AC26 Bottlenecks and Breakthroughs: Developing AI Models for Optimizing Calera Creek’s Wastewater Treatment Operations

CWEA Members: $35.00
Non-Members $45.00
CWEA Contact Hours: 1.0 contact hours towards CWEA Certifications: EIT

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.
Recognize 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.

AC26 Recorded Sessions 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)1.0  contact hours towards CWEA's Contact Hours, EIT

To receive your contact hours for viewing the live webinar, please note the one (1) attention check code that will be displayed during the webinar in the top left or right corner of the presentation for approximately 90 seconds.  Please enter this code 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 code, you will be able to create and download an electronic "Certificate of Completion" under the "Contents" tab.