Results and Discussion

Production—Demand Ratios

Numerical simulation was used to determine the relationship between total water or power demand and total water or power produced. To minimize the per unit cost of electricity production and water desalination, two important selections must be ac­counted for at a certain time interval: namely, electricity and water demands. In or­der to satisfy electricity generation and freshwater production demands, the optimal

Fig. 46.3 Arena simulation model for experimenting with various electricity production operation strategies in the case study plant

unit configuration and operating conditions are solved as implied in the mathemati­cal model. The benefit of modeling such a system via mathematical descriptions is that the process can be optimized in such a way that the operating factors of cost will be minimized, while simultaneously considering the various constraints.

The power produced and power demand patterns for the case study are shown in Fig. 46.5 while Fig. 46.6 shows the patterns of the water produced and water demanded. While the patterns in Fig. 46.5 and 46.6 shows that the case study plant adequately meets the demand pattern (i. e. the production to demand ratios are al­ways more than 1), the differences in the power/water produced and power/water demand can be streamlined through improved operating practices that aim to opti­mize production with respect to demand. Such practices would also aim at reduc­ing surplus, which if not leased or not consumed in the plant would constitute an opportunity lost.

Figure 46.7 shows the relationships between power produced, estimated power demand and the power used in the case study plant.

From Fig. 46.7 it can be observed that the total power used in-house is about 8 times less (on average) to the power generated. The power used in-house is also fairly constant, which can be considered to be good since energy improvements can be levelized in the whole plant. Figure 46.7 also shows that the power produced ex­hibits variational differences particularly in the months May, June, July, August and October. This variation is related to the climatic variation for which the said months are during summer time according to Doha climate. During this time, more power is needed for air-conditioning and cooling.

Fig. 46.5 Comparison of power produced and power demand

Fig. 46.6 Comparison of water produced and water demand

Figure 46.8 shows a comparison of the water demand and water produced for a period of one month. Figure 46.8 also shows that the water produced exhibits variational differences with particularly low demands in the months of November and December. Ideally, the total water produced must be greater than the total water consumption. From Fig. 46.8, it can be observed that the production volumes are always greater than the demand. Although this is generally regarded as the desired situation it conflicts with lean concepts such as waste of over production and waste of inventory. As such, it was recommended that a further investigation be conducted in order to come up with an optimal water production/demand ratio based on certain policies that may be considered practical regarding the supply of freshwater. Such policies should also include the distribution factors and scenarios from the water distribution company.

Fig. 46.7 Relationships between power produced, power demand and the power used in the case study plant

Fig. 46.8 Comparison of water production and water demand for a period of 1 year

Figure 46.9 shows a comparison of fuel gas consumption for the power genera­tion side and the water desalination side. From Fig. 46.9, it can be observed that the fuel consumed in the power generation side far exceeds that used in the water production side. Figure 46.10 shows that 99 % of natural gas consumed in the case study is used for power generation. Thus more natural gas (fuel) consumption is allocated to the power generation side than in the water desalination process. It can

Fig. 46.9 A comparison of fuel gas consumption for the power generation side and the water desalination side

Fig. 46.10 Relative percentage consumption of natural gas in the case study plant

also be noted from Fig. 46.10 that relatively very small natural gas fuel is used in the months; May, June, July, August, September, October November and December.

Data and information from numerical simulations suggest that the case study plant is capable of providing the required amounts of both power and water. Of concern are the surplus that is implied in the various graphical illustrations shown

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300

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Fig. 46.11

in this section. The variational differences shows that a wide range operational sce­narios and practices are implemented in the case study in a bid to optimize the production/demand ratios. However, the actual dynamics involved in the match­ing of demand and production are more complicated than implied in the previous discussions. This is because the case study plant is only one of the many plants that supply water and power to the State of Qatar. In addition, the case study busi­ness model is complicated by the fact that after the case study generates power and produces water, these commodities are sent to a transmission and distribution company which operates independently. Since these two organizations operate sep­arately, they could be optimized individually. However, the decoupling between these organizations means that each of them have different pull system objectives even though they share the same products. To avoid sub-optimal operations implied in the individually optimized operations, it is necessary for the two organizations to operate within an integrated value optimization framework. Such a framework will go a long way in streamlining operations so that the production quantities are commensurate with demand quantities. The integrated approach also has the ripple effect of reducing the per unit costs of both power and water. Figure 46.11 shows the variational power and water production for a length of 365 days. These variation patterns seem to indicate that the case study plant changes the production (of water or electricity) based on random or fluctuating demands. The random and larger variations in power demands can be explained by the fact that it is easier to estimate demands in water than electricity. However, it is difficult to conclude whether the demand is specific to the case study plant or some of it is used for supporting pro­duction from other plants under the corresponding company or some of the power is leased (since frequent leasing is part of the case study business model).

Fig. 46.12 Variation of the power to water ratio for a simulated run length of 365 days

Figure 46.12 shows a simulated plot of the power to water ratio for a simulated run length of 365 days. From Fig. 46.12 it can be inferred that theoretically (i. e. based on simulated data) the power to water ratio can range from a minimum of 51 to 151.26 kWh/m3, although it is acknowledged that the figure 46.151.26 kWh/m3 is generally beyond practical design of the case study plant. Although the power to water ratio figures are simulated figures, they seem to indicate that the case study plant strategy is to meet the high demands of water. On the other hand, the fuel plant allocation (see Fig. 46.9) seems to indicate a strong presence of power production in the case study plant. Albeit, these facts are very difficult to put into context because the case study plant is only one of the four plants under companies that produce wa­ter and electricity for the population in Qatar. The true context of the simulated data can be further developed by focusing the study on all dual purpose power and water production plants and how they relate to the issues of power and water demands in the State of Qatar.

From the literature review, it was found that by operating at wide range of top brine temperatures, improvements in energy consumption can be realized in the desalination process [11]. In addition, the installed configuration of the desalina­tion plant has an influence on energy consumption. In essence, dual-purpose power desalination plants are used to reduce the production cost of both electricity and water. Various combinations of power-desalination systems can be implemented to satisfy both power and water demands regardless of optimal polices. The preference of one scheme over another depend on many factors such as: the required power to water ratio, the cost of fuel energy charged to the desalting process, electricity sales, capital costs, and local requirements.

Table 46.1 Comparison of water to power ratios for different alternative plant configurations

Alternative configuration

Water/Power ratios

Rank

1

Gas turbine connected to MSF desalination system— control experiment (i. e. setup in the case study plant)

0.013

4

2

Extraction/condensation steam turbine coupled to MSF desalination system

0.031

5

3

Combined cycle gas turbine with backpressure steam turbine coupled to MSF desalination system

0.011

2

4

Backpressure steam turbine coupled to MSF desalination system

0.012

3

5

Combined cycle gas turbine with extraction/conden­sation steam turbine coupled to MSF desalination system

0.010

1

The power to ratios were used in the simulation experiments with the various configurations mentioned in the previous paragraph. Simulation models for these experiments were configured based on the experimental arena models shown in Figs. 46.3 and 46.4. Water and power production statistics were simulated for a sim­ulation length of 365 days. For each configuration, average values of the simulated data (i. e. both water and power) were determined and used to calculate the water to power ratios for the five configurations. The results are shown in Table 46.1.

Based on the water to power ration, alternative (5) (i. e. Combined cycle gas turbine with extraction/condensation steam turbine coupled to MSF) seems to be a better choise as the optimal configuration for dual purpose power and water produc­tion plants. This configuration performs better than the case study plant (alternative (1) in Table 46.1).

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