Hybrid machine learning assisted modelling framework for particle processes

Rasmus Fjordbak Nielsen, Nima Nazemzadeh, Laura Wind Sillesen, Martin Peter Andersson, Krist V. Gernaey, Seyed Soheil Mansouri*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Particle processes are used broadly in industry and are frequently used for removal of insolubles, product isolation, purification and polishing. These processes are challenging to control due to their complex dynamics and physical-chemical properties. With the developments in particle monitoring tools make it possible to gain real-time insights into some of these process dynamics. In this work, a systematic modelling framework is proposed for particle processes based on a hybrid modelling concept, which integrates first-principles with machine-learning approaches. Here, we utilize on-line/at-line sensor data to train a machine learning based soft-sensor that predicts particle phenomena kinetics by combining it with a mechanistic population balance model. This approach allows flexibility towards use of process sensors and the model predictions do not violate physical constraints. Application of the framework is demonstrated through a laboratory-scale lactose crystallization, a laboratory-scale flocculation, and an industrial-scale pharmaceutical crystallization, using only limited prior process knowledge.
Original languageEnglish
Article number106916
JournalComputers & Chemical Engineering
Number of pages19
Publication statusPublished - 2020


  • Hybrid modelling
  • Modelling framework
  • Machine learning based soft-sensor
  • Real-time training

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