Database errors

This database system is implemented in only a few instances becatisc of its complexity and its liability to errors, although it is a model for the World Wide Web,... 

Most databases secure their information from printed sources. On occasion, however, a subsequent letter to the editor of a pubHcation by a company mentioned in the article will point out an error. Unfortunately, these corrections are not always picked up by the respective databases that entered the initial data. 

The second classification is the physical model. Examples are the rigorous modiiles found in chemical-process simulators. In sequential modular simulators, distillation and kinetic reactors are two important examples. Compared to relational models, physical models purport to represent the ac tual material, energy, equilibrium, and rate processes present in the unit. They rarely, however, include any equipment constraints as part of the model. Despite their complexity, adjustable parameters oearing some relation to theoiy (e.g., tray efficiency) are required such that the output is properly related to the input and specifications. These modds provide more accurate predictions of output based on input and specifications. However, the interactions between the model parameters and database parameters compromise the relationships between input and output. The nonlinearities of equipment performance are not included and, consequently, significant extrapolations result in large errors. Despite their greater complexity, they should be considered to be approximate as well. 

The first two examples show that the interaction of the model parameters and database parameters can lead to inaccurate estimates of the model parameters. Any use of the model outside the operating conditions (temperature, pressures, compositions, etc.) upon which the estimates are based will lead to errors in the extrapolation. These model parameters are effec tively no more than adjustable parameters such as those obtained in linear regression analysis. More comphcated models mav have more subtle interactions. Despite the parameter ties to theoiy, tliey embody not only the uncertainties in the plant data but also the uncertainties in the database. 

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. 

Model Development PreHminary modeling of the unit should be done during the familiarization stage. Interactions between database uncertainties and parameter estimates and between measurement errors and parameter estimates coiJd lead to erroneous parameter estimates. Attempting to develop parameter estimates when the model is systematically in error will lead to systematic error in the parameter estimates. Systematic errors in models arise from not properly accounting for the fundamentals and for the equipment boundaries. Consequently, the resultant model does not properly represent the unit and is unusable for design, control, and optimization. Cropley (1987) describes the erroneous parameter estimates obtained from a reactor study when the fundamental mechanism was not properly described within the model. 

The presence of errors within the underlying database fudher degrades the accuracy and precision of the parameter e.stimate. If the database contains bias, this will translate into bias in the parameter estimates. In the flash example referenced above, including reasonable database uncertainty in the phase equilibria increases me 95 percent confidence interval to 14. As the database uncertainty increases, the uncertainty in the resultant parameter estimate increases as shown by the trend line represented in Fig. 30-24. Failure to account for the database uncertainty results in poor extrapolations to other operating conditions. 

The second method for mixture analysis is the use of specialized software together with spectral databases. We have developed a mixture analysis program AMIX for one- and multidimensional spectra. The most important present applications are the field of combinatorial chemistry and toxicity screening of medical preparations in the pharmaceutical industry. An important medical application is screening of newborn infants for inborn metabolic errors. 

Figure 17.2 An example of prediction of the conformations of three CDR regions of a monoclonal antibody (top row) compared with the unrefined x-ray structure (bottom row). LI and L2 are CDR regions of the light chain, and HI is from the heavy chain. The amino acid sequences of the loop regions were modeled by comparison with the sequences of loop regions selected from a database of known antibody structures. The three-dimensional structure of two of the loop regions, LI and L2, were in good agreement with the preliminary x-ray structure, whereas HI was not. However, during later refinement of the x-ray structure errors were found in the conformations of HI, and in the refined x-ray structure this loop was found to agree with the predicted conformations. In fact, all six loop conformations were correctly predicted in this case. (From C. Chothia et al.. Science 233 755-758, 1986.)...

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 you operate a computerized documentation system, your problems can be eased by the versatility of the computer. Using a database you can provide users with all kinds of information regarding the nature of the change, but be careful. The more you provide the greater the chance of error and the harder and more costly it is to maintain. 

There is considerable interest in developing a database on human error probabilities for use in chemical process quantitative risk assessment (CPQRA). Nevertheless, there have been very few attempts to develop such a database for the CPI compared, for example, with the nuclear industry. Some of the reasons for this are obvious. The nuclear industry is much more highly integrated than the CPI, with a much greater similarity of plant equipment... 

All the sections must be completed by the user and then submitted to a single administrator for addition to the database. Upon completion of the form, the user has the option of making a check submission, which processes the data and performs error checks as normal, but displays the verdict on screen for the user rather than sending the data to the administrator. A variety of errors are checked, including missing data and inconsistent data, invalid molecular structures or numeric data outside the normal range. When the user is satisfied with the form data, they can be submitted to the administrator via the exporf button. Upon submission, the data are stored... 

Automated data acquisition The object of using microprocessor-based systems is to remove any potential for human error, reduce manpower and to automate as much as possible the acquisition of vibration, process and other data that will provide a viable predictive maintenance database. Therefore the system must be able to automatically select and set monitoring parameters without user input. The ideal system would limit user input to a single operation. However this is not totally possible with today s technology. 

The procedure illustrated here, besides containing only trivial technical calculations, lacks important features that are required in production programs. Extensive error checking and recovery must be performed. The procedure must detect the occurrence of a self-referential system of formulas, which would result in attempting endless recursive calls. Access to multiple raw material and formula databases adds power to the program, but must be implemented by complex code to allow flexible control of that access. The structural and input/output statements to support these features may greatly exceed the number of statements that perform modelling calculations. 

Gilks WR, Audit B, De Angelis D, Tsoka S, Ouzounis CA. Modeling the percolation of annotation errors in a database of protein sequences. Bioinformatics 2000 18 1641-9. 

With this database in hand, a simple question is asked [29] How different is a knowledge-based potential derived from this lattice database compared to the actual energy function used to construct the database If statistical errors are negligible and the knowledge-based method is perfect, the answer is expected to be They are exactly the same. ... 

The local user performs data entry by directly entering data into the system s database stored on the local computer with customized electronic forms. The system performs edit checks, which include range, across-form, and across-visit checks at the time of entry. This feature greatly reduces data error rates. 

There has been a push for direct data collection (DDC) as an alternative to remote data capture (RDC). In this approach most of the required clinical data are acquired directly from existing patient record systems such as MRI machines, ECG, EEG, TTM, laboratories, and other measurement equipment. This approach eliminates the need for paper transcription and reentry to another system. It promises error-free and resource-efficient data capture, which allows early locking of the database and therefore potentially earlier product launch [30]. 

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