By Dr. Shankar Narasimhan Ph.D. (Ch.E.)

ISBN-10: 0884152553

ISBN-13: 9780884152552

This publication offers a scientific and finished therapy of the diversity of tools to be had for utilizing facts reconciliation concepts. information filtering, info compression and the effect of size choice on facts reconciliation also are exhaustively explained.

facts mistakes could cause titanic difficulties in any method plant or refinery. approach measurements may be correupted by way of energy provide flucutations, community transmission and signla conversion noise, analog enter filtering, adjustments in ambient stipulations, software malfunctioning, miscalibration, and the damage and corrosion of sensors, between different components. here is a booklet that is helping you observe, research, clear up, and stay away from the information acquisition difficulties which can rob crops of top functionality. This fundamental quantity presents the most important insights into info reconciliation and gorss blunders detection suggestions which are crucial fro optimum strategy regulate and knowledge structures.

This booklet is a useful instrument for engineers and executives confronted with the choice and implementation of knowledge reconciliation software program, or for these constructing such software program. For business body of workers and scholars, facts Reconciliation and Gross mistakes Detection is the final word reference.

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Extra info for Data reconciliation & gross error detection : an intelligent use of process data

Sample text

Many software packages like MATLAB and mathematical libraries such as I M S L o r HARWELL have pseudo-random number generators that can b e used for simulation purposes. It should b e noted, however, that it is implicitly assumed in the sirnulation that there are n o rnodel errors (that is, the true values of variables satisfy constraints) and that the measurement errors are norriially distributed with known variances. In practice, since these assumptions may b e violated, the error reduction that can b e achieved will be less than that estimated through simulation.

It should b e noted, however, that it is implicitly assumed in the sirnulation that there are n o rnodel errors (that is, the true values of variables satisfy constraints) and that the measurement errors are norriially distributed with known variances. In practice, since these assumptions may b e violated, the error reduction that can b e achieved will be less than that estimated through simulation. ltimately. the benefits of data reconciliation should b e evaluated in pr-actice through actual inlpro\;ernent in process perforrl1ance.

011the other hand, if li is greater than 5, signi5car;t delay i e s ~ ~ lwith t s a rnargina! improvement iri the noise filtering. ter acts like an exponential filter with a filter paraineter 0 tha: varies depending or1 the rnzgnitude of the difference between filtered and raw measoremen:s. 'he filter paianleter e is low far signals close to the previolls filtered 7,7alut: and high for signals far fionl tine previo:~s filter value. Measurements fai from the previous filtcred valut: have 8 = ! tcring at all oatside the design noise band).

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Data reconciliation & gross error detection : an intelligent use of process data by Dr. Shankar Narasimhan Ph.D. (Ch.E.)


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