DATA RECONCILIATION AND GROSS ERROR DETECTION FOR OLEFIN REACTOR PARAMETERS BY USING THE LEAST SQUARE, CAUCHY AND M-ESTIMATOR OBJECTIVE FUNCTIONS

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ASGHARPOUR B.; KARIMZADEH RAMIN; OMIDKHAH NASRIN M.R.

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Journal: Nashrieh Shimi va Mohandesi Shimi Iran Year:2008 | Volume:27 | Issue:3 Page(s): 41-60

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Title

DATA RECONCILIATION AND GROSS ERROR DETECTION FOR OLEFIN REACTOR PARAMETERS BY USING THE LEAST SQUARE, CAUCHY AND M-ESTIMATOR OBJECTIVE FUNCTIONS

Author(s)

ASGHARPOUR B. | KARIMZADEH RAMIN | OMIDKHAH NASRIN M.R. | Issue Writer Certificate

Keywords

DATA RECONCILIATIONQ3

GROSS ERRORQ3

THERMAL CRACKINGQ3

ETHYLENEQ2

LEAST SQUARESQ3

CAUCHYQ3

M-ESTIMATORQ3

Abstract

Occurrence of errors is measured parameters of an industrial unit including steam cracking furnaces due to operator and/or equipment fault is inevitable. In this research a method for DATA RECONCILIATION of measured parameters in a steam cracking process is presented. Scientific-statistical DATA RECONCILIATION methods are used to detect errors in measured process variables as well as to eliminate or to reduce the errors. The errors in measured data of an industrial steam cracking furnace are estimated by solving three different DATA RECONCILIATION problems, and the results compared together. The problem contains an objective function (error function) with equality equation constraints, mass, heat, and momentum balances, and inequality variable constraints. Least square, CAUCHY and M-ESTIMATOR objective functions are used to in the three DATA RECONCILIATION problems respectively. The used radical mechanism in the balance equations contains 400 radical reactions with 56 and 19 molecular and radical species respectively. The accuracy of the mass, energy, and momentum balance an equation was checked by comparing the model simulated results with the industrial reactor design. There is good conformity between the results. The search simplex method and the Rung-Kutta method with the LU decomposition were used to solve the DATA RECONCILIATION problem, and the differential balance equations used as the constraints. The results show that the used DATA RECONCILIATION problems are good methods for detecting, reducing, and elimination of the errors. The results obtained by the different methods show that the M-ESTIMATOR method as the best method will properly detect the errors.

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APA: Copy

ASGHARPOUR, B., KARIMZADEH, RAMIN, & OMIDKHAH NASRIN, M.R.. (2008). DATA RECONCILIATION AND GROSS ERROR DETECTION FOR OLEFIN REACTOR PARAMETERS BY USING THE LEAST SQUARE, CAUCHY AND M-ESTIMATOR OBJECTIVE FUNCTIONS. NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN), 27(3), 41-60. SID. https://sid.ir/paper/26132/en

Vancouver: Copy

ASGHARPOUR B., KARIMZADEH RAMIN, OMIDKHAH NASRIN M.R.. DATA RECONCILIATION AND GROSS ERROR DETECTION FOR OLEFIN REACTOR PARAMETERS BY USING THE LEAST SQUARE, CAUCHY AND M-ESTIMATOR OBJECTIVE FUNCTIONS. NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN)[Internet]. 2008;27(3):41-60. Available from: https://sid.ir/paper/26132/en

IEEE: Copy

B. ASGHARPOUR, RAMIN KARIMZADEH, and M.R. OMIDKHAH NASRIN, “DATA RECONCILIATION AND GROSS ERROR DETECTION FOR OLEFIN REACTOR PARAMETERS BY USING THE LEAST SQUARE, CAUCHY AND M-ESTIMATOR OBJECTIVE FUNCTIONS,” NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN), vol. 27, no. 3, pp. 41–60, 2008, [Online]. Available: https://sid.ir/paper/26132/en

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