Plant performance, analysis analyst

Unit layout as installed is the next step of preparation. This may take some effort if analysts have not been involvea with the unit prior to the plant-performance analysis. The equipment in the plant should correspond to that shown on the PFDs and P IDs. Wmere differences are found, analysts must seek explanations. While a hne-by-line trace is not required, details of the equipment installation and condition must be understood. It is particularly useful to correlate the sample and measurement locations and the bypasses shown on the P IDs to those ac tuaUy piped in the unit. Gas vents and liquid (particularly water-phase) discharges may have been added to the unit based on operating experience out not shown on the P IDs. While these flows may ultimately be small within the context of plant-performance an ysis, they may have sufficient impact to alter conclusions regarding trace component flows, particularly those that have a tendency to build in a process. 

These potential sampling problems must be solved in advance of the unit test. The conclusions drawn from any unit test are strongly affected by the accuracy of the sampling methods and the resultant analyses. Methods should be discussed and practiced before the actual unit test. Analysts should use the trial measurements in prehm-inary plant-performance analysis to ensure that the results will be use-bil during the actual unit test. 

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. 

The intensity of the situation requiring the analysis may not allow analysts to develop a formal preparatory review of the unit as described below. Analysts must recognize that the incomplete preparation may result in a less efficient analysis of plant performance. 

Confidence The accuracy of the conclusions drawn from any unit test depends upon the accuracy of the laboratory analyses. Plant-performance analysts must have confidence in these analyses including understanding the methodology and the limitations. This confidence is established through discussion, analyses of known mixtures, and analysis of past laboratory results. This confidence is established during the preparation stage. 

Even within a single sample analysis, it is hkely that some of the reported concentrations are known with greater accuracy than others. L oratory personnel will know which concentrations can be rehed upon and which should be questioned. The plant-performance analyst should know at this stage which of the concentrations are of greatest importance and direct the discussion to those components. 

Once the model parameters have been estimated, analysts should perform a sensitivity analysis to establish the uniqueness of the parameters and the model. Figure 30-9 presents a procedure for performing this sensitivity analysis. If the model will ultimately be used for exploration of other operating conditions, analysts should use the results of the sensitivity analysis to estabhsh the error in extrapolation that will result from database/model interactions, database uncertainties, plant fluctuations, and alternative models. These sensitivity analyses and subsequent extrapolations will assist analysts in determining whether the results of the unit test will lead to results suitable for the intended purpose. 

PROBLEM DEFINITION. This is achieved through plant visits and discussions with risk analysts. In the usual application of THERP, the scenarios of interest are defined by the hardware orientated risk analyst, who would specify critical tasks (such as performing emergency actions) in scenarios such as major fires or gas releases. Thus, the analysis is usually driven by the needs of the hardware assessment to consider specific human errors in predefined, potentially high-risk scenarios. This is in contrast to the qualitative error prediction methodology described in Section 5.5, where all interactions by the operator with critical systems are considered from the point of view of their risk potential. 

The literature includes a number of mis-matches, the following standing as examples for the many The use of bovine liver and other animal tissues for QC in the analysis of hmnan body fluids should not be considered by analysts. The matrix and the levels of trace elements do not match the levels to be analyzed, which may lead to serious errors. An even more severe mis-use was recently reported by Schuhma-cher et al. (1996) for NIST SRM 1577a Bovine Liver, which was used for QC in the analysis of trace elements in plant materials and soil samples in the vicinity of a municipal waste incinerator. Also recently, Cheung and Wong (1997) described how the quality control for the analysis of trace elements in clams (shellfish) and sediments was performed with the same material NIST SRM 1646, Estuarine sediment. Whilst the selected SRM was appropriate for sediments, its usefulness as a QC tool for clams is difficult to prove see also Chapter 8. This inappropriate use is the more mystifying because a broad selection of suitable shellfish RMs from various producers is available. 

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