Optimizing Chemical Usage in Water Plants Through Machine Learning in the Edge. Customer Story

Machine Learning in Distributed Water Plants

Traditionally, monitoring and operating water networks depended on sensors and SCADA systems, placing significant reliance on human operators. However, to improve efficiency, infrastructure managers were  looking into implementing advanced algorithms at the edge.

The volume of data handled by AI/ML within water management infrastructures is expanding daily. To navigate the complexities and sheer scale of this data, infrastructure managers are increasingly turning to Edge infrastructures like Barbara's, which enables the real-time processing of edge data without risking the integrity of the operational technology (OT) network.

‍The Role of Machine Learning in Reagent Dosing Control

Reagent dosing control loops are crucial for targeting variable chemical compounds in water and Machine Learning algorithms offer a groundbreaking solution by enabling automatic fine - tuning of chemical dosage bearing in mind the variables of the environment of each facility plant.

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Recommended reading: Adaptive AI and the Role of Edge Computing

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‍Customer Story: Deploying AI models in the Edge for water quality control

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 Due to the time it took to obtain these results, they were often outdated and unreliable.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 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 predictive applications.

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‍What was Acciona looking for?

1. Virtual Chemical Predictions: The objective was to utilise algorithms capable of forecasting chemical compositions in water, a task typically achievable only through laboratory analysis. By consistently monitoring associated parameters, ACCIONA aimed to attain enhanced control over these crucial variables.

2. Cost Reduction: By optimizing the control loops for reagent dosing, the goal was to reduce the significant expenses associated with heavy chemical usage, and also achieving substantial savings on penalty payments.‍

3. Global Scalability: The company aimed to extend the solution across its 89 plants worldwide, ensuring consistent efficiency and operational excellence.‍

4. Efficient Data Capture from Diverse Sources: Efficient data ingestion from a variety of sensors, machines, and devices was crucial. Alongside, ensuring the secure storage and management of all data was paramount.‍

5. AI Model Deployment and Fine-Tuning: The strategy included seamless deployment of AI models, coupled with the capability to automatically refine these models based on environmental variables, ensuring adaptability and resilience.‍

6. Remote Monitoring and Maintenance: Centralized, remote monitoring and maintenance of the entire lifecycle of applications and devices was essential for streamlined operations and enhanced performance.

Barbara´s Edge AI Platform to cater ACCIONA´s needs

Edge Computing is becoming instrumental for organizations looking to take full advantage of the Internet of Things (IoT) and other edge-oriented technologies.

With the explosion of connected devices and the need for real-time data processing, it is no longer practical to send all data to a centralized data centre. To achieve ACCIONA´s goals a solution involving Edge Processing was needed. This was crucial for the following reasons: 

1. Cutting down costs linked to data capture and transmission to the Cloud, particularly in situations requiring ongoing data collection.

2. Complying with stringent security and data privacy standards was critical to ACCIONA.

3. Accessing to real-time data processing capabilities.

Barbara's proposed solution included the software infrastructure to deploy, orchestrate and maintain AI models in the Edge. The deployment of Barbara OS on edge nodes, coupled with Barbara Edge Management Panel, streamlined the deployment, execution, and autonomous optimization of various applications and algorithms.

More precisely the solution involved:

• Connectivity and data processing with alarms in case of incidents.
• Running, updating and configuring “dockerized” applications remotely and securely.  
• Lifecycle management of edge nodes / edge gateways (upgrades, reconfigurations, etc).
• A graphic interface to view measurements in real-time.

Through  Barbara Panel, ACCIONA achieved seamless connectivity across all its on-premise assets, enabling the deployment of dockerized applications—whether developed in-house or installed directly from Barbara Marketplace.

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Barbara Panel (The Edge Management and Orchestration tool), allows for secure, remote updates and reconfigurations, granting complete governance over edge nodes for comprehensive lifecycle management.  

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Want to know more? download Acciona´s story here

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The Technological Challenges of deploying Machine Learning at the Edge

The biggest challenge involved the fine-tuning of distributed models to accommodate new environmental variables.‍

This flexibility was essential for achieving optimal performance from a single model across various plants, each with its own distinct conditions such as temperature, operational state, and positioning.

Additionally, it was the challenge of achieving robust connectivity, particularly when navigating the intricacies of the OPC-UA protocol and its authentication demands.

Barbara´s technological solution enabled a seamless integration of the remote management of applications and edge nodes with Acciona´s water plant servers safeguarding data privacy and device security through encrypted storage and transmission.

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Expected impact of Edge AI in numbers‍

• Over £23 million USD across its 89 desalinations plants, just the first year.
• An 800% increase in the speed of introducing new applications to any facility.
• The deployments of new predictive model such as water turbidity model.

‍Barbara, the Cybersecure Edge AI Platform for Water Plants  

Edge Computing is becoming an essential technology for organizations looking to take full advantage of the Internet of Things (IoT) and other edge-oriented technologies. With the explosion of connected devices and the need for real-time data processing, it is no longer practical to send all data to a centralized data centre.

An Edge Platform is necessary to orchestrate this infrastructure as it provides the ability to manage and control the edge devices, applications, and data, while also providing security, scalability and flexibility.  ‍‍

‍Barbara helps organizations accelerate their AI model and Edge App deployments, orchestrating and maintaining easily container-based or native applications across thousands of distributed edge nodes.

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The critical data in the industry originates 'at the edge', across thousands of IoT devices, industrial plants, and equipment machines. Explore how to transform data into real-time insights and actions, using the most efficient and zero-touch Edge AI Platform.

‍ Get a free trial today.

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