Deploying Adaptive AI in Distributed Desalination Plants

Learn how to deploy edge AI systems in industrial desalination plants through a real-world case study of boron control optimization. Explore the challenges of replacing 12-24 hour lab tests with real-time AI predictions while maintaining cybersecurity and working with heterogeneous plant infrastructure. Discover how Acciona's partnership led to developing a distributed AI architecture using Docker containers, local-first deployment, and edge computing to manage boron levels across multiple desalination facilities. Examine the technical stack including MQTT brokers for data standardization, TensorFlow for inference, JupyterLab and MLflow for on-device training, and InfluxDB with Grafana for local monitoring. Understand how this approach achieved over $200,000 in annual savings at a single site through reduced chemical usage and regulatory penalties while maintaining data security within plant premises. See how the same edge AI framework extends to energy optimization for high-pressure pumps, membrane fouling detection, and computer vision applications in critical infrastructure environments.