AC26 AI/Instrumental & Control Track Bundle

CWEA Member: $85.00
Non-Member: $110.00

1.0 contact hours towards CWEA's CSM, certifications.
2.0 contact hours towards CWEA's LAB, certifications.
1.0 contact hours towards CWEA's MT, certifications.
3.0 contact hours towards CWEA's EIT, certifications.

This series includes the following 50 minute sessions. 

Session 1: Integrating Machine Learning and Artificial Intelligence into Management of Systems in the Water Industry

The water industry is experiencing a transformative shift driven by the convergence of digital technologies and data science. Among these, artificial intelligence (AI) is emerging as a powerful and disruptive technology to address challenges in water resource management, infrastructure optimization, and operations. AI models' ability to learn from and predict patterns directly from data, examples, and experience, rather than relying on mechanisms or pre-defined rules, makes this technology highly applicable to the water industry.

Machine learning (ML) is a subset of AI that enables systems to learn from data and improve performance over time without explicit programming. ML sits within the broader AI landscape and is particularly relevant for water applications due to its ability to handle structured and unstructured data, adapt to changing conditions, and uncover hidden insights. In water management, ML can be leveraged to predict equipment failures, optimize treatment processes, forecast influent flows, and enhance decision-making.

This presentation explores the evolving role of ML in the water sector, demystifies core concepts, and illustrates their application through real-world case studies. It also highlights the critical importance of data quality, system integration, and human factors for successful deployment. The presentation emphasizes that while ML may appear complex, it is fundamentally rooted in mathematical relationships and pattern recognition—making it accessible and highly valuable when applied correctly.

The presentation will provide a practical assessment of the ML algorithms gaining utility in the water industry and discuss the data requirements and challenges when implementing ML technologies. Four primary ML paradigms will be discussed: supervised learning, unsupervised learning, reinforcement learning, and deep learning. Real-world case studies will demonstrate the practical applications of ML in predicting influent flows and forecasting biogas production. The presentation will also provide an overview of the current state of ML applications in the water industry across five stages of maturity, from research/embryonic to mature applications. Finally, the presentation will highlight the challenges associated with poor data quality and the importance of a robust system architecture for successful ML deployment in the water industry.

Learning Objectives:
Understand the Role of AI and ML in the Water Industry: Gain insights into how AI and ML are transforming water resource management, infrastructure optimization, and operations.
Describe different ML paradigms and applications relevant to the water industry supervised learning, unsupervised learning, reinforcement learning, and deep learning.
Recognize the importance of data quality, system integration and human factors in the successful deployment of ML technologies in the water sector."

Session 2: Bottlenecks and Breakthroughs: Developing AI Models for Optimizing Calera Creek’s Wastewater Treatment Operations
Arup and the City of Pacific worked together to develop two AI/ML models as proof of concepts for sequential batch reactor (SBR) and auto-thermal thermophilic aerobic digestion (ATAD) operations at the Calera Creek Water Recycling plant. Arup and City of Pacifica first identified opportunities through visualizing historic plant data and then developed, trained, and tested chosen models. The project conclusions highlight barriers to implementation of AI/ML in plant operations, and identified necessary next steps to overcome these obstacles.

The Calera Creek Water Recycling Plant, located in Pacifica, California, is a critical facility that uses advanced SBR technology to efficiently combine aeration and clarification processes and ATAD technology to produce Class A sludge. These technical innovations allow the plant to meet the evolving challenges of wastewater treatment, nutrient removal, and environmental sustainability.

With the increasing complexity of its operations—especially during peak storm flows—there are a growing number of opportunities for advanced analytics, rooted in Artificial Intelligence and Machine Learning techniques, to optimize the plant’s operations. These include improving energy efficiency, enhancing process performance, and reducing the risk of unexpected faults or failures through managed predictive maintenance. This presentation will include insights from the Engineers, Operators, and Data Scientists’ perspective, as well as lessons learned from the project team when addressing challenges in developing optimization approaches towards SBR and ATAD operation.

The presentation will also cover how to effectively incorporate stakeholder input, pivot when presented with ambivalent results, and create roadmaps for effective integration of AI models in wastewater treatment plant operation.

Learning Objectives:
Develop strategies to encourage AI integration with existing wastewater treatment processes.
Recongnize bottlenecks to predictive AI/ML modeling for sequential batch reactor (SBR) and auto-thermophilic aerobic digester systems.
Manage uncertainty surrounding wastewater treatment plant data availability and quality."

Session 3: Generative AI for Operations and Maintenance for Utilities
Water and wastewater utilities face mounting challenges—aging infrastructure, workforce shortages, and increasing operational complexity. Generative AI is emerging as a transformative tool to support operations and maintenance (O&M) by making institutional knowledge more accessible, improving decision-making, and boosting workforce efficiency.

This session presents the development and deployment of a domain-specific Generative AI platform for utility O&M. The goal is to show how large language models (LLMs), integrated with structured utility data, can assist field crews, operators, and engineers in real time. The system—called a Knowledge Twin—uses retrieval-augmented generation (RAG) to deliver accurate, actionable responses to natural language queries on assets, procedures, alarms, and troubleshooting.

Currently piloted with multiple utilities, the platform accommodates varying levels of data maturity. Data sources such as CMMS records, SCADA logs, SOPs, GIS data, and work order histories are ingested, structured into a knowledge graph, and connected to a fine-tuned LLM trained on the terminology, context, and workflows unique to water and wastewater operations.

Early results show that field staff can resolve issues faster, locate information more easily, and preserve institutional knowledge despite staff turnover. Use cases include alarm response guidance, step-by-step maintenance support, asset history retrieval, and on-demand training. A key finding is the system’s ability to uncover insights from previously siloed or underused data, reducing time-to-resolution and unplanned downtime.

The approach is designed to start small—focusing on high-impact use cases—and scale as more data is curated or digitized. This lowers the barrier to entry, enabling utilities of all sizes to leverage AI without perfect datasets or major system changes.

The session will feature a live demonstration, practical deployment considerations, and lessons from early adopters. Attendees will leave with a clear understanding of how Generative AI can be safely and effectively implemented to enhance daily O&M, improve service reliability, and strengthen workforce capabilities.

As utilities confront aging infrastructure and the demands of digital transformation, Generative AI provides a powerful path to future-proof operations and empower the next generation of utility professionals.

Learning Objectives:
1. Understand what Generative AI is and how it applies specifically to utility operations and maintenance.
2. To see key operational use cases where Generative AI delivers measurable value.
3. To understand how AI tools using existing utility data—regardless of current data maturity."

AC26 Recordings Sponsored By: 

Registrants who view the live webinar to see the slides and hear the audio and then enter the correct attention check code (directions below) will receive  contact hours towards CWEA's certification:1.0 CSM, 2.0 LAB, 1.0 MT, 3.0 EIT.

To receive your contact hours for viewing the recording, you will need to view each video in the series. Upon completion of the last video in the series, the system will automatically unlock the attention check code for you view. The two (2) different attention check codes that will be displayed, and you will need to enter these codes as 1st attention check code – 2nd attention check code (XXXX-XXXX) in the Attention Check Code component under the "Contents" tab.  

Please note, all user activity of CWEA certification holders on the Online Wastewater Education Network is subject to the CWEA Code of Ethics standards for professional conduct and ethics. Certification holders should receive credit for a training only once within the same contact hour period. Any attempt to undermine the certification process may be subject to ethics procedures and possible sanctions. It is not possible to receive contact hours for both attending the live webinar and viewing the recording.  

Once you have entered the correct attendance check codes, you will be able to create and download an electronic certificate of completion under the "Contents" tab.