The design of drinking water treatment plants must consider several objectives and satisfy
multiple constraints. The use of mathematical programming techniques can assist in determining
the optimal treatment plant design. Unfortunately, common practice assumes that raw water
characteristics and model parameters are known (perfect information) when, in fact, they include
either natural variation or experimental uncertainty. Including variability and uncertainty in the
design framework allows for a robust design. A framework is presented for including variability
and uncertainty into the design formulation for particulate removal under conventional treatment
(rapid mix, flocculation, sedimentation, and filtration). As an example, a deterministic design
that assumes perfect information is performed and shown not to be robust with respect to influent
variability and model parameter uncertainty. Individually incorporating one of four variable
influent parameters or three uncertain model parameters in the design process increased design
costs up to 21.2%. The resulting designs were, however, robust with respect to the individual
variabilities/uncertainties. Including multiple variable/uncertain parameters resulted in even
greater design costs than the sum of the individual variability/uncertainty values.
Includes 13 references, tables, figures.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 380 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 11 |
| Published : | 06/16/2002 |
