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

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openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023
Major/Subject
Mcode
Degree programme
Language
en
Pages
18
Series
IEEE Access, Volume 11
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.
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Publisher Copyright: Author
Keywords
Cloud Computing, Computational modeling, Costs, Digital Twin, Digital twins, Evolutionary computation, Evolutionary Computation, Heating systems, Optimization, Simulation, Water heating
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Citation
Eklund , M , Sierla , S , Niemisto , H , Korvola , T , Savolainen , J & Karhela , T 2023 , ' Using a digital twin as the objective function for evolutionary algorithm applications in large scale industrial processes ' , IEEE Access , vol. 11 , pp. 24185-24202 . https://doi.org/10.1109/ACCESS.2023.3254896