Forecasting Operational Conditions: A case-study from dewatering of biomass at an industrial wastewater treatment plant

Sebastian Olivier Nymann Topalian, Pedram Ramin, Kasper Kjellberg, Murat Kulahci, Xavier Flores Alsina, Damien J. Batstone, Krist V. Gernaey*

*Corresponding author for this work

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

Abstract

In this paper, we present a data-driven approach to predicting polymer dosages for industrial decanters based on upstream production data. First, a data extraction algorithm using on-line sensors is developed to identify when the operational mode is changed with a 99 % accuracy. Next, an investigation of process delays in the collected data is carried out by analysing partial autocorrelation matrix eigenvalues upon which is it concluded to transform the data by summarising the data by batch and including lagged summaries to account for a time delay of 2 hours. Finally, a random forest forecasting model is trained capable of learning structured information from the lagged summaries producing decent predictions for both low and high polymer dosages (RMSE 14.89). The proposed approach could potentially save operators 3-6 hours a day.
Original languageEnglish
Title of host publicationProceedings of the 14th International Symposium on Process Systems Engineering
EditorsYoshiyuki Yamashita, Manabu Kano
Place of PublicationAmsterdam
PublisherElsevier
Publication date2022
Pages2077-2082
ISBN (Electronic)978-0-443-18726-1, 978-0-323-85159-6
DOIs
Publication statusPublished - 2022
Event14th International Symposium on Process Systems Engineering (PSE 2021+) - Kyoto, Japan
Duration: 19 Jun 202223 Jun 2022

Conference

Conference14th International Symposium on Process Systems Engineering (PSE 2021+)
Country/TerritoryJapan
CityKyoto
Period19/06/202223/06/2022
SeriesComputer Aided Chemical Engineering
Volume49
ISSN1570-7946

Keywords

  • Control
  • Operation
  • Forecasting
  • Environmental Systems

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