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In an era where water scarcity and quality issues are increasingly prevalent, federated Edge AI emerges not just as a technological innovation, but as a crucial step towards a more resilient and sustainable water industry. Water companies are facing increasing challenges in treating water, including process inaccuracies. A proactive approach is vital to address these effectively, focusing on tackling challenges instead of reacting after they occur.
While machine learning (ML) experiments with interconnected IoT devices are ongoing, insights are often retrospective, limiting the ability to proactively address potential issues. As the volume of data grows, traditional centralised solutions become increasingly expensive and hard to manage. This paradigm shift to proactive problem-solving, enabled by federated learning, signifies a transformative approach to managing water resources efficiently and sustainably.
Federated Edge AI has the potential to revolutionise data analytics and predictive modelling in the water industry. It enables continuous learning from diverse local data sources, enabling the water industry to anticipate and tackle future challenges.
The water industry faces complex operational challenges, regulatory risks, and the need for advanced predictive solutions to ensure the provision of safe and sustainable water resources for communities and industries.
Operating and managing water treatment facilities involves high operational costs and complexity, posing a significant challenge for organisations. These facilities rely on advanced technologies, skilled personnel, and continuous monitoring to ensure efficient and effective water treatment.
In the next decade, total operating expenditures for wastewater systems are predicted to reach $368 billion, accounting for 42% of the overall expenditure.
Compliance with regulatory standards ensures the safety and quality of treated water while avoiding regulatory risks, fines, and reputational damage. Robust systems and processes are vital for organisations to ensure compliance.
Traditional approaches in the water industry typically rely on hindsight to identify and address problems. The cost of mismanagement, such as fines for incorrect dosing, is quantifiable and easily obtainable. However, water companies lack the necessary data to determine an optimised process, resulting in over-dosing and normalised approaches. That is why there is a growing demand for AI and ML solutions in the industry as they offer predictive capabilities. These technologies help companies analyse real-time data, detect patterns, and make proactive recommendations.
Federated learning’s adaptability plays a pivotal role in meeting current regulatory standards and swiftly adapting to future regulatory changes, ensuring continued compliance and safeguarding against potential fines.
The widespread availability of data and the potential benefits of ML are driving increased interest in its application to water management. Despite being recognised by water companies, its implementation has been gradual and fragmented.
While some water companies have started exploring machine learning, there is a stronger emphasis on data management rather than utilising ML for practical applications. However, the water industry is realising the value of integrating ML technologies, leading to broader adoption and advancements in water management practices and regulatory compliance. This shift acknowledges the benefits, such as improved operational efficiency and enhanced predictive capabilities in water treatment.
Federated learning is a machine learning approach that keeps data decentralised, allowing local devices or servers to develop training models. In the water industry, federated learning holds significant promise, offering solutions to challenges that traditional ML methods may not fully address.
Federated learning provides several advantages over traditional machine learning methods, particularly in terms of data privacy and security. Unlike machine learning, federated learning trains models directly on local devices without sharing raw data. Its decentralised approach enables scalability to distributed data sources while minimising the risk of system overload or downtime. It enables continuous learning from various data sources and has the potential to make a significant impact on the water value chain.
The water sector presents several promising use cases for federated Edge AI, with applications ranging from optimising chemical dosing and improving sludge management to enhancing treatment processes. The power of federated Edge AI can ensure the privacy of sensitive data while delivering high-quality water to consumers.
By harnessing federated learning, water companies can engage in a collaborative, industry-wide effort to refine treatment processes, leveraging shared insights while preserving the privacy of individual data sets.
Water treatment involves chemical dosing and carries regulatory risks, which come with heavy fines for incorrect treatment. Chemical dosing is used to adjust water with incorrect pH levels, and highly variable input requires accurate dosing to maintain optimal conditions. Federated Edge AI enables real-time data analysis from sensors and precise dosing strategies, maintaining optimal pH levels while ensuring data privacy.
Dosing systems require process optimisation to prevent mismanagement and overdosing. Federated Edge AI is an ideal infrastructure for dosing systems, enabling localised and scalable data analysis and decision-making, leading to more efficient and precise dosing processes.
Federated Edge AI can optimise treatment processes for both water consumption and wastewater treatment. It helps in efficiently using chemicals to remove solids from water, improving overall treatment efficiency.
