regarded as what a design object is supposed to do; it is a manageable representation of the overall behaviour of the object (Price 98). Some authors define function as an abstraction of its intended behaviour strongly related to its context (Gero 90a, Goel 92, Stroulia 92a, Chittaro 93, Brown 97, Chandrasekaran 00). Initially the human designers think in functions before they are concerned with specific properties. Functions can exist at different levels of abstraction, depending on the design phase that one is in and the current focus of the design interest. In preliminary design phases, functions usually are independent of working principle, whereas in later design phases, when the functions have been detailed, they become more and more dependent on the working principle that has been selected. In the following, a distinction

 *General functions*. (Keuneke 91, Lind 94, Kitamura 98, Bo 99) proposed a restricted list of general functions dealing with the transformation of matter, energy and/or

*Specialised functions or subfunctions*. Act on flows, forces, moments etc., independent of

 *Working principle dependent function*. Salomons (Salomons 95) defines it as the realisation of a specialised function (by means of physical phenomena). Several alternative solutions for fulfilling working principle dependent functions can exist without

The design process is a complex and not yet well understood cognitive process conducted by humans (Salomons 95). The design process is related to the process of actions and decisions that are taken during design in order to arrive at completed product design. Models of design processes provide a structured description of a process of design. The models differ in their underlying formalisations and have been represented in structures

SOAR (cognitive architecture for developing systems with intelligent behaviour) (Steier

task models or problem solving methods (Brown 89, Brazier 94, Wielinga 97), or

Kitamura 99, Fensel 01b, Kuraoka 03).

the (re)design; this constitutes what is called *(re)design rationale*.

**2.2 The role of function in the design process**

between three levels or categories of functions is made:

changing the working principle itself.

blackboard architectures (Ball 92),

algorithms (Alberts 93b),

the working principle.

**2.3 The design process**

such as:

91),

information, which are independent of the working principle.

The design object is the central "actor" that receives the attention during the overall design process. This can be a model of a component, artefact, process or system. Traditionally, the design object was created by technical drafts; but with the advent of computers, the design object has become a computer model that can be shown, modified and deleted easily. Thus, several models of artefacts have been used in design.

Some authors (Rasmussen 86, Douglas 88, Hoover 91, Lind 94, Turton 98, Leveson 00) have observed that abstractions of the design object are important during the design process to manipulate design objects. In this sense, Hoover (Hoover 91) has observed that:

Several research works have been developed about (re)design object manipulation. The most relevant approaches in this issue are model-based design and case-based design.

#### **2.4.1 Model-based design**

One of the most used approaches in the manipulation of the (re)design object is model-based design which really is a branch of model-based reasoning (MBR) applied to (re)design. Model-based reasoning constitutes a set of techniques applied in several domains and it is used to create models and reasoning about the domain. Mainly the most used technique from MBR has been *compositional modelling* (Falkenhainer 91, Falkenhainer 92, Nayak and Joskowicz 96), which is an approach to construct a model of an artefact (components, devices, processes, systems, etc.) on the basis of a description of the artefact and a query about the composition of the artefact. The modelling of functions (*functional representation*) (Sembugamoorthy 86, Chittaro 98, Chandrasekaran 00) is crucial in compositional modelling. Functional Representation is a top-down approach to describe functions on devices (function), its structure (structure) and its causal processes (behaviour) of the device that culminate with the achievement of the function. Functional modelling reduces drastically the amount of information if simulation is required (Price 98). The approaches of functional modelling can be classified in two groups:

Modelling Approach for Redesign of Technical Processes 101

objective of this approach is to find the part of the system, which causes the discrepancy between a formal specification of the system to be redesigned and the description of the existing technical system. Kitamura and Mizoguchi [Sasajima 95, Kitamura 99] proposed a redesign approach based on ontologies of functional concepts. They focus on capturing the rationales of design of an artefact and in organising general strategies of redesign. For the first point, they use an ontology of functional concepts that allows to identify functional structures and to represent automatically part of the design rationale. For the second point, they use an ontology of redesign strategies. This approach consists of the following stages: functional understanding, analysis of requirements, proposal of alternative and evaluation.

