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Harvest time prediction for batch processes
DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark. DTU Compute, Asmussens Alle 322, 2800 Kgs. Lyngby, Denmark.
Chr. Hansen A/S, Hvidovre.
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi. DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark.ORCID-id: 0000-0003-4222-9631
2018 (engelsk)Inngår i: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 117, s. 32-41Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Batch processes usually exhibit variation in the time at which individual batches are stopped (referred to as the harvest time). Harvest time is based on the occurrence of some criterion and there may be great uncertainty as to when this criterion will be satisfied. This uncertainty increases the difficulty of scheduling downstream operations and results in fewer completed batches per day. A real case study is presented of a bacteria fermentation process. We consider the problem of predicting the harvest time of a batch in advance to reduce variation and improving batch quality. Lasso regression is used to obtain an interpretable model for predicting the harvest time at an early stage in the batch. A novel method for updating the harvest time predictions as a batch progresses is presented, based on information obtained from online alignment using dynamic time warping.

sted, utgiver, år, opplag, sider
Elsevier, 2018. Vol. 117, s. 32-41
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URN: urn:nbn:se:ltu:diva-68892DOI: 10.1016/j.compchemeng.2018.05.019ISI: 000441891600004Scopus ID: 2-s2.0-85048458709OAI: oai:DiVA.org:ltu-68892DiVA, id: diva2:1209875
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Validerad;2018;Nivå 2;2018-06-25 (andbra)

Tilgjengelig fra: 2018-05-24 Laget: 2018-05-24 Sist oppdatert: 2019-03-27bibliografisk kontrollert

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