B REU College of Engineering Presentations in Science Prof. Amber Gee, Department of Communication.
Research Communication in Engineering Science & Technology
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Research Communication in Engineering Science & Technology 1 (2018) 29 Copyright © 2018 Asian Scientific Research TM by Galaxy Tech Solutions. All rights reserved. 29 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 Ern 1, *, Prakash Kumar Karunakaran 2 , Marappa Gounder Ramasamy 1 1 Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Perak, Malaysia. 2 Group 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 Model Predictive Control (MPC) requires a reasonably accurate dynamic model of the process being controlled which is realized through model identification. The increasing amount of modelling algorithms by various MPC vendors result with different performances in model identification. Therefore, this research was conducted to compare the effectiveness of the system identification algorithms under AIDAPro of Yokogawa, DMCPlus of Aspentech and System Identification Toolbox of MATLAB in the development of eight dynamic models of a de-ethanizer column. The research involved the development of the assessment criteria for model performance, data collection from step test, data cleaning and pre- processing, model identification using the mentioned identification technologies, and comparison of model performance to recommend the most effective technology. Under these identification technologies, Finite Impulse Response (FIR) and State-space model structures with their corresponding identification algorithms – Ordinary Least Squares (OLS) and Numerical Subspace State-space System Identification (N4SID) were used for identifying the models for the de-ethanizer column. The controlled variables selected are top and bottom product qualities; manipulated variables are reflux flowrate set point and temperature control set point; and the disturbance variables are the two feed flowrates of the column. The effectiveness of the process modelling technology was evaluated based on Normalized Root Mean Squared Error (NRMSE) of the model predictions using MATLAB. Simulation results of the model identified illustrated that the performance of MATLAB is superior in FIR model identification while DMCPlus performs the best in state-space model identification with both achieving above 70% model accuracy. In addition to the NRMSE results, simulation results were compared based on four qualitative criteria identified and the final recommendations for the suitability of the algorithms in the industry were established. * Corresponding author at Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Perak, Malaysia. Email addresses: [email protected] (Grace Ngu Sook Ern)
Transcript of Research Communication in Engineering Science & Technology
Research Communication in Engineering Science & Technology 1 (2018) 29
Copyright © 2018 Asian Scientific ResearchTM by Galaxy Tech Solutions. All rights reserved. 29
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: [email protected] (Grace Ngu Sook Ern)