Aranna et. al. [Fothergill 95, Forster 96, Forster 97b, Arana 00] proposed a redesign environment called DEKLARE, which supports acquisition, representation and reuse of redesign knowledge. It allows the designer to use design techniques to suggest alternative designs that fulfill specific requirements. Gupta et. al. [Das 94] proposed a methodology that automatically provides suggestions of redesign for reducing setup costs for mechanical parts. This approach is based on the interpretation of the design as a collection of mechanical features. The objective is to generate alternative mechanical features by means of geometric changes of the original parts and adding them to the feature set of the original part. Kim [Kim 93] proposed an approach for redesign of assemblies by means of planning techniques. Kim deals with the absence of required design information using the replay and modify principle. He employs a reverse engineering model to infer information about the process executed when creating a given design, and using the inferred information for design recreation or redesign. The propose model consists of the three stages: knowledge

acquisition, construction of the default design plan, and redesign based on cases.

Steinberg and Mitchell developed a system to redesign VLSI circuits [Steinberg 85]. This redesign approach is based on planning techniques and causal and teleological reasoning [de Kleer 79]. The subtasks of this approach are: a) focus on an appropriate section of the circuit, b) generate redesign options to the level of proposed specifications for individual modules, c) rank the generated redesign options, d) implement the selected redesign option, and e) detect and repair of side effects resulting from the redesign. Maulik et. al. [Maulik 92] proposed the use of optimisation techniques to redesign CMOS analog circuits. The optimisation approach is guided by three principles. First, equations that describe device characteristics are encapsulated and separated from equations that describe the performance of the circuit topologies. Secondly, constrained optimisation techniques are employed to synthesise the redesigned-scaled CMOS circuit. Finally, constrained optimisation allows the solution of some final constraints over specific variables. Based on the approach of Umeda et. al. [Umeda 92], Tomiyama et. al. [Umeda 94] describe an extension of their approach taking into account the potential functions of the components of an artefact to redesign it. The architecture consists of sensors, which monitor the machine, and a model-based reasoner diagnoses faults and plans repairs. The system generates a FBS model (Function-Behaviour-State) based on the design object, and then searches the model for candidate redundant function. The FBS model consists of a function hierarchy that represents the

**2.5.2 Mechanical engineering**

**2.5.3 Electrical and electronic engineering**

#### **2.4.2 Case-based design**

Case-based design is a branch of case-based reasoning (CBR). CBR is a general paradigm to solve problems based on the recovery, reuse, revision and retention of specific experiences (cases) (Aamodt 94). CBR has been applied to component-based systems (Maher 97a) which is, however, mostly concerned with the manipulation of design object descriptions. This paradigm is particularly attractive in domains where explicit models do not exist or its understanding is difficult (Kolodner 93). In CBR, similarities between formal methods implemented in computer programs and informal observations from designers are taken into account (Maher 97a). The applications of CBR can be for classification or synthesis tasks. (Re)design problems are within the synthesis tasks.

The direct or analogical use of previous designs or plans of design can reduce and improve the quality of design because take advantages of previous experiences (Maher 95). CBR is viewed as a redesign process for the adaptation of a case where a new artefact (named goal) is designed to achieve certain function, its physical structure can be inferred in analogical way from some physical, chemical or biological object (named source) whose function is similar to the required function.

CBR has been applied to solve problems of real world; there are several works about casebased design for example (Goel 89, Qian 92, Sycara 92, Bhatta 94, Borner 96, de Silva Garza 96, Maher 01, Price 97).

#### **2.5 Redesign approaches**

Here the division between research on design and redesign is remarked, only research from the redesign perspective is presented.

#### **2.5.1 Generic approaches in engineering**

Goel et. al. [Stroulia 92a, Stroulia 92b, Goel 94b, Goel 97a] presented a control architecture for model-based redesign in the context of case-based redesign. They state that the redesign task is characterised by small differences in the functions desired of and delivered by an existent known design. The redesign is divided in three subtasks: a) generation of modifications to the structure of the old design, b) realisation of the modifications on the structure, and c) evaluation of the new design. Eldonk et. al. [Alberts 93a, Bakker 94, Eldonk 96] presented a redesign approach based on techniques developed in model-based diagnosis. Eldonk et. al. state that redesign activities are diagnosis and respecification. The objective of this approach is to find the part of the system, which causes the discrepancy between a formal specification of the system to be redesigned and the description of the existing technical system. Kitamura and Mizoguchi [Sasajima 95, Kitamura 99] proposed a redesign approach based on ontologies of functional concepts. They focus on capturing the rationales of design of an artefact and in organising general strategies of redesign. For the first point, they use an ontology of functional concepts that allows to identify functional structures and to represent automatically part of the design rationale. For the second point, they use an ontology of redesign strategies. This approach consists of the following stages: functional understanding, analysis of requirements, proposal of alternative and evaluation.

