Abrahamsson, Victor

Franz, Kristina

Marin, Guy

Ristic, Nenad

Van Geem, Kevin

Ghent University Academic Bibliography

Laboratory for Chemical Technology, Ghent Universityhttp://www.lct.UGent.beAPPLICATION OF PARTIAL LEAST SQUARES REGRESSION FOR UNDERSTANDING AND PREDICTION OF FOULING IN THE TRANSFER LINE HEAT EXCHANGER OF A STEAM CRACKERNenad D. Ristic, Victor Abrahamsson, Kristina Franz, Kevin M. Van Geem, Guy B. Marin1Budapest, Hungary, 26th of May, 2017Outline• Introduction• Experimental study• Data structure  • Variable selection PLSR modelling • Conclusions 2Budapest, Hungary, 26th of May, 2017Steam cracking3Budapest, Hungary, 26th of May, 2017EthylenePropylene1,3-ButadieneBTXHeart of a petrochemical plantThe main source of ethylene, propylene and other valuable hydrocarbonsSteam crackerEthaneLPGNaphthaGas CondensateAtmospheric Gas OilFouling4Budapest, Hungary, 26th of May, 2017Fouling in the reactor coil and the TLE1. Thermal resistance 2. Pressure buildup 3. Decrease in selectivity towards olefins Parameters influencing fouling:• Operating conditions, i.e. T, P, δ• Material properties  • Feedstock compositionDREAMPrediction of fouling based on feedstock composition? Outline• Introduction• Experimental study• Data structure• Variable selection PLSR modelling • Conclusions 5Budapest, Hungary, 26th of May, 2017Pilot scale steam cracking experiments6Budapest, Hungary, 26th of May, 2017Pilot scale steam cracking • Duration - 6 hour• Identical process conditions • 7 gas condensate feedstocksCoke deposited in TLE is burned off (800 to 900 °C)after each experiment CO and CO2 concentrations are monitored (0.1 Hz) in the outlet stream (app. 1.5 hours) 	   			360    · 		 – Molecular Mass of carbon      - Molar concentration of CO – Volumetric flow rate                      - Molar concentration of CO2 – Pressure                                               – Temperature  – Gas ConstantCompositional Characterization7Budapest, Hungary, 26th of May, 20172431510 20 30 40 50 60 70 80 90time, mintime, sparaffins and naphthenicsMonoaromatics diaromaticsnaphtheno -diaromaticstriaromaticsInternal StandardNaphtheno -aromatics0VolatilityPolarityFIDLiquid CO2modulator1st dimension columnPONA, Restek, 50 m, 0.25 mm, 0.5 µmPTV Injector 2nd dimension columnBPX-50, SGE, 2 m, 0.15 mm, 0.15 µmHigh resolution separation of GC × GC enablesunrevealing the complex nature of petroleum streamsDue to the large number of detected molecules thenumber of samples (7) is unfavorable for exploratoryanalysisOutline• Introduction• Experimental study• Data structure• Variable selection PLSR modelling • Conclusions 8Budapest, Hungary, 26th of May, 2017Data structure9Budapest, Hungary, 26th of May, 2017Gas condensate 1 (0.01s) 2 (0.02s) … … N (96.6 min)1 X11X12… … X1N2 X21X22… … X2N3 X31X32… … X3N4 X41X42… … X4N5 X51X52… … X5N6 X61X62… … X6N7 X71X72… … X7NRAW X DATA2D           TR,1 × TR,2 × ν (Hz)TR,2×ν(Hz)TR,1Exploratory analysis requires data alignment solving retention time shift issues1D           TR,1 × ν (Hz)SamplesData alignment10Budapest, Hungary, 26th of May, 2017Tomasi, G., et al. (2011). "Icoshift: An effective tool for the alignment of chromatographic data." Journal of Chromatography A 1218(43): 7832-7840.Zhang, D., et al. (2008). "Two-dimensional correlation optimized warping algorithm for aligning GCxGC-MS data." Analytical Chemistry 80(8): 2664-2671.icoshiftIcoshift - applied on complete structure 1D data and subsequently on each 5 modulation segment2D COW2D COW 1D – segment of 20 data points with a allowable movement of 2 data points 2D – segment of 80 data points with a allowable movement of 30 data points  !  "  #  ! $ # %#&'(') *  + $ #  " $(#)./01%#&'('X #  +(2 ) *  3 )(*)(./01%*&'('445($6 78&98&2 : 78;<&98;< ,  78&98&2 : 78;<&98;< )>(2?Partitioned chromatogram Warped chromatogramOutline• Introduction• Experimental study• Data structure• Variable selection PLSR modelling• Conclusions 11Budapest, Hungary, 26th of May, 2017Partial least squares regression (PLSR)IJIK@APLSRPLS1 if K = 1PLS2 if K > 1BCD$  	CD  EFGHD  I	D  EJI2  K22I.  K..… … … …X=TP′Y=U′T=XWY=T′Y=XW′B =W′(regression  coefficients are not independent)Y=XBMaximize covariance of X and Y (NIPALS or SIMPLS algorithms)Direct multiple linear regression (MLR) cannot handle collinear and large rank matrixes Budapest, Hungary, 26th of May, 201712KDWold, S., M. Sjöström and L. Eriksson (2001). "PLS-regression: A basic tool of chemometrics." Chemometrics and Intelligent Laboratory Systems 58(2): 109-130.Variable selection - Methods that require initially valid PLSR model 13Budapest, Hungary, 26th of May, 2017Variable importance in projection (VIP)LMC? 	∑ O?0.P0 · QQ0 · RQQ11S7 · +TQQ0  U0.V0D V0 QQ11S7  U.BDBSelectivity ratio (SR)A  AWXY  ZXY  VXY[XYD  ZXYVXY  AUU [XY ADVXYVXYD VXY Q? QQ\ ]7S>\^,?QQ_\`^9S7,?QQ\ ]7S>\^,? 	 VXY[XY,?D . QQ_\`^9S7,?  	 ZXY,? .Regression Vectorwjf - weight value for variable j of component fSSYf - the sum of squares of y for the fth componentJ - the number of variables SSYtotal - the sum of squares of the total explained variance of yF - the total number of components, i.e. LVstTP – scores vectorpTP - loadings vector SS – sum of squares for each variable j Regression coefficients which are a single measure of association between each variableand the response can be utilized based on the assumption that variables with larger coefficient are more influential and therefore more importantThe regression vector, VIP and SR were acquired straight from the PLS_ToolboxMehmood, T., K. H. Liland, L. Snipen and S. Sæbø (2012). "A review of variable selection methods in Partial Least Squares Regression." Chemometrics and Intelligent Laboratory Systems 118: 62-69.Rajalahti, T., R. Arneberg, F. S. Berven, K. M. Myhr, R. J. Ulvik and O. M. Kvalheim (2009). "Biomarker discovery in mass spectral profiles by means of selectivity ratio plot." Chemometrics and Intelligent Laboratory Systems 95(1): 35-48Farrés, M., S. Platikanov, S. Tsakovski and R. Tauler (2015). "Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation." Journal of Chemometrics 29(10): 528-536.Variable selection – Screening methods14Budapest, Hungary, 26th of May, 2017RReliefF algorithmK? C^ 00	a	|	^00	P8	C^ 00	P8C^ 00	a −1 − C^ 00	a	|	^00	P8	 C^ 00	P81 − C^ 00	aF-test statistic or Fisher ratio +  	Q_\e_\``>	Q_\`^9S7` MS - mean squares calculated for each variable Pdiff C - the probability that the two nearest variables have different predictionsPdiff F - the probability that the two nearest variables have different values for the independent variablePdiff C | diff F -the probability that the two nearest variables have both different values for the independent and dependent variableMATLAB Statistics and Machine Learning ToolboxPatchava, K. C., M. Benaissa and H. Behairy (2015). Improving the prediction performance of PLSR using RReliefF and FSD for the quantitative analysis of glucose in Near Infrared spectra. