Abstract
Data reconciliation (DR) and gross error detection are two common tools used in industry to provide accurate and reliable data, which is useful to analyse plant performance and basis for troubleshooting. DR techniques improve the accuracy of measurements by using redundancies in material and energy balances. This provides reliable information that could help decision making regarding plant operation, which potentially leads to financial benefit. This paper presents the utilization of plant data to perform troubleshooting of ammonia reactor, in particular the profile of catalyst activity. Bad plant data are collected and then analysed using DR to produces reconciled data, which could be used to detect and identify the gross error measurements. The input data for DR and gross error detection were gathered from Aspen HYSYS V8.8 simulations by modelling the single-bed ammonia reactor. The result presents that bad plant data could define actual system condition such as gross error measurements in normal condition or catalyst activity problem. Both conditions are modelled by DR to indicate actual system condition using statistical analysis and to perform troubleshooting. Appropriate troubleshooting could save time and provide financial benefits by avoiding wrong accusation of system problem, specifically in ammonia reactor evaluated in this paper.
Highlights
The reliability of plant data could determine the effectiveness of plant performance improvement strategies
The other assumption is there is no channeling in ammonia reactor system, as the focuses of this paper are gross error measurements and the decreasing of catalyst activity
This study prove that data reconciliation could provide right troubleshooting based on bad plant data, this study shows that data reconciliation is true for strategies that rely on first-principles models of the chemical plant, such as model predictive control [12] or real-time optimization [13] and combining with simulation to define faster solution of troubleshooting could save time and financial benefit
Summary
The reliability of plant data could determine the effectiveness of plant performance improvement strategies. Good measurement usually provides data redundancy to guarantee data availability This redundancy could be used to determine the suitable mathematical model and improve the accuracy of plant data by reducing the effect of random errors. Data reconciliation adjusts process measurements with random errors by having them satisfy material and energy balance constraints and is a way to improve the quality of the measurements taken from a process via DCS or any other means of data collection. [6] In ammonia plant, plant data is usually provided in logbook and noted by operator, and it is recorded in DCS system These plant data could be used to improve the plant performance or to solve plant problems. Ammonia reactor could be simplified as single bed reactor to ease the analysis or data reconciliation
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