Potentialities and limitations of machine learning to solve cut-and-shuffle mixing problems: A case study

Thomas F. Lynn, Julio M. Ottino, Richard M. Lueptow, Paul B. Umbanhowar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well studied, and a simple mixing methodology, which we refer to as the longest segment (LS) method, works well under a broad range of situations. We use supervised learning to train a neural network (NN) to emulate the LS mixing algorithm for mixing a one-dimensional domain of two species. We find that a generic deep NN can emulate the LS method with good accuracy but cannot generalize to conditions significantly outside its training repertoire. The challenges in defining the mixing problem and generalizing a ML mixing approach are indicative of those expected for more complex systems where optimal or near optimal mixing methods remain unknown.

Original languageEnglish (US)
Article number117840
JournalChemical Engineering Science
Volume260
DOIs
StatePublished - Oct 12 2022

Keywords

  • Artificial intelligence
  • Cutting-and-shuffling
  • Granular materials
  • Interval exchange transforms
  • Machine learning
  • Mixing

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering

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