Many researches have been focused on the design of Heat Exchanger Networks
(HEN) for efficient energy management. In order to design a HEN to achieve the
minimum total cost, one has to consider the costs of heat exchanger area, utilities
and exergy loss. Previous researchers normally design HEN based on Pinch Technique
(Linnhoff et al., 1983), Mathematical Programming
(Floudas et al., 1986), Stochastic optimization
(Lewin, 1998; Dipama et al.,
2007) or Exergy Analysis (Kotas, 1995; Lim,
2002; Lim and Manan, 2001; Linnhoff
et al., 1982). Superstructure approach (Umeda
et al., 1978) and genetic algorithm approach (Dipama
et al., 2007) can include all constraints but however is very complex
and is not preferred by practicing engineers. Exergy analysis for HEN design
(Lim and Manan, 2001; Lim, 2002)
which is based on second law of thermodynamic will not minimize heat exchanger
area. In the other hand by minimizing exergy loss, heat exchanger area will
MATERIALS AND METHODS
The current proposed method consists of several stages (Fig. 1) as described next.
Step 1: Construction of segregated enthalpy vs. temperature curve: In order to construct the Segregate enthalpy vs. temperature curve, the first step is to change the supply and target temperatures of the hot and cold streams (Th and Tc) to shifted temperatures (Th1 and Tc1) using Eq. 1 and 2:
The next step is to draw all the hot and cold streams based on the shifted
temperature in the enthalpy vs. temperature plot. The detailed instructions
to draw the segregated enthalpy vs. temperature can be found in (Wan
Alwi and Abdul Manan, 2009). In order to achieve minimum heat exchanger
network area and utility cost, some rules needed to be applied as follow:
||The hot stream which has the biggest heat load is Recommended
to be matched first with cold stream which has the biggest heat load
||It is desirable to match the high temperature hot stream with
the high temperature cold stream. Similarly, the hot stream with lower temperature
is recommended to be matched with cold stream with the lowest temperature.
This action will increase the exergy efficiently of the stream
Step 2: Setting up payback period match table: In step 2, the payback
period is used as decision variable for the stream match. To obtain payback
period, Eq. 3 is used. Areas of heat exchangers are calculated
using Eq. 4 and exergy consumption using Eq.
5. The heat exchanger bare module cost is calculated based on (Biegler
et al., 1997). Assumming operating hours of 8400 h year-1,
price of fuel, cooling water and hot oil are 0.00291, $41.504 and $169.421 kw-1,
respectively, the total cost can be calculated using Eq. 6
and the savings using Eq. 7.
|| Method flow chart
Step 3: Loops and paths optimization: In some cases, the matching process
can lead to some load being left unsatisfied in the networks or leaving excessive
units with relatively small heat loads. Therefore, further evolution on the
networks should be carried out to optimize the networks by reassigning the unsatisfied
heat load and minimizing the number of heat exchanger by loop breaking techniques.
Details explanation about loop breaking and path optimization are explained
by Kemp (2007).
The methodology is tested using the data from (Kemp, 2007)
where it consists of three hot and three cold streams. Table 1
shows the stream data with the shifted temperature assuming ΔTmin
= 10°C. In this stage, the streams matches have not been decided yet.
From the stream data above, the hot and cold streams can be plotted directly to segregate curve. Figure 2 show the preliminary segregate curve of case study 1.
The possible stream match can be gained by applying some rules explained in Step 1. The final segregated curve is shown in Fig. 3.
The next step is to calculate the payback period from all possible matches achieved from the previous step. In order to decide the stream matches with minimum payback period the proposed method use the Payback Period Match Table (PPMT). The PPMT is shown in Table 2. By using the PPMT, information about the minimum payback period for all possible streams matches can be clearly shown.
After choose the smallest payback period for all the streams, the final payback period match table is achieved. As can be seen in Table 3, the stream match which have the minimum payback period are H1 - C1, H2(1) - C2(1), H2(2)-C3 and H3-C2(2).
|| Stream data for study case 1
||Preliminary segregated enthalpy vs. temperature curve for
case study 1
||Final segregated enthalpy vs. temperature curve for case study
|| Payback period match table
|| Final payback period match table
|| Heat exchanger network design for case study 1
||Final heat exchanger networks design using pinch analysis
Figure 3 describe that there are cold stream left unmatched (12 kW). These cold streams will use additional hot utility to satisfy its entire heat load. The final heat exchanger network design is shown in Fig. 4.
In this study, the proposed method results are compared with the results obtained using pinch technique. By using the same case study, the heat exchanger networks obtained is as shown in Fig. 5. The results comparison between proposed method and pinch analysis is shown in Table 3.
|| Comparison result between proposed method and pinch analysis
As can be seen in Table 4, the heat exchanger area cost as well as exergy loss using the proposed method is lower than the results obtained using pinch analysis.
A new systematic method for optimal heat recovery networks design has been introduced. The method minimizes area and exergy consumption for grassroots design.