#### **2.5.2 Mechanical engineering**

100 Advances in Chemical Engineering

 *State-based representations*. It uses units of function representation, which are abstractions of behaviour states. Behaviour states and hence functions may be

 *Flow-based representations*. Flow-based representations are based on the concepts of flow and effort. In this approach exists a predefined set of functions, and functions of all existent components are expressed in terms of these primitive functions. This approach is based on the System Theory (Bertalanffy 50) and its derivatives (Abstraction Hierarchy (Rasmussen 86), Qualitative Process Theory (Forbus 84), and Multilevel Flow

Case-based design is a branch of case-based reasoning (CBR). CBR is a general paradigm to solve problems based on the recovery, reuse, revision and retention of specific experiences (cases) (Aamodt 94). CBR has been applied to component-based systems (Maher 97a) which is, however, mostly concerned with the manipulation of design object descriptions. This paradigm is particularly attractive in domains where explicit models do not exist or its understanding is difficult (Kolodner 93). In CBR, similarities between formal methods implemented in computer programs and informal observations from designers are taken into account (Maher 97a). The applications of CBR can be for classification or synthesis

The direct or analogical use of previous designs or plans of design can reduce and improve the quality of design because take advantages of previous experiences (Maher 95). CBR is viewed as a redesign process for the adaptation of a case where a new artefact (named goal) is designed to achieve certain function, its physical structure can be inferred in analogical way from some physical, chemical or biological object (named source) whose function is

CBR has been applied to solve problems of real world; there are several works about casebased design for example (Goel 89, Qian 92, Sycara 92, Bhatta 94, Borner 96, de Silva Garza

Here the division between research on design and redesign is remarked, only research from

Goel et. al. [Stroulia 92a, Stroulia 92b, Goel 94b, Goel 97a] presented a control architecture for model-based redesign in the context of case-based redesign. They state that the redesign task is characterised by small differences in the functions desired of and delivered by an existent known design. The redesign is divided in three subtasks: a) generation of modifications to the structure of the old design, b) realisation of the modifications on the structure, and c) evaluation of the new design. Eldonk et. al. [Alberts 93a, Bakker 94, Eldonk 96] presented a redesign approach based on techniques developed in model-based diagnosis. Eldonk et. al. state that redesign activities are diagnosis and respecification. The

associated even with static objects which do not cause any state change.

Modelling (Lind 90, Lind 94)).

tasks. (Re)design problems are within the synthesis tasks.

**2.4.2 Case-based design**

similar to the required function.

96, Maher 01, Price 97).

**2.5 Redesign approaches**

the redesign perspective is presented.

**2.5.1 Generic approaches in engineering**

Aranna et. al. [Fothergill 95, Forster 96, Forster 97b, Arana 00] proposed a redesign environment called DEKLARE, which supports acquisition, representation and reuse of redesign knowledge. It allows the designer to use design techniques to suggest alternative designs that fulfill specific requirements. Gupta et. al. [Das 94] proposed a methodology that automatically provides suggestions of redesign for reducing setup costs for mechanical parts. This approach is based on the interpretation of the design as a collection of mechanical features. The objective is to generate alternative mechanical features by means of geometric changes of the original parts and adding them to the feature set of the original part. Kim [Kim 93] proposed an approach for redesign of assemblies by means of planning techniques. Kim deals with the absence of required design information using the replay and modify principle. He employs a reverse engineering model to infer information about the process executed when creating a given design, and using the inferred information for design recreation or redesign. The propose model consists of the three stages: knowledge acquisition, construction of the default design plan, and redesign based on cases.

