Data reconciliation with application to a natural gas processing plant Ahmad Rafiee a, * , Flor Behrouzshad b a Sharif Engineering Process Development Company, Tehran, Iran b South Pars Gas Company (SPGC), Asalouyeh, Iran article info Article history: Received 14 December 2015 Received in revised form 19 March 2016 Accepted 22 March 2016 Available online 25 March 2016 Keywords: Mass and energy balance Data reconciliation Natural gas processing plant Gross error detection abstract Data reconciliation is a mathematical technique that uses process information to fulfill material and energy conservation laws. This technique adjusts random errors in the measured data in weighted least squares to satisfy mass and energy balance constraints. In this paper, data reconciliation is applied to a natural gas processing plant. Based on the measured data, the mass flow of output streams is greater than the mass flow of the raw feed to the plant which leads to an imbalance of about 7%. The global test is employed to check the presence of systematic errors (gross errors) in the measured data. The results indicate that there is no gross error in the measurements. © 2016 Elsevier B.V. All rights reserved. 1. Introduction Chemical plants consist of different unit operations (nodes) such as reactors, separators, storage tanks, pumps, compressors, and so forth. The nodes are connected among themselves and with the environment by streams. Measurements of flows, temperatures, pressures, concentrations, etc. at different points of the nodes and streams are taken to control and evaluate process performance. In some cases not all process variables are measured due to cost or technical considerations. A well known problem is that measuring devices in the same location show different values and the mass and energy balances are not fulfilled over the nodes. We need to know the most accurate values (reconciled values) of measured variables and if possible, estimate the unmeasured ones to satisfy the balance equations. Data reconciliation is not a new idea and has been applied to different processes. Sarabia et al. (2012) dealt with the data reconciliation technique on a petrol refinery and the optimal management of hydrogen networks was presented. Islam et al. (1994) developed a reconciliation package for an industrial pyrol- ysis reactor with nonlinear mass and energy balance equations. There are 11 equations and 36 variables. The results of data reconciliation indicate that a gross error is present in the mea- surements. Pierucci et al. (1996) performed online data reconciliation and optimization of a large scale olefin plant. Placido and Loureiro (1998) reconciled the measurements for different units of Fafen ammonia plant in Brazil with 55 mass balance equations, 25 unmeasured and 51 measured variables. The results show that gross errors are present in the measurements. Lida and Skogestad (2008) applied the data reconciliation method on a catalytic naphtha reformer. Their model has 501 variables and 442 equations. Meyer et al. (1993) used the data reconciliation method to treat raw data of an industrial food plant. The process consists of 14 unit operations, 12 components and 34 streams. Lida and Skogestad (2001) developed a real time optimization system of heat exchange network in Statoil Mongstad refinery. There are 85 streams, 20 heat exchanger and totally 210 variables. The results show that several flow measurements have poor performance. Nonlinear steady state data reconciliation approach was applied on a gold processing plant by Lima (2006). Bazin et al. (1998) applied the data reconciliation to a rotary dryer. The results show that there are gross errors in the measurements. Knopf (2012) reconciled the measured data of a gas turbine cogeneration system for electricity and steam generation. There are 23 variables and 8 mass and en- ergy balances. The problem is solved by Excel and the results show that there is no gross error in the system. Bazin et al. (2005) applied the data reconciliation method on the measurements of a copper solvent extraction. There are 2 equations and 30 variables. The problem is solved by Excel. Ijaz et al. (2013) simulated the heat exchange network and reconciled the measured data. The network consists of 9 exchangers and the number of measured and un- measured variables is 22 and 10, respectively. Jiang et al. (2014) * Corresponding author. E-mail address: a.rafiee82@gmail.com (A. Rafiee). Contents lists available at ScienceDirect Journal of Natural Gas Science and Engineering journal homepage: www.elsevier.com/locate/jngse http://dx.doi.org/10.1016/j.jngse.2016.03.071 1875-5100/© 2016 Elsevier B.V. All rights reserved. Journal of Natural Gas Science and Engineering 31 (2016) 538e545