Collection of benchmark test problems for data reconciliation and gross error detection and identification

Abstract In an industrial scenario, one can find measured data that do not satisfy the mass and energy laws of conservation. This problem can be approached by applying data reconciliation (DR) and gross error detection and identification (GEDI) techniques, however, authors generally validate their methods using a reduced set of problems, restricting the application of the proposed methods to them. The objective of this work is to present a collection of benchmark problems for DR and GEDI to help the evaluation of these methods in different types of flowsheets. First, challenges issues related with DR and GED are presented with examples. Then, a general overview of the benchmark collection set is presented. In conclusion, it can be observed that this challenging research area needs a common problem set for validating DR and GEDI and this paper fills this gap, helping the validation of the methods.

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