Judgmental estimates

There Is one general conclusion I do feel quite comfortable In drawing. Far too much risk assessment that Is done as slngle-value-best-estlmate analysis should In fact be done as probabilistic analysis. There are undoubtedly several reasons for this. Performing probabilistic analysis can get analytically messy. Obtaining subjective Judgmental estimates of uncertain coefficients can be awkward uid Is subject to a variety of pitfalls. And, idille most people appesu to be quite comfortable with such basic notions of uncertainty as "odds" unless care Is taken, the results of probabilistic analysis can become somewhat difficult to communicate to a semi-technical or non-technical audience. 

In establishing a risk level, two judgmental estimates must be made of the probability of a hazard-related incident or exposure occurring, and of the severity of harm or damage that may result. Rarely will precise incident or exposure probability data be available, and the differences in the estimates made by risk assessors of the severity of harm or damage that can occur in a given situation may be very large. 

No single value for a profitability estimate should be accepted without further consideration. An inteUigent consideration of the cumula-tive-cash-flow and cumulative-discounted-cash-flow curves such as those shown in Fig. 9-10, together with experience and good judgment, is the best way of assessing the financial merit of aprojec t. 

Because the technical barriers previously outhned increase uncertainty in the data, plant-performance analysts must approach the data analysis with an unprejudiced eye. Significant technical judgment is required to evaluate each measurement and its uncertainty with respec t to the intended purpose, the model development, and the conclusions. If there is any bias on the analysts part, it is likely that this bias will be built into the subsequent model and parameter estimates. Since engineers rely upon the model to extrapolate from current operation, the bias can be amplified and lead to decisions that are inaccurate, unwarranted, and potentially dangerous. 

Parameter estimation is a procedure for taking the unit measurements and reducing them to a set of parameters for a physical (or, in some cases, relational) mathematical model of the unit. Statistical interpretation tempered with engineering judgment is required to arrive at realistic parameter estimates. Parameter estimation can be an integral part of fault detection and model discrimination. 

As with troubleshooting, parameter estimation is not an exact science. The facade of statistical and mathematical routines coupled with sophisticated simulation models masks the underlying uncertainties in the measurements and the models. It must be understood that the resultant parameter values embody all of the uncertainties in the measurements, underlying database, and the model. The impact of these uncertainties can be minimized by exercising sound engineering judgment founded upon a famiharity with unit operation and engineering fundamentals. 

Estimation of corrosion likelihood results from consideration of the characteristics of the soils and of the installed object, which are tabulated in Table 4-1 for nonalloyed and low-alloy steel products. Rating numbers, Z, are given according to the data on individual characteristics from which a further judgment can be made using the sum of the rating numbers. 

Now you can reconsider the material balance equations by adding those additional factors identified in the previous step. If necessary, estimates of unaccountable losses will have to be calculated. Note that, in the case of a relatively simple manufacturing plant, preparation of a preliminary material-balance system and its refinement (Steps 14 and 15) can usefully be combined. For more-complex P2 assessments, however, two separate steps are likely to be more appropriate. An important rule to remember is that the inputs should ideally equal the outputs - but in practice this will rarely be the case. Some judgment will be required to determine what level of accuracy is acceptable, and we should have an idea as to what the unlikely sources of errors are (e.g., evaporative losses from outside holding ponds may be a materials loss we cannot accurately account for). In the case of high concentrations of hazardous wastes, accurate measurements are needed to develop cost-effective waste-reduction options. It is possible that the material balance for a number of unit operations will need to be repeated. Again, continue to review, refine, and, where necessary, expand your database. The compilation of accurate and comprehensive data is essential for a successful P2 audit and subsequent waste-reduction action plan. Remember - you can t reduce what you don t know is therel... 

If the monitoring data, mass balance, or omission factor used to estimate the release Is not specific to the toxic chemical being reported, the form should identify the estimate as based on engineering calculations or best engineering judgment. 

All data available at your facility must be utilized to calculate treatment efficiency and influent chemical concentration. You areDfll required to collect any new dataforthe purposes of this reporting requirement. If data are lacking, estimates must be made using best engineering judgment or other methods. 

O - Estimate is based on other approaches such as engineering calculations (e.g., estimating volatilization using published mathematical formulas) or best engineering judgment. This would Include applying an estimated removal efficiency to a wastestream, even if the composition of the stream before treatment was fully characterized by monitoring data. 

Experience and judgment as to fouling severity are required to estimate fouling factors (FF , FFj) to determine the overall heat transfer... 

Confidence limits for the conclusions cannot be expressed simply because of the complexity of hazard assessments. The estimation of uncertainties is itself a process subject to professional judgment. However, the team s estimates of probability are believed to be realistic, but may be pessimistic by a factor of perhaps two or three, but less than a factor of ten. Uncertainties also exist... 

Comer, M. K. et al, enerating Human Reliability Estimates using Expert Judgment. SNL, November 1984. 

Risk analysis is an assessment of tlie likelihood (probability) of an accidental release of a hazardous material and tlie actual consequences that might occur, based on tlie estimated vulnerable zones. The risk analysis is a judgment of probability and severity of consequences based on tlie history of previous incidents, local experience, and tlie best available current technological information. It provides an estimation of ... 

To make wise judgments requires that individuals know what experts estimates of the risks are, what it would cost (in terms of their otlier values) to reduce tlieni, and how certain and free of bias tlie estimates are. Scientific precision is not needed, but a sense of whether a risk is big, medium, small, or infinitesimal is. The challenge of risk coinmunication is to provide... 

Cause-consequence analysis serx es to characterize tlie physical effects resulting from a specific incident and the impact of these physical effects on people, the environment, and property. Some consequence models or equations used to estimate tlie potential for damage or injury are as follows Source Models, Dispersion Models, Fire Explosion Models, and Effect Models. Likelihood estimation (frequency estimation), cliaractcrizcs the probability of occurrence for each potential incident considered in tlie analysis. The major tools used for likelihood estimation are as follows Historical Data, Failure sequence modeling techniques, and Expert Judgment. 

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