Data reconciliation and gross error detection in chemical process networks

Ajit C. Tamhane*, Richard S.H. Mah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

115 Scopus citations


Measurements made on stream flows in a chemical process network are expected to satisfy mass and energy balance equations in the steady state. Because of the presence of random and possibly gross errors, these balance equations are not generally satisfied. The problems of how to reconcile the measurements so that they satisfy the constraints and how to use the reconciled values to detect gross errors are considered in this article. Reconciliation of measurements is usually based on weighted least squares estimation under constraints, and detection of gross errors is based on the residuals obtained in the reconciliation step. The constraints resulting from the network structure introduce certain identifiability problems in gross error detection. A thorough review of such methodologies proposed in the chemical engineering literature is given, and those methodologies are illustrated by examples. A number of research problems of potential interest to statisticians are outlined.

Original languageEnglish (US)
Pages (from-to)409-422
Number of pages14
Issue number4
StatePublished - Nov 1985


  • Chemical engineering applications
  • Constrained weighted least squares
  • Nonlinear constraints
  • Outlier detection
  • Steady state processes

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics


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