Data reconciliation and gross error detection: application in chemical processes

Mohammad Hosein EGHBAL AHMADİ
1.600 911

Abstract


Abstract. Measured data are normally corrupted by different kinds of errors in many chemical processes. In this work, a brief overview in data reconciliation and gross error detection believed as the most efficient technique in reducing the measurement errors and obtaining accurate information about the process is presented. In addition to defining the basic problem and a survey of recent developments in this area that is categorized in “Real Time Optimization” field, we will describe about advanced optimization methods in nonlinear cases. At the end, implementation of data reconciliation is illustrated on a challenging process of Claus as a case study and as a result, a modified and consistent model with regard to measured data is presented by simultaneous estimation of key model parameters. In our case study, automation capability of ASPEN HYSYS is used to provide interface environment to reach global optimum.


Keywords


Data Reconciliation, Gross Error Detection, Optimization, Modeling, Claus Process

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−E RT C CS−2 exp(E RT C2( )n k5=318 m mol s1)) E5=55703.8 ( . J mol1)) k6 =18 10 ( .3 × reaction10 r E6 =17988 ( . J mol1)) −1)) −1)) 2S 7 k7 =53 10 ( .3 × m mol s1−1 E7=160630 ( . J mole1)) − ) −1))

After reconciliation Date 2 0.00186 0.00365 0.00021 0.000576 53 65 26 1124 1219 101 140 1124 48 54 25 1124 1124 Te Stream 4 at ur e/ C Parameters name k1 458 k4 2280955772 k7 55853000 k2 0.888 k6 2201800 3 n 02 CONCLUSIONS

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