AerationAI™
Site-Specific Aeration-Optimization Software for Existing Plant Infrastructure
Real-Time Dynamic Aeration Optimization for Reliable Treatment and Lower Energy Use
AerationAI™ is a proprietary, patent-pending wastewater-treatment application that applies facility-specific predictive methods, trusted operational information, engineering knowledge, process-response evaluation, and governed operational intelligence to improve aeration performance.
AerationAI is designed to determine the aeration appropriate for current and anticipated treatment conditions while protecting treatment performance, equipment, existing control safeguards, and operator authority.
It is designed to work with suitable existing plant instrumentation, PLC/SCADA systems, aeration equipment, historians, and biological treatment infrastructure.
Predict the requirement. Validate the information. Understand the response. Govern the action.
The objective is not minimum airflow. It is the airflow required for reliable biological treatment—no more and no less—as operating conditions change.
1. The Aeration-Control Challenge
Aeration is essential to biological wastewater treatment, but supplying more air than the process requires can consume unnecessary energy without improving treatment performance.
The challenge is that aeration requirements are not constant. They change with influent conditions, biological activity, process configuration, temperature, equipment performance, operating conditions, and treatment objectives.
Conventional aeration strategies may depend heavily on fixed operating targets, individual measurements, predetermined control relationships, or operator intervention. These approaches can perform effectively under their intended design conditions, but they may not fully anticipate changes in aeration requirements or distinguish between necessary and unnecessary aeration.
At the same time, aggressive aeration reduction can pose treatment risks if changes in biological requirements, instrumentation reliability, process response, equipment constraints, or operating conditions are not adequately considered.
Aeration optimization therefore requires more than simply reducing airflow or maintaining a predetermined dissolved-oxygen target.
The objective is not minimum airflow. The objective is the aeration required for reliable treatment—no more and no less.
2. What AerationAI Is
AerationAI is a facility-specific Engineering Intelligence application for predictive aeration optimization.
It combines available operational information with facility-specific engineering relationships, predictive methods, treatment requirements, process response, equipment capabilities, operating constraints, and approved authority to determine appropriate aeration recommendations as conditions change.
AerationAI is not intended to replace the facility’s existing treatment process, PLC/SCADA infrastructure, equipment protections, or operator authority.
Instead, it provides a supervisory Engineering Intelligence capability that can operate progressively—from historical analysis and parallel validation to operator advisory use and, where specifically validated and authorized, governed supervisory control.
3. What AerationAI Does
AerationAI converts available plant information into facility-specific aeration recommendations and, where authorized, governed operational actions through four high-level functions.
Understand Current and Anticipated Conditions
Uses available facility and operating information to characterize relevant current conditions and anticipate changing aeration requirements.
Determine Appropriate Aeration
Applies facility-specific predictive and engineering methods to determine the aeration appropriate for reliable treatment under changing operating conditions.
Evaluate Information, Process Response, and Constraints
Evaluates whether available operational information and observed process behavior provide an adequate basis for the proposed response and whether applicable treatment, equipment, engineering, and operating constraints are satisfied.
Recommend or Govern Action
Depending on the approved deployment stage, provides recommendations for operator review or supports specifically authorized, bounded supervisory actions while treatment and equipment performance continue to be monitored.
4. Core AerationAI Capabilities
AerationAI combines predictive aeration, trusted operational information, process-response evaluation, engineering constraints, and governed operational use.
Facility-Specific Predictive Aeration
Anticipates changing aeration requirements using facility-specific operating information and process behavior rather than relying solely on a fixed operating target.
Trusted Operational Information
Evaluates whether relevant operational information is sufficiently reliable and appropriate for its intended engineering use before consequential recommendations or actions are advanced.
Process-Response Evaluation
Considers actual facility response when evaluating whether aeration recommendations remain appropriate as operating conditions change.
Engineering Constraints and Safeguards
Evaluates proposed responses against applicable treatment requirements, equipment capabilities, operating boundaries, information requirements, and approved engineering safeguards.
Governed Operational Use
Supports recommendations or, where specifically authorized, bounded supervisory actions within approved operating conditions, existing safeguards, cybersecurity requirements, and facility authority.
5. How AerationAI Works
AerationAI follows a high-level Engineering Intelligence process:
Understand the Condition → Predict the Aeration Requirement → Validate the Information and Response → Apply Engineering Constraints → Recommend or Govern Action
The process is facility-specific. AerationAI considers the information, treatment objectives, process relationships, equipment capabilities, operating constraints, and control environment applicable to the individual facility.
AerationAI also evaluates operational feedback so that recommendations remain connected to actual facility behavior rather than relying solely on model output.
Prediction proposes the aeration requirement. Engineering Intelligence determines whether and how that recommendation should be used.
6. Operational Value
AerationAI is designed to improve aeration performance while keeping treatment reliability ahead of energy optimization.
Reduced Unnecessary Aeration
Identifies opportunities to reduce aeration when treatment conditions and engineering requirements permit.
Improved Energy Performance
Reducing unnecessary airflow can reduce blower energy demand, subject to facility configuration, equipment efficiency, operating conditions, and treatment requirements.
Treatment Protection
Aeration recommendations remain subordinate to treatment objectives, engineering constraints, equipment protection, and approved operating requirements.
More Consistent Aeration Decisions
Facility-specific predictive and engineering methods provide a repeatable technical basis for evaluating changing aeration requirements.
Better Use of Existing Operational Information
AerationAI uses suitable existing plant information and infrastructure to develop facility-specific operational intelligence.
Progressive Operational Authority
Facilities can begin with analysis and recommendations and expand authority only after performance, readiness, safeguards, and organizational authorization have been demonstrated.