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS.Model validationResults Preprocessing PLSR - RMSECV (g deposit/6 h)Method X-block Y-block 1LV 2LVFull log10 MC log10 MC 0.95 0.91Regression vector log10 MC log10 MC 0.35 0.44VIP log10 MC log10 MC 0.38 0.43SR log10 MC log10 MC 0.47 0.45RReliefF log10 MC log10 MC 0.42 0.62Fisher ratio log10 MC log10 MC 0.38 0.5115Budapest, Hungary, 26th of May, 20171. Both X and Y data blocks are Mean Centered (MC)2. Logarithmical transformation of both blocks3. Evaluation via calculation of RMSECV established using leave-one out methodology 4. With increasing the number of LV the threat of overfitting increases 5. Models are build by iterative increase of the relevance cut-off value, with a constrain that the model must not have less than 10 000 pixels MC – mean centered               log10 – logarithm base 10             Fisher ratio16Budapest, Hungary, 26th of May, 2017Pixels corresponding to aromatics and naphthenics with long aliphatic side chains and high boiling point paraffins serve as the best TLE fouling predictors • FR model× Full model1 LVFR modelFull PLSR modelVariable importance in projection 17Budapest, Hungary, 26th of May, 2017Confirms that aromatics and naphthenics with long aliphatic side chains along with poly-aromatics and high boiling point paraffins serve as good TLE fouling predictors• VIP model× Full model1 LVFull PLSR modelVIP modelRegression vector18Budapest, Hungary, 26th of May, 2017• RC model× Full model1 LVSame pixels, corresponding to aromatics and naphthenics with long aliphatic side chains, poly-aromatics and to some extent to high boiling point paraffins serve as good TLE fouling predictorsThe method enabled establishing of the most accurate TLE fouling prediction model with an average relative error of 20 %Full PLSR modelRV modelOutline• Introduction• Experimental study• Data structure and PCA  • Variable selection PLSR modelling • Conclusions 19Budapest, Hungary, 26th of May, 2017Conclusions• Pixel-based PLSR models can be applied within the calibration sample range• Identical analytical method must be used for characterization of each sample, slight shifts in retention can be aligned• Variable selection techniques are able to detect the chromatographic regions, i.e. chemical compounds, with the strongest correlation with the studied phenomenon • Monoaromatics and naphthenics with a high boiling point are the best TLE fouling predictors• Composition of the feeds is successfully correlated with the TLE fouling, providing a predictive model with an average relative error of 20 %20Budapest, Hungary, 26th of May, 2017Acknowledgments21Budapest, Hungary, 26th of May, 2017Long Term Structural Methusalem Funding by the Flemish GovernmentQ & A22Budapest, Hungary, 26th of May, 2017Thank you foryour attention!GlossaryBTX – Benzene, Toluene, XyleneLPG – Liquefied petroleum gas  T - Temperature P - Pressure δ – Dilution TLE – Transfer Line Heat ExchangerGC × GC - comprehensive two-dimensional gas chromatographyPCA – Principle Component AnalysisPC – Principle componentPLSR - Partial Least Squares Regression VIP - Variable importance in projectionSR - Selectivity ratio  RMSECV - Root Mean Square Error of Cross ValidationLV - latent variableMC - Mean CenteredFR - Fisher RatioRC - Regression CoefficientsPTV - Programmed Temperature Vaporising injectorFID - Flame Ionization Detector23Budapest, Hungary, 26th of May, 2017

Application of partial least squares regression for understanding and prediction of fouling in the transfer line heat exchanger of a steam cracker

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Application of partial least squares regression for understanding and prediction of fouling in the transfer line heat exchanger of a steam cracker

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