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.

在线测量的运行数据的质量对于煤气化装置的性能评估通常不能令人满意，因为它们从来都不是无差错的，即使硬件的仔细安装和维护也不能完全消除这个问题。数据核对是一种数据预处理技术，它可以通过过程建模和优化来提高测量数据的准确性，也可以与统计测试方法一起用于粗差检测。在这项研究中，数据核对和粗差检测首先用于工业煤气化厂，识别偏离变量，产生缺失的测量值并评估气化关键性能。调和结果表明：增益值>42%；测得的粗合成气产量与实际值相差很多，通过对账校正后降低了 12%；确定相同合成气成分检测的更准确测量值；未测量的气化炉温度计算精度良好，精度为±56K，并计算未转化碳/灰的分布；由于 KPI 计算中涉及的测量的改进，KPI 与提高的准确性相协调。结果表明，对于这种具有复杂多阶段物流和多设备的煤气化装置，数据核对是一种宝贵的工具，对于帮助装置提高测量精度和实现可靠运行具有重要意义。