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.
This paper describes a collaborative digital twin approach for equipment dimensioning and selection in industrial process plants. Dynamic process simulator (Apros) was used to model the process and its automation, including pumps, while a product specific dynamic simulator (Virtual Drive) was used to model the motor and frequency converter. This approach allows all stakeholders to design and dimension the process equipment together in a holistic and energy optimal way. Simulation can be used to reach an optimal equipment solution that prevents overdimensioning, leading to up-front and total life-cycle cost savings.Co-simulation was made possible by implementing a prototype Functional Mock-up Interface (FMI) for both Apros 6 and Virtual Drive, allowing Apros to import Virtual Drive as a Functional Mock-up Unit (FMU). This paper shows how the FMI solution can be used for finding energy optimal selections for pumps and related powertrain products.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.