Crude oil refineries use a series of measurement instruments to monitor process units. One of these units is the pre-heat train. Flow rate and temperature measurements are taken from specific locations for monitoring purposes, especially fouling deposition. The availability of these measurements is related to the instrumentation cost, which limits the number of instruments installed on each unit. Dealing with missing measurements requires specific techniques for allowing the estimation of such variables. Data reconciliation and gross error detection can be integrated to improve the accuracy of process measurements. This work presents an integrated approach that considers the estimation of missing measurements, identification and estimation of measurement bias and reconciliation of measured data. The set of reconciled data is used for determining fouling models to predict the thermal performance of a crude oil pre-heat train. The fouling models can also be used for fouling mitigation and optimisation of cleaning schedules.

原油精炼厂使用一系列测量仪器来监控过程单元。这些单元之一是预热机组。流量和温度测量值是从特定位置获取的，用于监控目的，尤其是结垢沉积。这些测量的可用性与仪器成本有关，这限制了每个单元上安装的仪器数量。处理缺失的测量值需要特定的技术来估计这些变量。可以整合数据对账和总错误检测，以提高过程测量的准确性。这项工作提出了一种综合方法，该方法考虑了丢失测量的估计，识别和估计测量偏差以及对测量数据进行核对。该组对帐数据用于确定结垢模型，以预测原油预热机组的热性能。结垢模型还可用于减轻结垢并优化清洁时间表。