Data reconciliation is a technique that provides an optimal estimate of true process values by reconciling measured data with known constraints of the system. It accounts for inaccuracies in measurements due to factors like instrumentation errors, fouling, or process fluctuations. Using data reconciliation results in measured data that is ready for meaningful insights, agrees with all constraints, can predict unmeasured variables, and identify sources of inaccuracy. While it has advantages, setting up data reconciliation for a specific process can take time due to requirements of working with process engineers and customizing it to the unique process flow diagram and data structure.

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Data Reconciliation for Chemical Process Systems
Computational CoE,
Shell Technology Center Bangalore, India
Senthil K. Vadivelu
DR gives an optimal estimate of the
true value, which meets all known
constraints in the system.
Measured = True value + Error
Random Gross
Parameters have
lesser uncertainty
Better economic estimates, stable
control of nonlinear systems etc.
Ref. 1
Ref. 2
References:
1. Hodouin D (2010). Process Observers and Data Reconciliation Using Mass and Energy
Balance Equations, Chapter 2 in Advanced Control and Supervision of Mineral Processing
Plants, part of the series Advances in Industrial Control, pg. 15 - 83.
2. Knopf FC (2012). Modeling, Analysis and Optimization of Process and Energy Systems,
John Wiley & Sons.
Measured process data inevitably contain inaccurate information
process fluctuations | faulty instrumentation| drift in calibration | fouling | ….
Decisions based on inaccurate data can cost
millions in business and safety
Use the optimal estimate of the true value given by:
Data Reconciliation
Advantages
 Measured data ready for meaningful insights
 Data in agreement with all the system constraints
 Predict unmeasured variables
 Identify sources of inaccuracy
Data Reconciliation gives an optimal estimate of the true value which meets
all known constraints in the system
Examples of equality constraints
Examples of inequality constraints
Total mass balance Molecular balance
across mixers &
separators
Atomic balance
across reactors
Energy & momentum
balances
Aged catalyst
selectivity lower than
fresh catalyst
Reactant molar flow
more at inlet than at
outlet
Product molar flow
more at outlet than at
inlet
Requires close working with
the process engineer or SME.
Setting up of DR often takes
time since its implementation
is specific to a given process
flow diagram and its data
structure.