Errors gross

Gross errors. These result from unpredictable 

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. 

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) 

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) 

Crowe, C.M., Recursive Identification of Gross Errors in Linear Data Reconciliation, AJChE Journal, 34(4), 1988,541-550. (Global chi square test, measurement test) 

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) 

Madron, F, A New Approach to the Identification of Gross Errors in Chemical Engineering Measurements, Chemical Engineeiing Science, 40(10), 1985, 1855-1860. (Detection, elimination) 

Terry, P.A. and D.M. Himmelhlau, Data Rectification and Gross Error Detection in a Steady-State Process via Artificial Neural Networks, Indushial and Engineeiing Chemistiy Reseaieh, 32, 199.3,. 3020-3028. (Neural networks, measurement test) 

Nounou, M. N., and Bakshi, B. R., On-line multiscale fillering of random and gross errors without process models, AIChE J., 45(6), 1041 (1999). 

Romagnoli, J.A. and G. Stephanopoulos, Rectification of Process Measurement Data in the Presence of Gross Errors, Chemical Engineeiing Science, 36(11), 1981, 1849-186.3. 

The methods discussed in the technical hterature are not exact. Numerical simulations of plant performance show that gross errors frequently remain undetected when they are present, or measurements are isolated as containing gross errors when they do not contain any. 

This inexact performance leads to the recommendation that measurement sets should be discarded in their entirety when gross errors are detected. Therefore, actual isolation of which measurements contain error is not necessary when entire sets are discarded. 

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

Measurement Test This test compares the adjusted measurements to the actual measurements. In so doing, each measurement is tested for gross error. From the reconciliation development, 

This test does not require reconciliation before it is applied. However, should the null hypothesis be rejected, it only indicates that a gross error might be present. It does not isolate which of the measurements (or constraints) are in error. Consequently, gross-error isolation must be done subsequently. 

The adjusted mea.surements are not unique and may he no better than the actual mea.surements. Simulation studies testing reconciliation methods in the absence of gross error show that they arrive at a better estimate of the actual component and stream flows 60 percent of the time 40 percent of the time, the acdual measured values better represent the unit performance. 

The authors test two methods coupled with the measurement test. In one, they sequentially eliminate measurements and rearrange the constraints to isolate the specific measurements that contain gross errors. In the other, streams are added back as the search continues. 

However, other bias errors are so substantial that their presence will significantly distort any conclusions drawn from the adjusted measurements. Rectification is the detection of the presence of significant bias in a set of measurements, the isolation of the specific measurements containing bias, and the removal of those measurements from subsequent reconcihation and interpretation. Significant bias in measurements is defined as gross error in the literature. 

Unlike the other two tests, this is associated with each measurement. Reconcihation is required before this test is apphed, but no further isolation is required. However, due to the limitations in reconciliation methods, some measurements can be inordinately adjusted because of incorrectly specified random errors. Other adjustments that do contain gross errors may not be adjusted because the selected constraints are not sensitive to these measurements. Therefore, even though the adjustment in each measurement is tested for gross error, rejection of the mill hypothesis for a specific measurement does not necessarily indicate that that measurement contains gross error. 

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