Using a digital twin as the objective function for evolutionary algorithm applications in large scale industrial processes

Miro Eklund, Seppo Sierla, Hannu Niemistö, Timo Korvola, Jouni Savolainen, Tommi Karhela

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

In this paper, we describe how the up-to-date state of a digital twin, and its corresponding simulation model, can be used as a fitness function of an evolutionary algorithm for optimizing a large-scale industrial process. An ICT architecture is presented for solving the computational challenges that arise when the fitness function evaluation takes considerable amount of time. Parallel computation of the fitness function in a cloud computing environment is proposed and the evolutionary algorithm is connected to the computational environment using the Function-as-a-Service approach. A case-study was conducted on the district heating network of Espoo, the second largest city in Finland. The study shows that the architecture is suited for optimizing the operating costs of the large district heating network, with over 800 km of water pipes and over 14 heat producers, reaching a cost-saving of an average of 2%, and up-to 4%, over the current industrial state-of-the-art method in use at the city of Espoo.
Original languageEnglish
Pages (from-to)24185-24202
Number of pages18
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Optimization
  • Digital twins
  • Evolutionary computation
  • Water heating
  • Heating systems
  • Costs
  • Computational modeling
  • Cloud Computing
  • Simulation
  • Digital Twin
  • Evolutionary Computation
  • Cloud computing
  • simulation
  • digital twin
  • evolutionary computation
  • optimization

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