The optimal approach for dynamic data reconciliation consists in using a complete and exact process model. Unfortunately, such a model is difficult to obtain in industrial practice. Through an example, several observers based on static, stationary and dynamic sub-models are designed and compared to the optimal approach. The comparisons illustrate that, for the given conditions, static observers...

Discrete wavelet transform (DWT) is known for its signal processing ability. In the recent researches, DWT is adopted for signal filtering before executing dynamic data reconciliation. While on-line dynamic data reconciliation is concerned, the computation is heavy duo to the filtering in every time instant. In this article, a shift property of the DWT is indicated and is applied to reduce the ...

Data reconciliation is a model-based technique that reduces measurement errors by making use of redundancies in process data. It is largely applied in modern process industries, being commercially available in software tools. Based on industrial applications reported in the literature, we have identified and tested different configuration settings providing a numerical assessment on the perform...

An integral approach for dynamic data reconciliation that combines a direct numerical integration via Simpson’s rule and data smoothing via discrete wavelet decomposition is presented. By simple numerical integration, the differential-algebraic equations governing the material balances are transformed into algebraic constraints to formulate the reconciliation problem. The frequency responses an...

This paper discusses the current developments within a novel environment to perform related model-based activities. In particular, the paper focuses in the modules corresponding to data preprocessing and dynamic data reconciliation. In terms of the former module, this work discusses the implementation of three approaches based on the minimum median distance (MMD), the moving median (MM), and th...