#### **2.5.3 Electrical and electronic engineering**

Steinberg and Mitchell developed a system to redesign VLSI circuits [Steinberg 85]. This redesign approach is based on planning techniques and causal and teleological reasoning [de Kleer 79]. The subtasks of this approach are: a) focus on an appropriate section of the circuit, b) generate redesign options to the level of proposed specifications for individual modules, c) rank the generated redesign options, d) implement the selected redesign option, and e) detect and repair of side effects resulting from the redesign. Maulik et. al. [Maulik 92] proposed the use of optimisation techniques to redesign CMOS analog circuits. The optimisation approach is guided by three principles. First, equations that describe device characteristics are encapsulated and separated from equations that describe the performance of the circuit topologies. Secondly, constrained optimisation techniques are employed to synthesise the redesigned-scaled CMOS circuit. Finally, constrained optimisation allows the solution of some final constraints over specific variables. Based on the approach of Umeda et. al. [Umeda 92], Tomiyama et. al. [Umeda 94] describe an extension of their approach taking into account the potential functions of the components of an artefact to redesign it. The architecture consists of sensors, which monitor the machine, and a model-based reasoner diagnoses faults and plans repairs. The system generates a FBS model (Function-Behaviour-State) based on the design object, and then searches the model for candidate redundant function. The FBS model consists of a function hierarchy that represents the

Modelling Approach for Redesign of Technical Processes 103

The overall redesign process depends on the problem-solving strategy used. In order to start the redesign process, the problem must be specified in terms of objectives that the original artefact must satisfy and the criteria that can be used to rank the alternative designs. Then a synthesis process takes place and the results are a set of alternative designs. Each of these alternatives is analysed and evaluated in terms of the predefined objectives and design criteria. Finally, one alternative is selected to be implemented. The process is highly iterative; the results from later stages are fedback to early stages to modify objectives,

Design alternatives are generated through a process of analysis of system composition. The designer breaks down the system (artefact) into a set of subsystems (components), together with the functions and constraints imposed upon the individual subsystem designs. These aspects are analysed with respect to desired system performance features and constraints. The process is iterative until an acceptable design alternative is achieved. At the end of this process all components must be described in such detail that an implementation of the

The understanding of the redesign object depends strongly on the mental models of the human designer. Usually designers communicate their ideas more easily in terms of abstract, high-level descriptions to describe complex concepts (Price 03). The description of the redesign object can be done in many different ways, depending on the context and purpose for which the description is to be used. In the early phases of redesign, highly abstract descriptions (e.g. qualitative or causal) might be helpful, whereas in later phases,

Considering the notion of function, some researchers (Sembugamoorthy 86, Goel 89, Franke 92, Keuneke 91, Chittaro 93, Iwasaki 93) organise the knowledge in a domain by means of functional concepts. The main claim of these approaches is that functions and intentions can provide important additional information for understanding and reasoning about the structure and behaviour of physical systems. In addition, other researchers have directed their extentions to hierarchical modelling by means of different aggregation levels (Liu 91, Rajamoney 91) or different approximations (Weld 86, Kuipers 87, Struss 91, Falkenhainer 91) to organise the knowledge. Independently of the tools and representations employed, several authors (Fischo. 78, Checkland 81, Jaffe 91, Vicente 92) suggest that two important aspects must be addressed if computer tools are used to tackle activities of complex systems: *Content*, the semantic information that should be contained in the representation given the goals and tasks of the users. The content gives the basic issues to understand the information about the redesign object. Independently of the amount and complexity of the information, the designer can conceive, in general terms, the objectives of the

 *Structure*, how to design the representation to facilitate that the user can extract the required information. The structure concerns to the organisation of the process

more detailed and quantitative descriptions provide more suitable information.

**2.6 Modelling**

**2.6.1 Modelling the redesign process**

criteria, design alternatives, and so on.

whole object can be performed.

redesign object.

components.

**2.6.2 Modelling the redesign object**

designer's intentions, and a behaviour network that describes how the function hierarchy is carried out. The system first tries a control type strategy that adjusts various machine parameters. If the strategy fails the system applies a strategy based on functional redundancy, it uses the potential functions of existing parts in a slightly different way from the original design. Heo et. al. [Heo 98] presented a redesign approach of digital electronic systems by means of evolutive programming. They use directed acyclic graphs known as task flow graphs (TFG) to represent the redesign object. Each node of the graph represents computational tasks; an edge represents a transfer of data. The design process consists of five tiers: a) system-level design, b) architectural design, c) logic design, d) circuit design, and e) physical design.

