![](bg8.png)
Table 1 shows the objective values for the four de-
signs. These architectures use different storage tech-
nologies to store excess supplies during intervals of
low demand to satisfy the fluctuations in demand. We
can see that architecture A and C achieve better re-
sults than C and D under the same input assumption.
These comparison can lead to useful insight in know-
ing which component can contribute to the system the
most.
For the purposes of evaluating the model in solv-
ing realistic size problems, we generate four differ-
ent instances from architecture A by varying two di-
mensions:(1) The number of atomic services (AS)
and (2) The number of intervals in the time horizon
(w). Table 2 shows the number of atomic services,
the number of intervals and the time it take the solver
to solve each instance. We can see that when we set
the time horizon (w) to 24 hour, we reach the optimal
solution in 16 seconds and 3 minutes when the num-
ber of atomic services are 78 and 306, respectively.
Whereas, when we use 168 intervals we converge to
near optimal solution within 0.43% gap in 25 seconds
and 1.29% gap in 45 seconds when the number of
atomic services are 42 and 78, respectively. As an
initial step, the solution time to optimality is practical
to operate on an interval (e.g., 1 hour).
Table 2: Shows different variation of problem sizes.
Intervals
(w)
Atomic
Services
(AS)
Gap
(%)
Time
(s)
24 78 0 16
24 306 0 183
168 42 0.03 105
168 78 0.03 693
6 CONCLUSIONS AND FUTURE
WORK
We reported on the development of a formal ana-
lytic model and a decision guidance system for de-
salination service networks that can be applied to a
broad range of desalination designs and architectures.
The model and the system are based on an extensible
repository of atomic component models, initially in-
cluding models for pumps, renewable energy sources,
water and power storage, and reverse osmosis units.
We conducted an experimental study to demonstrate
the applicability of the model and the system to a
range of desalination designs, using four examples,
and the scalability of the solution to realistic size
problem. As future work, we plan to expand the ex-
perimentation to study a realistic water supply chain
using our proposed model. Additionally, we plan to
develop a modular investment model based on the ac-
curate operational model developed in this paper.
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