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Equipment and processes based on complex physical and chemical laws are difficult to model in conventional ways.
Machine learning will be used to create identical digital counterparts of equipment and process stages with all fully simulated properties.
This digital twin will be used to find more accurate and complete settings for maximum process efficiency.