#### **2.5.4 Chemical engineering**

The (re)design of chemical processes is made with the purpose of adapting existing processes to changes in economic, technological or environmental requirements. In the eighties, there were mainly significant advances on saving energy by means of two constraint-based approaches: a) pinch methodology (Tjoe 86, Smith 87, Linnho. 88) and b) mathematical programming on synthesis and design of processes [Papoulias 83, Pistikopoulos 87, Vaselenak 87]. In the nineties, Gundersen [Gundersen 90] made a revision of systematic methods of redesign of processes, which were broadly tackled. In such revision, he emphasised two important observations:

Doherty et. al. [Fischer 87] developed a systematic procedure of redesign by means of opportunistic searches; the procedure considers modifications in the structure of the flowsheet and in the dimension of equipments. Kirkwood et. al. [Kirkwood 88] implemented a methodology of redesign by means of an expert system by using heuristic rules to construct hierarchical structures. Nelson and Douglas [Nelson 90] developed a systematic procedure considering alternative reaction routes; the procedure is hierarchical and provides guides to identify viable processes. Rapoport et. al. [Rapoport 94] proposed an algorithm to design units of process by means of the redesign of already existing ones. The algorithm consists on hierarchical levels and heuristic rules; this approach is similar to synthesis of processes. Stephanopoulos et. al. [Han 95] developed an approach based on agents to synthesis of processes; they modeled the process of design like a set of tasks that can be executed by agents. Systems have also been developed to satisfy economic, environmental and safety constraints. Kraslawski et. al. [Kraslawski 00] developed a methodology centred on the identification and elimination of bottlenecks in reaction and separation sections. Sylvester et. al. [Sylvester 00] optimised processes within the concept of Greener Process. Hertwig et. al. [Hertwig 01] applied techniques of MINLP (Mixed-Integer Non-Linear Programming) to optimise configuration of processes. Pasanen [Pasanen 01] developed a tool for conceptual design of processes, this is called Phenomenon Driven Process Design (PDPD). This methodology focuses on the systematisation of design conceptual of chemical processes. Uerdingen et. al. [Uerdingen 01] presented a screening method based on an analysis of the flow path pattern. They use performance indicators to rate the economic impact of each component of the flowsheet in the flow path.

#### **2.6 Modelling**

102 Advances in Chemical Engineering

designer's intentions, and a behaviour network that describes how the function hierarchy is carried out. The system first tries a control type strategy that adjusts various machine parameters. If the strategy fails the system applies a strategy based on functional redundancy, it uses the potential functions of existing parts in a slightly different way from the original design. Heo et. al. [Heo 98] presented a redesign approach of digital electronic systems by means of evolutive programming. They use directed acyclic graphs known as task flow graphs (TFG) to represent the redesign object. Each node of the graph represents computational tasks; an edge represents a transfer of data. The design process consists of five tiers: a) system-level design, b) architectural design, c) logic design, d) circuit design,

The (re)design of chemical processes is made with the purpose of adapting existing processes to changes in economic, technological or environmental requirements. In the eighties, there were mainly significant advances on saving energy by means of two constraint-based approaches: a) pinch methodology (Tjoe 86, Smith 87, Linnho. 88) and b) mathematical programming on synthesis and design of processes [Papoulias 83, Pistikopoulos 87, Vaselenak 87]. In the nineties, Gundersen [Gundersen 90] made a revision of systematic methods of redesign of processes, which were broadly tackled. In such

The systematic methods of redesign of processes are based on methods of design of

