(22d) Importance of Data Reconciliation on Improving Performances of Crude Refinery Preheat Trains

Performance of a preheat train
is evaluated through plant monitoring data (temperature and flow measurements);
here the reliability of data reconciliation plays a significant role. Most
preheat trains do not monitor all stream temperatures (or flow rates) and
frequently guess the missing data through ?short-cut' methods. Wrong choice of
?short-cut' methods such as the use of linear interpolation is shown to result
in misleading conclusions.  

This paper introduces a
systematic data reconciliation approach through three steps. 1) Generation of missing
data through a modified preheat train simulator based on Energy Fuels, 2009, 23(3), pp 1323?1337. 2) Filtering unreliable data
through a ?trusted' heat balance. 3) Grouping heat exchanger monitoring data
into different time sections to identify trends under different operating
periods.

The superiority of the new data
reconciliation methodology is illustrated through a series of case studies. The
reconciled data are then utilized to make key operational decisions to improve
preheat train performance.