AN INTELLIGENT DIGITAL TWIN OF THE MODEL FOR THE PRODUCTION OF DRY BUILDING MIXES BASED ON MAN-MADE WASTE
Keywords:
artificial intelligence, digital twin, dry mixes, technological process, architecture, modelingAbstract
The article discusses the development of an intelligent digital twin for the production of dry building mixes using man-made waste from metallurgy and energy based on artificial intelligence technologies. The relevance of the study lies in the need to increase the resource and environmental efficiency of dry mix production, reduce the proportion of clinker materials, and introduce digital methods of process control in conditions of variability in raw material properties.
The developed model is implemented as a hybrid digital twin that combines discrete event modeling of a production line and an intelligent subsystem for predicting product quality. Process control is formalized using a finite state machine that covers all key stages of production: component dosing, mixing, unloading, packaging, and batch archiving. Artificial intelligence is used to evaluate the physico-mechanical properties of a mixture (strength, density, workability) based on the composition of the formulation and the proportion of man-made components.
Quantitative results confirming the effectiveness of the proposed approach were obtained during the simulation. It has been established that the proportion of man-made waste in the composition of dry mixtures can reach 40-50% without a critical decrease in the predicted quality indicators. The reduction in cement consumption is up to 30%, while the estimated capacity of the line reaches 15-20 t/h. The model provides automatic accounting of slag and ash consumption by batch, shift, and month, as well as dynamic assessment of the carbon footprint of products.
The scientific novelty of the work lies in the integration of simulation modeling of the production cycle with the AI subsystem for assessing quality and environmental performance within a single digital model. The practical significance is determined by the possibility of using the developed model as a digital twin of the real production of dry building mixes, a tool for optimizing formulations, supporting management decisions and integrating into SCADA/MES systems of industrial enterprises.
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