Multivariate analysis of industrial scale fermentation data

Lisa Mears, Rasmus Nørregård, Stuart Stocks, Gürkan Sin, Krist Gernaey, Kris Villez

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Batch production processes pose specific challenges for process monitoring and control. This isdue tomany reasons includingnon-linear behaviour, and arelativelypoor understanding of thesystem dynamics[1].It is therefore challenging for theprocess engineer to optimise the operationconditions, due to a lack of available process models, and complex interactions between variableswhich are not easy to define, especiallyacross scales and equipment.There is however a vastamount of batch process datagenerated, which canbe investigated with the aim of identifyingdesirable process operating conditions, and thereforeareas offocus for optimising the processoperation.This requires multivariate methods which canutilise the complexdatasetswhich areroutinely collected, containing online measured variables and offline sample data.Fermentation processes are highly sensitive to operational changes, as well as between batchvariations, and are therefore an interesting application of multivariate methods. The processdynamics are governed by the combination of process variables, and cannot be fully characterisedby individual variables alone[2]. There is also a lack of sensors for key variables which areconsidered todefine the operation[3], which makestraditional modelling a challenge.Although multivariate techniques are routinely used for chemometric applications, theirapplication to batch processes islesscommon due to the additional challenges associated withuneven batch lengths and less reproducible data, which has naturally greater variability, as well ashigh measurement noise. This requires additional preprocessing stagesin order to extract theinformation within such a dataset.A 30 batch dataset from a production process operating at Novozymes A/Sis analysed bymultivariate analysis with the aim of predicting the final product concentration, which is measuredoffline at the end of each batch. By creating a model for product concentration, it is possible toanalysethe model results and interpret this to guideprocess optimisation effortstowardsachieving a greaterproduct concentration.
Original languageEnglish
Publication date2015
Number of pages1
Publication statusPublished - 2015
Event25th European Symposium on Computer Aided Process Engineering : 12th International Symposium on Process Systems Engineering - Copenhagen, Denmark
Duration: 31 May 20154 Jun 2015


Conference25th European Symposium on Computer Aided Process Engineering
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