One of the major challenges for energy companies is to adapt their process plants to the continuous improvements of available technologies, so as to make their old plants as competitive and cost-efficient as the new ones. Along these lines, process stream data was recently collected for analysing opportunities for improved process integration of the Hydrocracker Unit of a major oil refinery located in Lysekil on the West Coast of Sweden. However, inconsistencies in the process data measurements, e.g. energy balances that do not add up, made the study cumbersome. For analysing heat exchanger networks it is essential to establish sets of process data with balanced heat balances for the existing heat exchangers. The aim of this thesis project was to develop a computer-based solution for systematic analysis, identification and correction of the “raw” data obtained from process data measurements in order to acquire such a consistent set of data. With this purpose, a tool for Data Reconciliation and Gross Error Detection for process stream data was developed using Visual Basic in Microsoft Excel. The tool is based on the Modified Iterative Measurement Test. A second tool, which is easier for handle large data sets and especially designed for networks with non-linear constraints was also developed. This second tool is only able to solve Data Reconciliation problems, so it is targeted for sets of data where there are exclusively random errors. Both developed tools were used to analyse the data set collected from the refinery’s Hydrocracker Unit with the purpose of generating a consistent set of data with balanced heat exchangers. The solution proposed is an energy balanced network, where from the 32 temperature measurements, all the reconciled values, except two, are within the specified bounds indicated. The two reconciled temperatures outside the bounds are the ones in which the presence of a gross error has been confirmed. Since this is a preliminary study, the solution proposed must be taken as a recommendation.

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