| spelling |
oapen-20.500.12657-493812021-11-23T14:04:13Z Chapter Operationalizing Heterogeneous Data-Driven Process Models for Various Industrial Sectors through Microservice-Oriented Cloud-Based Architecture Valdemar, Lipenko Sebastian, Nigl Andreas, Roither-Voigt Zelenay, David industrial optimization, model predictive control integration, machine learning model integration, Bayesian network integration, enterprise resource planning (ERP) forecast model integration, prediction model integration, model calculation graph, microservice-oriented architecture, cloud computing bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBC Engineering: general Industrial performance optimization increasingly makes the use of various analytical data-driven models. In this context, modern machine learning capabilities to predict future production quality outcomes, model predictive control to better account for complex multivariable environments of process industry, Bayesian Networks enabling improved decision support systems for diagnostics and fault detection are some of the main examples to be named. The key challenge is to integrate these highly heterogeneous models in a holistic system, which would also be suitable for applications from the most different industries. Core elements of the underlying solution architecture constitute highly decoupled model microservices, ensuring the creation of largely customizable model runtime environments. Deployment of isolated user-space instances, called containers, further extends the overall possibilities to integrate heterogeneous models. Strong requirements on high availability, scalability, and security are satisfied through the application of cloud-based services. Tieto successfully applied the outlined approach during the participation in FUture DIrections for Process industry Optimization (FUDIPO), a project funded by the European Commission under the H2020 program, SPIRE-02-2016. 2021-06-02T10:13:38Z 2021-06-02T10:13:38Z 2021 chapter ONIX_20210602_10.5772/intechopen.92896_495 https://library.oapen.org/handle/20.500.12657/49381 eng application/pdf n/a 72819.pdf InTechOpen 10.5772/intechopen.92896 10.5772/intechopen.92896 09f6769d-48ed-467d-b150-4cf2680656a1 H2020-SPIRE-2016 723523 open access
|
| description |
Industrial performance optimization increasingly makes the use of various analytical data-driven models. In this context, modern machine learning capabilities to predict future production quality outcomes, model predictive control to better account for complex multivariable environments of process industry, Bayesian Networks enabling improved decision support systems for diagnostics and fault detection are some of the main examples to be named. The key challenge is to integrate these highly heterogeneous models in a holistic system, which would also be suitable for applications from the most different industries. Core elements of the underlying solution architecture constitute highly decoupled model microservices, ensuring the creation of largely customizable model runtime environments. Deployment of isolated user-space instances, called containers, further extends the overall possibilities to integrate heterogeneous models. Strong requirements on high availability, scalability, and security are satisfied through the application of cloud-based services. Tieto successfully applied the outlined approach during the participation in FUture DIrections for Process industry Optimization (FUDIPO), a project funded by the European Commission under the H2020 program, SPIRE-02-2016.
|
