Algebra of Data Reconciliation

Data Reconciliation

1. Scope, aims and benefits of Data Reconciliation 1.1. Importance of Measurements for Process Monitoring 1.2. Sources of Experimental Errors 1.3. How to Achieve Measurement Redundancy 2. Exploiting redundancy 2.1. Variable Classification 2.2. Benefits of Model Based Data Validation 3. Mathematical formulation of the validation problem 3.1. Data Validation for Steady State Systems 3.2. Solution...

Balancing and Data Reconciliation Minibook

Reconciliation and Merging of Heterogeneous XML Data

With the popularity of information repositories, merging information available at various heterogeneous data sources has become an interesting problem. The data at the various sources might itself be structured, semi-structured or unstructured and the integration process involves reconciliation at various levels. XML has recently emerged as a good candidate in providing a unified view through a...

Dynamic Data Reconciliation for Improved Process Performance

One of main thrusts of modern plant operation is to improve the quality of online information in distributed control system (DCS). Accurate information about the current state of a process is paramount for plant monitoring and control. Unfortunately, process measurements are often corrupted by measurement noise. The presence of measurement noise not only prevents plant operators from identifyin...

Combined Data Reconciliation and Parameter Estimation

This paper presents a nonlinear approach for data reconciliation. The advantages of this approach over the conventional extended Kalman filtering (EKF) are namely, less linearization errors are generated in the process, and secondly the choice of the objective function is flexible. In this work, two probability density functions, namely the Logistic and Lorentz distribution are proposed as the ...