Doherty et. al. [Fischer 87] developed a systematic procedure of redesign by means of opportunistic searches; the procedure considers modifications in the structure of the flowsheet and in the dimension of equipments. Kirkwood et. al. [Kirkwood 88] implemented a methodology of redesign by means of an expert system by using heuristic rules to construct hierarchical structures. Nelson and Douglas [Nelson 90] developed a systematic procedure considering alternative reaction routes; the procedure is hierarchical and provides guides to identify viable processes. Rapoport et. al. [Rapoport 94] proposed an algorithm to design units of process by means of the redesign of already existing ones. The algorithm consists on hierarchical levels and heuristic rules; this approach is similar to synthesis of processes. Stephanopoulos et. al. [Han 95] developed an approach based on agents to synthesis of processes; they modeled the process of design like a set of tasks that can be executed by agents. Systems have also been developed to satisfy economic, environmental and safety constraints. Kraslawski et. al. [Kraslawski 00] developed a methodology centred on the identification and elimination of bottlenecks in reaction and separation sections. Sylvester et. al. [Sylvester 00] optimised processes within the concept of Greener Process. Hertwig et. al. [Hertwig 01] applied techniques of MINLP (Mixed-Integer Non-Linear Programming) to optimise configuration of processes. Pasanen [Pasanen 01] developed a tool for conceptual design of processes, this is called Phenomenon Driven Process Design (PDPD). This methodology focuses on the systematisation of design conceptual of chemical processes. Uerdingen et. al. [Uerdingen 01] presented a screening method based on an analysis of the flow path pattern. They use performance indicators to

and e) physical design.

processes.

**2.5.4 Chemical engineering**

revision, he emphasised two important observations:

Most of the projects in the industry of processes were redesign projects.

rate the economic impact of each component of the flowsheet in the flow path.

#### **2.6.1 Modelling the redesign process**

The overall redesign process depends on the problem-solving strategy used. In order to start the redesign process, the problem must be specified in terms of objectives that the original artefact must satisfy and the criteria that can be used to rank the alternative designs. Then a synthesis process takes place and the results are a set of alternative designs. Each of these alternatives is analysed and evaluated in terms of the predefined objectives and design criteria. Finally, one alternative is selected to be implemented. The process is highly iterative; the results from later stages are fedback to early stages to modify objectives, criteria, design alternatives, and so on.

Design alternatives are generated through a process of analysis of system composition. The designer breaks down the system (artefact) into a set of subsystems (components), together with the functions and constraints imposed upon the individual subsystem designs. These aspects are analysed with respect to desired system performance features and constraints. The process is iterative until an acceptable design alternative is achieved. At the end of this process all components must be described in such detail that an implementation of the whole object can be performed.

#### **2.6.2 Modelling the redesign object**

The understanding of the redesign object depends strongly on the mental models of the human designer. Usually designers communicate their ideas more easily in terms of abstract, high-level descriptions to describe complex concepts (Price 03). The description of the redesign object can be done in many different ways, depending on the context and purpose for which the description is to be used. In the early phases of redesign, highly abstract descriptions (e.g. qualitative or causal) might be helpful, whereas in later phases, more detailed and quantitative descriptions provide more suitable information.

Considering the notion of function, some researchers (Sembugamoorthy 86, Goel 89, Franke 92, Keuneke 91, Chittaro 93, Iwasaki 93) organise the knowledge in a domain by means of functional concepts. The main claim of these approaches is that functions and intentions can provide important additional information for understanding and reasoning about the structure and behaviour of physical systems. In addition, other researchers have directed their extentions to hierarchical modelling by means of different aggregation levels (Liu 91, Rajamoney 91) or different approximations (Weld 86, Kuipers 87, Struss 91, Falkenhainer 91) to organise the knowledge. Independently of the tools and representations employed, several authors (Fischo. 78, Checkland 81, Jaffe 91, Vicente 92) suggest that two important aspects must be addressed if computer tools are used to tackle activities of complex systems:

Modelling Approach for Redesign of Technical Processes 105

*goals*, which are the objectives or purposes of the system, i.e., the ends that the designers

*functions*, which are the means by which the goals are obtained, i.e., the powers or

*physical components*, which are the different elements of the system, the equipment of

The concept of goal is central to MFM, as it is the "descriptor object" for teleological information. It is important to be able to recognise and describe goals, as they play an important role in every activity using means-end information. Without knowing the goals, it is virtually impossible to know the available functions. Three different types of goals can be

*production goals*, which are used to express what enables production. For example, a

 *safety goals*, *which are used to express reasons of safe operation. For instance, a particular process variable should be kept above or below some value, or inside or outside an interval. economy goals, which are used to express considerations of overall process optimisation.*

