Marin, Guy

Pyl, Steven

Reyniers, Marie-Françoise

Van Geem, Kevin

English

Ghent University Academic Bibliography

TrainingThe Hidden NodesANN for Naphtha ReconstructionMahalanobis Distance dMPrincipal Component Analysis of the Training DataValidationApplication RangeArtificial Neural NetworkFeedstock ReconstructionFeedstockProperties7th International Workshop on Mathematics in Chemical Kinetics and Engineering 2011, May 18th - 20th 2011, University of Heidelbergwww.lct.ugent.be steven.pyl@ugent.beDetailed Feedstock CompositionDerive the detailed composition of a complex mixturefrom a limited set of macroscopic characteristicsAverage Molecular WeightHydrogen/Carbon ratioSpecific DensityGlobal PIONA analysisGoalThe Need for Detail...Global SARA analysisGroup-type GC-MS analysisSulfur contentNitrogen content1H and/or 13C NMR dataD86 Distillation dataD2887 SimDist dataEtc.FastRobustStandardAcknowledgementThe Long Term Structural Methusalem Funding by theFlemish Government – grant number BOF09/01M00409Modelling a complex non-linear relalationship between multiple input and output variablesThe application range of the Artificial Neural Network isdetermined by the training data9 feedstock properties 3 principal componentsà Accounting for approximately 80% of the total varianceà Graphical representation of the training data in the 3D principal component spaceUnknown naphthasAnalytical weight fraction [wt%]0 2 4 6 8 10 12 14 16Reconstructed weight fraction [wt%]0246810121416ANN(a)Analytical weight fraction [wt%]0 2 4 6 8 10 12 14 16Reconstructed weight fraction [wt%]0246810121416(b)Input9 Feedstock PropertiesÈGlobal PIONA,Density,ASTM D86 IBP, 50%BP, and FBPOutputDetailed PIONAC3 – C13ÈWeightfractions of 28 components ω1,1ANN = nodes + weighted connectionsHow to determine the weights and activation values? à A lot of training data = examples of the relationship to be modeledGhent University – Department of Chemical EngineeringLaboratory for Chemical Technology, Technologiepark 918, Ghent, BelgiumAnalytical weight frac ion [wt%]0 2 4 6 8 10 12 14 16Reconstructed weight fraction [wt%]0246810121416ANN(a)Analytical weight frac ion [wt%]0 2 4 6 8 10 12 14 16Reconstructed weight fraction [wt%]0246810121416(b)Naphtha A – in range Naphtha B – out of rangeNaphtha A – in rangedM = 0.9Naphtha B – out of rangedM = 3.2Comparison between reconstructed composition and detailed analytical composition, determined using gas chromatographyA priori classification of the naphtha based on its principal component representationà dM is a measure for the distance from the data set center, taking into account the variance of the data in each directionà dM follows a Hotelling T²-distributionà critical value defines an ellipsoidal subspaceü Restricts the node output to a value between 0 and 1ü Introduction of the non-linear modeling propertiesInput weighted sum of input signals jjjiiin SS ,,)(,,11iiinSiouteSOuput transformed signal à Sigmoid funtion activationvaluei jjiji yyTE2exp,rec, )(~5.2)1(~ critM,,,2,,M dFpnnpTd pnpnpzCovz1M )(TdNaphtha training setà Contains the macroscopic properties and detailed composition of approximately 300 naphthas with a wide range of propertiesPyl, et al. AIChE J. 2010VPZ  ~n×9n×3 9×3S.P. Pyl, K.M. Van Geem, M.-F. Reyniers, G.B. MarinBack-propagation algorithmà Gradient based optimization of weights and activation valuesà Minimizing the training errornin,1nin,2nh,1nh,Nhnout,2nout,3input layernout,1nout,Ncnin,Np.........p1p2pNp...y1rec...hidden layer output layery2recy3recyNcrecFundamental Process Modeling = Molecule-based Modelingà Accurate feedstock composition is crucial!DetailedFeedstockCompositionDetailedProductCompositionMicrokinetic Model Reactor ModelThese models are indispensible for state-of-the-artprocess design, optimization and controlFundamentalModelUsing Artificial Neural Networks to Reconstructthe Composition of Complex Feedstocks

Using artificial neural networks to reconstruct the composition of complex feedstocks

https://biblio.ugent.be/publication/1964451/file/1964453

Using artificial neural networks to reconstruct the composition of complex feedstocks

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