Generic error modelling system

FIGURE 2.5. Dynamics of Generic Error Modeling System (GEMS) (adapted from Reason, 1990). 

Reason, J.T. (1987). Generic Error-Modelling Systems (GEMS) a cognitive framework for locating human error forms. In J. Rasmussen, K. Duncan and J. Leplat (eds). New Technology and Human Error. Wiley. New York. 

Figure 13.6 Dynamics of generic error-modelling system (CEMS). (From Reason 0...

This simple example is very instructive and shows the basic key features of classical error-correction. First, one has to assume a particular and physically motivated error model, one cannot fight a completely unknown enemy Then, one applies the following generic scheme. First, one encodes information on well-chosen states of an extended and redundant system of bits. For instance, in the repetition code, the original bit of information is encoded on two particular states of a three bit system. The idea is clear redundancy prevents information from serious damage due to the errors and assures very likely recovery (let us emphasize that one uses the same kind of trick in everyday life when asking someone to repeat a sentence or a question to make sure of every word). Finally, after the transmission through the noisy channel, one decodes... 

Finally, the MOS should also take into account the uncertainties in the estimated exposure. For predicted exposure estimates, this requires an uncertainty analysis (Section 8.2.3) involving the determination of the uncertainty in the model output value, based on the collective uncertainty of the model input parameters. General sources of variability and uncertainty in exposure assessments are measurement errors, sampling errors, variability in natural systems and human behavior, limitations in model description, limitations in generic or indirect data, and professional judgment. 

In Equations (6.1) to (6.3), is the reactor outlet temperature in °C, and Tapp is the approach temperature calculated from the gas analysis for the Boudouard reaction in K. The according pressure-based equilibrium constant is represented by p,B in bar and was fitted (ln(/ p) over 1/T) with a third-order polynomial expression, is the total system pressure in bar and xqo is the mole fraction of CO in the product gas. All variables are imported automatically and the equations are solved iteratively. Of course, the Boudouard reaction itself is not valid as soon as carbon is set as inert. However, the calculation procedure provides a temperature and pressure dependent empirical ( pseudo-Boudouard ) expression that relates to CO2/CO and permits a robust correlation for this generic model with a smooth transition to the zone where carbon is present. Including this modification, the model results for the validation case indicate the right order of magnitude for the CO2 concentration (1.22% deviation of the molar flows). The hydrogen balance of the case from the literature had a feilure rate of 3.13% hence, the model with a closed balance predicts the same excessive amount All error for the other components could be reconciled to less than 1.3% each. 

In our case, this allowed us to model and verify a distributed system with an arbitrary number of units and sensors. Moreover, the introduced constants became the system parameters that may vary from one application to another. For instance, different sensors may have different valid thresholds, while the error value to be displayed may also depend on a particular type of a display. Furthermore, the software functions used to convert the temperature or calculate the output value may differ even within the same system. Nonetheless, the derived formal proofs of the data freshness and integrity properties hold for any valid values of the generic parameters. We believe that the presented approach can also be used in other domains without major modifications. 

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