Research Communication in Engineering Science & Technology 1 (2018) 29

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Asian Scientific Research

Research Communication in Engineering Science & Technology

Journal homepage: http://www.asianscientificresearch.com/journals/RCEST

Special issue: Regional Chemical Engineering Undergraduate Congress (RCEUC)

Model Identification for De-ethanizer Column Grace Ngu Sook Ern1,*, Prakash Kumar Karunakaran2, Marappa Gounder Ramasamy1 1Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Perak, Malaysia. 2Group Technical Solutions, PETRONAS, 50050 Kuala Lumpur, Malaysia.

ARTICLE INFO ABSTRACT

Received: 30 August 2018 Received in revised form: 30 August 2018 Available Online: 15 September 2018 Keywords: Model identification Distillation column Identification packages FIR State-space OLS N4SID Model performance

ModelPredictiveControl(MPC)requiresareasonablyaccuratedynamicmodel of the process being controlledwhich is realized throughmodelidentification.TheincreasingamountofmodellingalgorithmsbyvariousMPCvendorsresultwithdifferentperformances inmodel identification.Therefore, this researchwas conducted to compare the effectiveness ofthe system identification algorithms under AIDAPro of Yokogawa,DMCPlusofAspentechandSystem IdentificationToolboxofMATLAB inthedevelopmentofeightdynamicmodelsofade-ethanizercolumn.Theresearch involved thedevelopmentof theassessmentcriteria formodelperformance, data collection from step test, data cleaning and pre-processing, model identification using the mentioned identificationtechnologies, and comparison ofmodel performance to recommend themosteffectivetechnology.Undertheseidentificationtechnologies,FiniteImpulse Response (FIR) and State-space model structures with theircorresponding identification algorithms –Ordinary Least Squares (OLS)andNumericalSubspaceState-spaceSystemIdentification(N4SID)wereused for identifying the models for the de-ethanizer column. Thecontrolled variables selected are top and bottom product qualities;manipulated variables are reflux flowrate set point and temperaturecontrol set point; and the disturbance variables are the two feedflowrates of the column. The effectiveness of the process modellingtechnologywasevaluatedbasedonNormalizedRootMeanSquaredError(NRMSE)of themodelpredictionsusingMATLAB. Simulationresultsofthe model identified illustrated that the performance of MATLAB issuperiorinFIRmodelidentificationwhileDMCPlusperformsthebestinstate-spacemodel identification with both achieving above 70%modelaccuracy. In addition to the NRMSE results, simulation results werecompared based on four qualitative criteria identified and the finalrecommendations for the suitability of the algorithms in the industrywereestablished.

* Corresponding author at Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Perak, Malaysia. Email addresses: gracengu2@gmail.com (Grace Ngu Sook Ern)