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7/174 Leak Impact Factor
           used to estimate hazard zones. Such estimates are an important   pipe that would contribute to a specific
            part of modeling when absolute risk estimates are sought (see   leak location, scaled from 0 to IO.
            Chapter  14). The  calculations underlying the  estimates are   Pipe-diameter   pipe diameter
            important in a relative risk assessment because they identify the   Max flow rate   pumped flowrate of product
            critical variables that make one release potentially more conse-   HVA   high value area, as defined in this chapter,
            quential than another. They also help the evaluator to better   susceptible to spill damage
            understand the threat from the pipeline and more appropriately   Public lands   binary  measure  of  presence  of  national
            characterize receptors that are potentially damaged.    parks, wildlife refuges, etc. susceptible  to
             Damage states that can be used to define hazard zones are   spill damage
            discussed in Chapter 14.                   Water       binary measure of presence of water body
                                                                    susceptible to spill damage
                                                       Population   0-10  point  scale  indicating  relative
            IX.  Leak impact factor sample                          population  density  susceptible to  spill
                                                                    damage
            Many approaches are possible for evaluating the relative conse-   Water intake   binary  measure  of  presence  of  drinking
            quences of a pipeline failure. For each component of the LIF   water intake structure susceptible to spill
            that should be considered, some sample scoring protocols have   damage
            been  presented.  Some additional algorithm samples can be   LIF   Leak impact factor, as defined in this chapter
            found in Appendix E and the case studies of Chapter 14.
                                                        This sample algorithm is a high-level screening tool used to
            Leak Impact Factor Samples                 identify changes in consequence along the route of a specific
                                                       pipeline. The relative consequences are measured by the LIF,
            In this sample LIF algorithm, a liquid pipeline operator uses the   whose main components are
            relationships shown in Table 7.23 to evaluate the LIE A brief
            description of the variables used is as follows:   Product hazard (PH)
                                                        Receptors (R)
            prod-haz    product hazard, scored as described in this   Spill volume (S)
                         chapter                        Spread range or dispersion (D)
            spill       a score ranging from 0 to 1 .O proportional to
                         relative volume of potential release;  1 .O   where
                         reflects largest  volume  spill possible in   S is a function of pumping rate, leak detection capabilities,
                         this risk model               drain volume, and emergency response capabilities; and
            v1          volume  lost  to  leak  prior  to  system shut
                         down                                        LIF= PH x Rx S x D
            v2          volume lost to leak from detection to system
                         isolation                      This model is applied to a pipeline transporting butadiene,
            v3          volume  lost  to  leak  due  to  drainage  of   whose product hazard is greater than for most hydrocarbons-
                         isolated pipeline section     about twice as high as for butane or propane. A higher health
            Spread      measure of relative dispersion range   hazard score (Nh per NFPA), reactivity score (N,  per NFPA),
            Overland    measure  of  relative  dispersion  due  to   and a lower CERCLA reportable quantity create the higher
                         surface flows                 hazard level.
            Subsurface   measure  of  relative  dispersion  due  to   In the initial application of this algorithm, changes in conse-
                         subsurface flows              quence are thought to be driven solely by changes in operating
            Drain       surrogate for drain volume, this is actually   pressure and  population  density  along  this  pipeline.  Other
                         the upstream and downstream lengths of   variables are included in the model but are not used initially.


            Table 7.23  Algorithms for scoring the leak impact factor
            LIF         [(prod-ha) x (spill) x (spread) x (receptors)]
            Product hazard   (prod-haz)            Product hazard is calculated elsewhere and stored as a database variable
            Spill       { [(Vl) + (V2) + (v3)]/23000)/10 + 0.2   3 components of total spill volume are adjusted by scaling factors
            Spread      [(overland)/3 + (subsurface)/8J   Variables adjusted by scaling factors
            Receptors   [(population) + (HVA) + (public-lands)   Total receptor score is sum of individual receptor scores, weighted elsewhere
                         + (wetlands) + (water-intake) + (waters)]
            v1          [(ma flow rate)/l2]        Spill volume contributed by pumping flow rate
            v2          (0)                        Volume contributed by leak detection and response time; to be included later
            v3          [(drain) x (pipe_diameter)*]   Contributing lengths of upstream and downstream pipe are adjusted hy pipe
                                                     diameter as surrogate volume calculation
            High value   (HVA)
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