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Automatic fine-tuning of distributed predictive models

ACCIONA a global infrastructure operator, spent significant time and resources examining water samples in a laboratory to determine chemical concentrations. Due to the time it took to obtain these results, they were often outdated and unreliable. This resulted in additional costs related to chemical supply as well as possible regulatory penalties

By implementing real-time optimized Machine Learning control algorithms at each of its desalination plants, ACCIONA managed to minimize the use of reactive chemicals, eliminate associated regulatory penalties, and leverage an edge infrastructure to implement new applications for predictive maintenance, energy efficiency and reinforcement learning.