Errors detection

Regarding the reset of equipment, attentiveness is key, and it must be shown that the measure is efficient and there is no risk of hiding an erroneous situation. 

Seeming hardware architecture can be achieved through five main techniques  

Under this section, we shall present these different techniques and discuss their implementation. 

As shown in Figme 1.7, this technique is intended to complement the hardware architecture with an element for detecting errors in the case of error detection, different solutions may be envisaged, such as restart or cutting off output. 

The implementation of error detection is based on three techniques  

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To determine if a process unit is at steady state, a program monitors key plant measurements (e.g., compositions, product rates, feed rates, and so on) and determines if the plant is steady enough to start the sequence. Only when all of the key measurements are within the allowable tolerances is the plant considered steady and the optimization sequence started. Tolerances for each measurement can be tuned separately. Measured data are then collec ted by the optimization computer. The optimization system runs a program to screen the measurements for unreasonable data (gross error detection). This validity checkiug automatically modifies tne model updating calculation to reflec t any bad data or when equipment is taken out of service. Data vahdation and reconciliation (on-line or off-line) is an extremely critical part of any optimization system. 

Serth, R.W, B. Srikanth, and S.J. Maronga, Gross Error Detection and Stage Efficiency Estimation in a Separation Process, AlChE Journal, 39(10), 1993, 1726-1731. (Physical model development, parameter estimation)... 

Phillips, A.G. and D.P. Harrison, Gross Error Detection and Data Reconciliation in Experimental Kinetics, Indushial and Engineeiing Chemistiy Reseaieh, 32, 1993,2530-2536. (Measurement test)... 

Rollins, D.K. and J.F. Davis, Gross Error Detection when Variance-Covariance Matrices are Unknown, AlChE Journal, 39(8), 1993, 13.35-1341. (Unknown statistics)... 

Serth, R.W. and W.A. Heenan, Gross Error Detection and Data Reconciliation in Steam-Metering Systems, AlChE Journal, 32(5), 1986, 7.3.3-742. 

Verneuil, VS. Jr., P. Yang, and F. Madron, Banish Bad Plant Data, Chemical Engineeiing Piogiess, October 1992, 45-51. (Gross-error detection overview)... 

Intended Use The intended use of the model sets the sophistication required. Relational models are adequate for control within narrow bands of setpoints. Physical models are reqiiired for fault detection and design. Even when relational models are used, they are frequently developed bv repeated simulations using physical models. Further, artificial neural-network models used in analysis of plant performance including gross error detection are in their infancy. Readers are referred to the work of Himmelblau for these developments. [For example, see Terry and Himmelblau (1993) cited in the reference list.] Process simulators are in wide use and readily available to engineers. Consequently, the emphasis of this section is to develop a pre-liminaiy physical model representing the unit. 

Gro.s.s-error-detection methods detect errors when they are not pre.sent and fail to detect the gro.s.s errors when they are. Couphng the aforementioned difficulties of reconciliation with the hmitations of gross-error-detection methods, it is hkely that the adjusted measurements contain unrecognized gross error, further weakening the foundation of the parameter estimation. 

Designers can provide systems to allow error detection and to effect recovery before the error becomes serious. 

During the PHEA stage, the analyst has to identify likely human errors and possible ways of error detection and recovery. The PHEA prompts the analyst to examine the main performance-influencing factors (PIFs) (see Chapter 3) which can contribute to critical errors. All the task steps at the bottom level of the HTA are analyzed in turn to identify likely error modes, their potential for recovery, their safety or quality consequences, and the main performance-influencing factors (PIFs) which can give rise to these errors. In this case study, credible errors were found for the majority of the task steps and each error had multiple causes. An analysis of two operations from the HTA is presented to illustrate the outputs of the PHEA. Figure 7.12 shows a PHEA of the two following tasks Receive instructions to pump and Reset system. 

A measurement element An error detection element A final control element... 

Consider the process flowsheet shown in Figure El6.4, which was used by Rollins and Davis (1993) in investigations of gross error detection. The seven stream numbers are identified in Figure El6.4. The overall material balance can be expressed using the constraint matrix Ay = 0, where A is given by... 

ADEs and medication errors can be extracted from practice data, incidents reports from health professionals, and patient surveys. Practice data include charts, laboratory, prescription data, and administrative databases, and can be reviewed manually or screened by computer systems to identify signals. A method of ADE and medication error detection and classification has been presented that is feasible and has good reliability (Marimoto et al. 2004). It can be used in various clinical settings to measure and improve medication safety. 

The presence of gross errors invalidates the statistical basis of the common data reconciliation procedures, so they must be identified and removed. Gross error detection has received considerable attention in the past 20 years. Statistical tests in combination with an identification strategy have been used for this purpose. A good survey of the available methodologies can be found in Mah (1990) and Crowe (1996). 

Tamhane, A. C., and Mah, R. S. H. (1985). Data reconciliation and gross error detection in chemical process networks. Technometrics, 27, 409-422. 

Tjoa, I., and Biegler, L. (1991), Simultaneous strategies for data reconciliation and gross error detection of nonlinear systems. Comput. Chem. Eng. 15,679. 

The sequential procedure can be implemented on-line, in real time, for any processing plant without much computational effort. Furthermore, by sequentially deleting one measurement at a time, it is possible to quantify the effect of that measurement on the reconciliation procedure, making this approach very suitable for gross error detection/identification, as discussed in the next chapter. 

Serth, R., and Heenan, W. (1986). Gross error detection and data reconciliation in steam metering systems. AIChE J. 32,733-742. 

Alburquerque, J. S., and Biegler, L. T. (1996). Data reconciliation and gross error detection for dynamic systems. AIChE J 42,2841-2856. 

We have discussed, in Chapter 7, a number of auxiliary gross error detection/ identification/estimation schemes, for identifying and removing the gross errors from the measurements, such that the normality assumption holds. Another approach is to take into account the presence of gross errors from the beginning, using, for example,... 

As discussed before, in the conventional data reconciliation approach, auxiliary gross error detection techniques are required to remove any gross error before applying reconciliation techniques. Furthermore, the reconciled states are only the maximum likelihood states of the plant, if feasible plant states are equally likely. That is, P x = 1 if the constraints are satisfied and P x = 0 otherwise. This is the so-called binary assumption (Johnston and Kramer, 1995) or flat distribution. 

Tjoa and Biegler (1991) used this formulation within a simultaneous strategy for data reconciliation and gross error detection on nonlinear systems. Albuquerque and Biegler (1996) used the same approach within the context of solving an error-in-all-variable-parameter estimation problem constrained by differential and algebraic equations. 

Within the context of data reconciliation and gross error detection, Alburquerque and Biegler (1996) used a p function given by... 

The first case study consists of a section of an olefin plant located at the Orica Botany Site in Sydney, Australia. In this example, all the theoretical results discussed in Chapters 4,5,6, and 7 for linear systems are fully exploited for variable classification, system decomposition, and data reconciliation, as well as gross error detection and identification. 

A data reconciliation procedure was applied to the subset of redundant equations. The results are displayed in Table 4. A global test for gross error detection was also applied and the x2 value was found to be equal to 17.58, indicating the presence of a gross error in the data set. Using the serial elimination procedure described in Chapter 7, a gross error was identified in the measurement of stream 26. The procedure for estimating the amount of bias was then applied and the amount of bias was found... 

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