Data rectification and gross error detection in a steady state process via artificial neural networks

One of the many problems engineers face is that of identifying and eliminating gross errors from measured data, and rectifying collected data to satisfy process constraints such as the mass and energy balances that describe a process. While it is possible to use statistical methods coupled with error reduction techniques to rectify data, the strategy must be carried out iteratively in many steps. Artificial neural networks (ANN) being composed of basis functions yield excellent models, and can be trained to rectify data. We demonstrate the application of an ANN to rectify the simulated measurements obtained from a steady-state heat exchanger. Both random and gross errors added to the simulated measurements were successfully rectified. A comparison was made of the application of ANN with rectification by constrained least squares via nonlinear programming, and the ANN treatment proved to be superior. We conclude that the use of ANN appears to be a promising tool for data rectification