Sludge is a biological degradation process that breaks down chemicals in waste using biology within the waste itself. Sludge transitions from one holding tank to another, with different tanks resulting in different degradation of organic waste. Settling tanks are used to break down waste and separate it from the solution.
Reactive management requires personnel on call at all times to deal with issues. Restoring sludge reactors can be an expensive process, involving transporting sludge from another site or even acquiring another water company.
Federated Edge AI enables collaboration and knowledge sharing while preserving data privacy. It helps optimise sludge removal schedules and improve drying and processing techniques across multiple water treatment facilities.
Advancements in federated Edge AI infrastructure offer promising applications in the water sector, such as optimising treatment processes, improving wastewater treatment, and lowering energy consumption and operational costs.
Beyond operational efficiencies and cost savings, federated Edge AI’s optimised processes contribute significantly to environmental conservation efforts, reducing the industry’s carbon footprint and promoting sustainable water management practices.
Water regulations have shaped the water industry over the past decade, tasking water company teams to deliver high-quality wastewater treatment, which is expensive and runs the risk of high fines. Process optimisation mismanagement remains challenging due to a lack of necessary data. Inconsistent water quality underscores the need for water treatment optimisation in closed systems with potential for improvement. Federated learning can be deployed across the water industry to optimise treatment processes to reduce energy consumption, reduce costs – and most importantly, deliver safe water to consumers.
The water sector aims to achieve net-zero carbon emissions by 2050. Achieving this goal requires innovative solutions, such as machine learning and federated Edge AI infrastructure, that optimise resource allocation and identify energy-efficient treatment processes. This approach drives cost-effective and sustainable energy usage in water treatment. For example, more efficient dosing processes lead to reduced carbon emissions.
Mismanagement in the water industry is very expensive, so water companies can benefit from federated Edge AI that allows continuous learning and predicts future challenges. By maintaining model accuracy, water companies can avoid steep mismanagement fines and eliminate costs from unexpected downtime.
Centralised cloud systems, widely used in ML integration, are vulnerable to cyber attacks due to their centralised data collection. Federated Edge AI models keep data on local devices and servers, enhancing privacy and security while minimising unauthorised access risks.
Federated Edge AI also offers greater control and flexibility, making it a more secure alternative to central cloud systems even without an internet connection. Federated Learning decreases the need for data transfer, thereby reducing system latency issues. It prioritises data locality, ensuring that organisations’ proprietary water treatment data is secure and scalable.
Federated Edge AI provides a secure, privacy-preserving, and efficient approach to using ML for water processes compared to centralised cloud systems.
As cyber threats evolve, the intrinsic security and resilience of federated Edge AI not only protect current operations but also future-proof the water industry against emerging digital risks.
Thames Water plays a vital role in providing water supply and sanitation services to approximately 16 million people in London and the South East region. Thames Water’s adoption of ML techniques is a great example underscoring the importance of innovation in building organisational resilience. Thames invested £1 billion in a transformation program set to conclude in 2025. Their adoption of smart technologies, including ML and data analytics, aims to enhance water services and optimise water abstraction. Machine Learning has enabled Thames Water to make informed decisions, improve system performance, and enhance resilience.
This whitepaper highlights the role and potential impacts of federated Edge AI in the water industry. Water treatment companies can enhance capabilities, overcome challenges, and ensure the provision of safe and sustainable water resources by using innovative FL technology. Federated learning can be applied in chemical dosing, closed system applications, overflows, dosing systems, and treatment processes in the water industry.
While revolutionising data analytics and predictive modelling, Federated Edge AI offers security and resilience improvements, regulatory compliance, and avoidance of fines. The integration of federated Edge AI in the water industry has the potential to drive advancements in water management practices and unlock significant value.
The journey towards integrating federated Edge AI within the water industry begins with forward-thinking companies like yours. By partnering with OctaiPipe, you’re not just adopting cutting-edge technology; you’re leading the charge towards a smarter, safer, and more sustainable water future.
At OctaiPipe, we excel in federated Edge AI in distributed IoT environments, being at the forefront of technological advancements in critical infrastructure. Our platform seamlessly integrates Edge capabilities with federated learning technology.
Embrace a new era of critical infrastructure with OctaiPipe and leave behind traditional approaches. OctaiPipe’s federated Edge AI solution empowers professionals and organisations to gain valuable insights at the device level, enabling secure and cost-efficient data sharing across devices.
