Data Reconciliation Using AspenPlus

Process measurements made in chemical plants generally do not satisfy material and energy balance constraints due to random or possibly gross errors in the measuring device readings. Data reconciliation is a method of adjusting random errors in the measurements in a weighted least squares sense in order to satisfy the process constraints. Linear data reconciliation involves solving a linear system of mass and energy balances as well as inequality constraints. Non‐linear data reconciliation, involving non‐linear mass and energy balances and constraints, is substantially more complex and requires a significant amount of work in developing the model and/or solution strategy. Steady state simulation packages equipped with optimization routines can be used to perform data reconciliation and parameter estimation with existing models which automatically satisfy mass and energy balance constraints. The time required to develop a data reconciliation problem can be shortened by using these packages without sacrificing the quality of the results. Five examples are presented to illustrate this technique using the AspenPlus flowsheet simulation and Optimization system. The first four are simple problems taken from literature and are included to validate the method. The fifth example involves a nonlinear industrial distillation example included to illustrate the scope of the technique. In the fist four cases, the solution to the data reconciliation problem war easily developed and quickly solved. The fifth example involved somewhat more work but was still relatively quickly developed and solved. Steady state process simulation systems can be used to perform non‐linear data reconciliation. The reduction in development time increases dramatically with the problem complexity involving staged separation and multiple units.