Beginning at the factory level.

Problem description

Bio-based products like enzymes are typically produced using a sequence of semicontinuous unit operations, generally starting with fermentation, and subsequent purification and formulations steps.

Often products are manufactured in a multi-process, multi-product plants, with complex routing of the physical flow and real-life constraints used by experts to make decisions on optimal scheduling options. These systems are complex in several dimensions which reduces the chances of operating at the optimum schedule for a given product mix. Furthermore, real-production systems in multi-purpose facilities suffers from inherent variability, which impact process steps durations and outputs. Within our DSM production sites we have chosen one of our key multi-product facilities as case study.

Goal

Deliver solutions in three dimensions: 

1. Digital Model
  • How can we understand variability and quantities of actual processes?
  • What is the right optimization algorithm to create optimize scheduled for a biotech multi-process, multi-product, factory?
2. Machine learning for pattern recognition for scheduling & planning
  • How to represent schedules such that they are suitable for machine learning?
  • How to teach the system with historical solutions?
3. Machine learning based  capacity model
  • How to use ML to predict and understand real capacity utilization, accounting for variability?
  • How type of algorithms could be used to optimize the capacity utilization?
  • How to deliver a predictive capacity model?

Project team

Kim van den Houten
Kim van den Houten
TU Delft
Mathijs de Weerdt
Mathijs de Weerdt
TU Delft
David Tax
David Tax
TU Delft
Esteban Freydell
Esteban Freydell
DSM

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