Now showing items 1-3 of 3
Comparison of different artificial neural nets for the detection and location of gross errors in process systems
The reliability of the data which characterize the behavior of a plant is critical to the effective monitoring and improvement of plant performance. It is thus essential that gross errors in these data, which can arise ...
Identification of gross errors in material balance measurements by means of neural nets
Reliable sets of steady-state component and total flow rate data form the cornerstone for the monitoring of plant performance. The detection and isolation of gross errors in these data constitute an essential part of the ...
The use of neural nets to detect systematic errors in process systems
The monitoring of plants and the verification of process models depend crucially on reliable sets of steady state component and total flow rate data. These measurement data are generally subject to random noise (and possibly ...