*The function is the second* important concept on MFM. A function is always associated with a goal, and correspondingly, goals are always associated with functions. MFM describes the functional structure of a system as a set of interrelated flow structures on different abstraction levels. The levels are connected via achievement and condition relations; the flow structures consist of connected flow functions. Thus, the following types of flow

These flows are of completely different types, but they have many properties in common. Most flow functions can appear in each type of flow structure, thus, there are three flow types of flow functions. In MFM plant functions are represented by a set of mass, energy, activity and information flow structures on several levels of abstraction. The levels are interdependent and form means-end structures. Mass and energy flow structures are used to model the functions of the plant and activity and information flow structures are used to model the functions of the operator and the control systems. The mass and energy flow

*source*, the capability of a physical system to act as an infinite reservoir of mass, energy,

*transport*, the capability of a system to transfer mass, energy, or information from one

*barrier*, the capability of a system to prevent the transfer of mass, energy or information

*balance*, the capability of a system to provide a balance between the total rates of

part of the system to another (from one medium to another).

from one part of the system to another (from one medium to another). *storage*, the capability of a system to accumulate mass, energy, or information.

specific process variable should be kept within a given interval.

and operators want that the system reaches.

capabilities of the system.

which it consists.

recognised:

structures can be: mass flows energy flows information flows

functions are:

or information.

incoming and outgoing flows.

Rasmussen (Rasmussen 85) observed that the complexity of a system depends on the level of resolution in which the system is considered. The complexity can only be measured comparing with other systems observed at the same level of abstraction. The complexity can be manageable with more or less detail in the representations: then, hierarchical modelling can be seen as a way to handle complex systems. Models of complex artefacts can be expressed in terms of a hierarchy of levels of organisation, each one more complex than the previous. This modelling approach is named *Hierarchy Theory* (Rasmussen 86). Rasmussen studied the protocols developed by people working on complex systems and found that human users structure the system along two dimensions: a part-whole abstraction and a means-ends abstraction.

Some authors (Umeda 90, Franke 92, Lind 94) propose representation approaches for physical systems which maintain a clear separation between knowledge of structure and behaviour on one side and knowledge of function or purposes on the other side. This feature makes them useful in redesign of technical complex systems. The hierarchical functional modelling approaches employed in the present work are Multilevel Flow Modelling (MFM) (Lind 90, Lind 94, Lind 96, Lind 99) and Multimodelling (Ziegler 79, Praehofer 91, Chittaro 93), which are following described. Both approaches provide a more intuitive vision of reasoning on each task to be performed, and thus the redesign activities are enhanced. These approaches have been applied successfully in diagnosis and control domains.

#### **<IP_ADDRESS> Multilevel flow modelling**

MFM provides a graphical and systematic basis for using means-end and whole-part hierarchical decompositions in the modelling of complex systems such as industrial plants. By the distinction between means and ends, a system is described in terms of goals, functions and the physical components that involves. At the same time, each of these descriptions can be given on different levels of whole-part decompositions. The main types of decomposition are illustrated in Figure 2. These are functional models with a very high level of abstraction, combined with a teleological representation of goals, or purposes, of the modelled system. Lind has suggested a syntax for a formal language and given the general ideas on how to use the MFM representation.

Fig. 2. Means-ends and part-whole dimensions in MFM.

An MFM model is a prescriptive description of a system, a representation of what it has been designed to do, how it should do it, and with which information it should do it. Thus, the three basic concept types of MFM are:

The concept of goal is central to MFM, as it is the "descriptor object" for teleological information. It is important to be able to recognise and describe goals, as they play an important role in every activity using means-end information. Without knowing the goals, it is virtually impossible to know the available functions. Three different types of goals can be recognised:

*The function is the second* important concept on MFM. A function is always associated with a goal, and correspondingly, goals are always associated with functions. MFM describes the functional structure of a system as a set of interrelated flow structures on different abstraction levels. The levels are connected via achievement and condition relations; the flow structures consist of connected flow functions. Thus, the following types of flow structures can be:

mass flows

104 Advances in Chemical Engineering

Rasmussen (Rasmussen 85) observed that the complexity of a system depends on the level of resolution in which the system is considered. The complexity can only be measured comparing with other systems observed at the same level of abstraction. The complexity can be manageable with more or less detail in the representations: then, hierarchical modelling can be seen as a way to handle complex systems. Models of complex artefacts can be expressed in terms of a hierarchy of levels of organisation, each one more complex than the previous. This modelling approach is named *Hierarchy Theory* (Rasmussen 86). Rasmussen studied the protocols developed by people working on complex systems and found that human users structure the system along two dimensions: a part-whole abstraction and a

