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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]

A virtual qualification (VQ) model is proposed using a digital twin not only for verifying electrical properties of a liquid crystal display (LCD) but also for assessing the effect of environmental stresses. The digital twin is developed to reflect product specifications and distributions of manufacturing process data. The VQ model utilizes the digital twin to infer and verify the changes in electrical characteristic values in the internal circuit under a high‐temperature operating test. Additionally, a procedure is proposed for developing digital twins, and its effectiveness is demonstrated based on a case study of an LCD. The developed digital twin may also be used for similar LCD models for design verification. [ABSTRACT FROM AUTHOR]

The safety, durability and power density of lithium‐ion batteries (LIBs) are currently inadequate to satisfy the continuously growing demand of the emerging battery markets. Rapid progress has been made from material engineering to system design, combining experimental results and simulations to enhance LIB performance. Limited by spatial and temporal resolution, state‐of‐the‐art advanced characterization techniques fail to fully reveal the complex multi‐scale degradation mechanism in LIBs. Strengthening interaction and iteration between characterization and modeling improves the understanding of reaction mechanisms as well as design and management of LIBs. Herein, a seed cyber hierarchy and interactional network framework is demonstrated to evaluate the overall lifecycle of LIBs. The typical examples of bridging the characterization techniques and modeling are discussed. The critical parameters extracted from multi‐scale characterization can serve as digital inputs for modeling. Furthermore, advanced computational techniques including cloud computing, big data, machine learning, and artificial intelligence can also promote the comprehensive understanding and precise control of the whole battery lifecycle. Digital twins techniques will be introduced enabling the real‐time monitoring and control of LIBs, autonomous computer‐assisted characterizations and intelligent manufacturing. It is anticipated that this work will provide a roadmap for further intensive research on developing high‐performance LIBs and intelligent battery management. [ABSTRACT FROM AUTHOR]

Characterization, digital twins, machine learning, simulation Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle (Adv. Keywords: characterization; digital twins; machine learning; simulation EN characterization digital twins machine learning simulation 1 1 1 09/05/22 20220901 NES 220901 B Lithium Batteries b In article number 2200889, Kai Yang, Shichun Yang and co-workers demonstrate the potential of cyber hierarchy and interactional network framework (CHAIN) to bridge multi-scale characterization and modeling technologies to evaluate lithium battery overall lifecycle. [Extracted from the article]