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The realm of digital twins is experiencing rapid growth and presents a wealth of opportunities for Industry 4.0. In conjunction with traditional simulation methods, digital twins offer a diverse range of possibilities. However, many existing tools in the domain of open‐source digital twins concentrate on specific use cases and do not provide a versatile framework. In contrast, the open‐source digital twin framework, OpenTwins, aims to provide a versatile framework that can be applied to a wide range of digital twin applications. In this article, we introduce a re‐definition of the original OpenTwins platform that enables the management of custom simulation services and the management of FMI simulation services, which is one of the most widely used simulation standards in the industry and its coexistence with machine learning models, which enables the definition of the next‐gen digital twins. Thanks to this integration, digital twins that reflect reality better can be developed, through hybrid models, where simulation data can feed the scarcity of machine learning data and so forth. As part of this project, a simulation model developed through the hydraulic software Epanet was validated in OpenTwins, in addition to an FMI simulation service. The hydraulic model was implemented and tested in an agricultural use case in collaboration with the University of Córdoba, Spain. A machine learning model has been developed to assess the behavior of an FMI simulation through machine learning. [ABSTRACT FROM AUTHOR]

Reducing the environmental impact of porcelain tile production while maintaining cost-effectiveness is challenging. This work introduced a novel modeling approach for optimizing a standard composition range comprising kaolinite (15-38 wt.%), illite (0-20 wt.%), quartz (20-40 wt.%), and feldspar (20-45 wt.%) to establish a robust composition interval for porcelain stoneware tiles. The proposed study considers several factors, such as composition impact on the manufacturing sequence, production costs, and CO2 emission.Aflowsheet simulation databasewas generated by coupling the Dyssol framework with MATLAB. This study investigated the influence of rawmaterial compositionwithin the process sequence, the total CO2 emissions, and production costs within the contexts of Spain and Brazil, two of the top five global producers. Granules with a higher proportion of talc and illite exhibit reduced moisture content after spray drying, and these combinations have lower green body porosity after compaction. The addition of talc allowed for decreased porosity content after compaction reduced firing temperature, and lowered costs and CO2 emissions despite the higher prices associated with talc. The proposed simulation methodology offers a powerful decision-making tool for optimizing raw material composition tominimize cost and CO2 emissions in the porcelain tile production. This methodology represents an early stride toward integrating digital twin methodologies within the ceramic tile sector, facilitating improved process regulation, and promoting the adoption of digital technologies. [ABSTRACT FROM AUTHOR]