Some authors (Umeda 90, Franke 92, Lind 94) propose representation approaches for physical systems which maintain a clear separation between knowledge of structure and behaviour on one side and knowledge of function or purposes on the other side. This feature makes them useful in redesign of technical complex systems. The hierarchical functional modelling approaches employed in the present work are Multilevel Flow Modelling (MFM) (Lind 90, Lind 94, Lind 96, Lind 99) and Multimodelling (Ziegler 79, Praehofer 91, Chittaro 93), which are following described. Both approaches provide a more intuitive vision of reasoning on each task to be performed, and thus the redesign activities are enhanced. These approaches have been applied successfully in diagnosis and control

MFM provides a graphical and systematic basis for using means-end and whole-part hierarchical decompositions in the modelling of complex systems such as industrial plants. By the distinction between means and ends, a system is described in terms of goals, functions and the physical components that involves. At the same time, each of these descriptions can be given on different levels of whole-part decompositions. The main types of decomposition are illustrated in Figure 2. These are functional models with a very high level of abstraction, combined with a teleological representation of goals, or purposes, of the modelled system. Lind has suggested a syntax for a formal language and given the general

An MFM model is a prescriptive description of a system, a representation of what it has been designed to do, how it should do it, and with which information it should do it. Thus,

means-ends abstraction.

**<IP_ADDRESS> Multilevel flow modelling**

ideas on how to use the MFM representation.

Fig. 2. Means-ends and part-whole dimensions in MFM.

the three basic concept types of MFM are:

domains.

These flows are of completely different types, but they have many properties in common. Most flow functions can appear in each type of flow structure, thus, there are three flow types of flow functions. In MFM plant functions are represented by a set of mass, energy, activity and information flow structures on several levels of abstraction. The levels are interdependent and form means-end structures. Mass and energy flow structures are used to model the functions of the plant and activity and information flow structures are used to model the functions of the operator and the control systems. The mass and energy flow functions are:

Modelling Approach for Redesign of Technical Processes 107

The Multilevel Flow Modelling (MFM) and the Multimodelling approaches are able to represent how the human designers behave during the redesign process. MFM is used for high-levels of abstractions and Multimodelling is more suitable for the intermediate and lower levels. Thus, the structure and behaviour of the components (equipments) are abstracted using the Multimodelling approach and then this abstraction is mapped to the MFM approach. The bridge between both approaches is the functions for the equipments in the domain. This functional modelling is the basis to manipulate the process during all the redesign process. The main idea is to model hierarchically the process and reason by using functional abstract concepts. In this way the designer can "navigate" in top-down and bottom-up directions in the representation in similar way as when the designer creates its mental models about the process. From an abstract point of view, there are three actors that

1. *The simulator*. The commercial software used to obtain the design description of the process and to implement and evaluate the generated alternative process designs. 2. *The reasoner*. The software modules required to model the process, identify the suitable equipment/section to be modified and obtain similar equipments/sections based on the

1. Identification of objectives and criteria. This stage covers the design-description

2. Generation of design alternatives. This stage is similar to the obtaining alternatives and

This redesign framework can deal with complex technical processes (the redesign object). The modelling approach was chosen to mimic the behaviour of human designers in real redesign situation of such processes. The final intention is to support human designers, not

The first stage of the framework is to obtain the design description of the process to be redesigned. This description is enough to carry out the redesign activities, and just few adaptations are necessaries to fulfill the redesign objective. This stage is carried out in two

3. Evaluation of alternatives. This stage is carried out manually by the human designer. 4. Implementation of alternatives. This stage is also carried out manually by the human

acquisition and the identification of candidates stages of the framework.

human experts acquire through direct interaction with the system. 4. Aggregation levels. The degree of granularity of the represented knowledge.

**3. The redesign framework**

play an independent role in this framework:

adaptation stages in the framework.

**3.1 Design-description acquisition**

designer.