Data Reconciliation of an Industrial Petrochemical Plant Case Study: Olefin Plant (Hot Section)

The quality of online measured operational data is usually not satisfactory for the performance monitoring of olefin plants, due to the low accuracy of measuring instrument. Data reconciliation (DR) is a data processing technique that can improve the accuracy of measured data through process modeling and optimization, and can also be used for gross error detection together with statistical test method. In this study, DR and gross error detection are applied to an industrial olefin plant in order to demonstrate their usefulness for gaining of accurate mass and energy balance for accounting center. DR simulation results showed the relative root mean squared errors of the primary flow measurements, namely the inlet mass flow rate of feed to F-101, the inlet mass flow rate of feed to F-102, and the inlet mass flow rate of feed to F-104 are reduced by 40.3%, 13.4%, and 21.4%. Reconciliation process results showed DR is effective for accuracy improvement of operating data. So, authors provide a case study where gross error detection is performed together with global test and serial elimination strategies, and a gross error in the measurement of existed olefin plant was successfully detected and validated by onsite inspection of the olefin plant.