Multivariate Analysis of Industrial Scale Fermentation Data

Lisa Mears, Rasmus Nørregård, Stuart M. Stocks, Mads O. Albæk, Gürkan Sin, Krist Gernaey, Kris Villez

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations of multivariate modelling were carried out using different data pre-processing and scaling methods in order to extract the trends from the industrial data set, obtained from a production process operating in Novozymes A/S. This data set poses challenges for data analysis, combining both online and offline variables, different data sampling intervals, and noise in the measurements, as well as different batch lengths. By applying unfold principal component regression (UPCR) and unfold partial least squares (UPLS) regression algorithms, the product concentration could be predicted for 30 production batches, with an average prediction error of 7.6%. A methodology is proposed for applying multivariate analysis to industrial scale batch process data.
Original languageEnglish
Title of host publicationProceedings of the 25th European Symposium on Computer Aided Process Engineering
EditorsKrist V. Gernaey, Jakob K. Huusom, Rafiqul Gani
Publication date2015
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
Internet address
SeriesComputer Aided Chemical Engineering


  • Multivariate Data Analysis
  • Bioprocess
  • Process Optimisation


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