An approach to gross error detection based on the residual of single node

Measurements such as flow rates from a chemical process are inherently inaccurate. They are contaminated by random errors and possibly gross errors such as process disturbances, leaks, departure from steady state, and biased instrumentation. These measurements violate conservation laws and other process constraints. Data reconciliation aims at estimating the true values of measured variables that are consistent with the constraints, detecting gross errors, and solving for unmeasured variables. The problem of gross error detection and identification became the bottleneck of data reconciliation. A new approach for gross error identification based on the reliability and precision of the flow meters, as well as the residual of a single node, is presented. Simulations are given and a comparison is made between the new approach and some other widely used methods. It is shown that the proposed method is quite effective in gross error identification, especially when the system comprises a relatively large number of measurements.