Data reconciliation of an industrial coal gasification plant

Abstract

The quality of online measured operational data is usually not satisfactory for the performance evaluation of coal gasification plants, because they are never error free, even careful installation and maintenance of the hardware cannot completely eliminate this problem. Data reconciliation is a data preprocessing technique which can improve the accuracy of measured data through process modeling and optimization, and can also be used for gross error detection together with a statistical test method. In this study, data reconciliation and gross error detection are used to an industrial coal gasification plant firstly, which identifies the departing variables, produces missing measurements and evaluates gasification key performance. The reconciled results show that: the accuracy of the primary measurement coal mass flow is improved a lot according to the high Gain value of >42%; the measured raw syngas production is distant from the real value a lot and corrected through reconciliation by 12% lower; the more accurate measurements for the same syngas composition detection are identified; the unmeasured gasifier temperature is calculated with good accuracy of ±56K, and also the distribution of unconverted carbon/ash are calculated; the KPIs are reconciled with improved accuracy due to the improvement of the measurements involved in the KPIs calculation. The results demonstrate that, for such coal gasification plants with complex multiphase logistics and multiple devices, data reconciliation is a valuable tool and shows important significance to help the plants improve measurement accuracy and realize reliable operation.