Potential performance and energy benefits are facility-specific and should be established through documented validation.
7. Advantages and Benefits of AerationAI
AerationAI combines facility-specific predictive aeration, trusted operational information, engineering safeguards, and governed operation to improve aeration performance while protecting reliable treatment. Rather than treating lower airflow or lower DO as objectives themselves, AerationAI seeks the aeration conditions appropriate for treatment requirements, changing operating conditions, equipment capability, and facility-specific constraints.
Reduce Unnecessary Aeration and Energy Use
AerationAI determines aeration requirements from changing treatment conditions rather than assuming that more airflow is always better. It identifies where airflow can be reduced while maintaining treatment requirements, engineering constraints, and equipment protections.
Improve Treatment Reliability and Process Performance
AerationAI keeps reliable biological treatment ahead of energy optimization. Aeration recommendations are evaluated against treatment requirements, process response, equipment capability, operating constraints, and engineering safeguards.
Respond Proactively to Changing Treatment Conditions
Aeration requirements change with influent conditions, biological activity, temperature, equipment operation, and process configuration. AerationAI uses facility-specific predictive relationships to anticipate changing needs rather than relying only on reactive correction.
Improve Aeration Decisions and Operational Consistency
AerationAI provides a repeatable, facility-specific technical basis for evaluating aeration requirements as operating conditions change, supporting more consistent and technically defensible decisions.
Use Existing Plant Information and Infrastructure Effectively
AerationAI is designed to work with suitable existing instrumentation, PLC/SCADA systems, historians, blowers, airflow-control equipment, and treatment infrastructure while identifying material information, instrumentation, or integration gaps that could affect implementation.
Reduce Operational and Equipment Burden
More consistent aeration management can reduce unnecessary operator intervention and equipment operation while preserving existing protections, engineering constraints, control safeguards, and operator authority.
Enable Progressive, Governed Aeration Optimization
AerationAI can progress from Historical Analysis through Shadow Mode and Advisory Operation to, where validated and specifically authorized, Governed Control. Operational authority expands only as performance, information confidence, safeguards, facility readiness, and authorization justify it.
Performance and energy benefits are facility-specific and should be established through documented validation.
8. Applications and Demonstration
AerationAI can support aeration optimization in suitable biological wastewater treatment and water recycling applications where facility information, instrumentation, aeration equipment, process configuration, and operating objectives provide an appropriate technical basis.
AerationAI may be appropriate for facilities seeking to:
- Reduce unnecessary aeration energy
- Improve responsiveness to changing treatment conditions
- Strengthen aeration-management consistency
- Evaluate existing aeration-control performance
- Support operator decision-making
- Develop a validated pathway toward governed supervisory aeration control
Demonstration Using Real Plant Data
AerationAI can be demonstrated using historical plant operating information so facility personnel can evaluate how the technology interprets changing conditions, develops aeration recommendations, and responds to actual facility behavior.
Demonstration results are illustrative and do not establish performance claims for another facility.
9. AerationAI Within the Engineering Intelligence Platform
AerationAI is implemented through McC AI Group’s Engineering Intelligence Platform, which combines complementary capabilities for trusted operational information, site-specific engineering knowledge and evidence-based reasoning, facility-specific operational intelligence, and governed decisions and actions.
SensorAI™ — Trusted Operational Information
Determines whether measurements and operational information are sufficiently reliable, timely, representative, and appropriate for their intended engineering or operational use.
WisdomAI™ — Site-Specific Engineering Knowledge & Evidence-Based Reasoning
Applies site-specific engineering knowledge, approved practices, operating history, applicable requirements, and relevant evidence to interpret conditions, evaluate operational significance, and support evidence-based reasoning.
OperationsAI™ — Facility-Specific Operational Intelligence
Uses trusted operational information, facility-specific operational models and methods, operating objectives, process and equipment relationships, and engineering constraints to determine appropriate operational responses under current and anticipated conditions.
PilotAI™ — Governed Decisions & Actions
Evaluates whether a recommendation or action may be modified, constrained, held, blocked, escalated, authorized, or executed within approved operating boundaries and organizational authority.
Engineering Intelligence Architecture
Trusted Information → Engineering Knowledge → Evidence-Based Reasoning → Operational Intelligence → Governed Automation
Runtime Pathway
SensorAI → WisdomAI → OperationsAI → PilotAI
10. Integration With Existing Plant Infrastructure
AerationAI is designed to work with suitable existing plant and facility infrastructure rather than require wholesale replacement of existing automation or aeration systems.
Depending on the facility, integration may include:
- Process instrumentation
- PLC and SCADA systems
- Plant historians and operational databases
- Blowers and associated equipment
- Airflow-control equipment
- Aeration basins and treatment processes
- Existing alarms, interlocks, and equipment protections
- Relevant treatment and performance information
When suitable instrumentation, information quality, connectivity, and control interfaces are available, implementation can normally proceed without new process hardware or major physical modifications.
Where material information, instrumentation, or integration gaps exist, they are identified during facility evaluation.
11. Performance Validation Before Expanding Authority
Validation may include comparison of predicted and observed process behavior, assessment of information reliability, evaluation of aeration recommendations, confirmation of engineering constraints and safeguards, and review of treatment performance.
Progression is based on demonstrated performance and facility readiness—not simply elapsed time.
Historical Analysis — Evaluate Data & Opportunity
Evaluate available historical information to understand facility behavior, aeration performance, operating patterns, instrumentation, treatment requirements, and potential improvement opportunities.
Facility-Specific Model Development — Develop & Validate Site-Specific Methods
Develop and evaluate the facility-specific predictive methods, operating relationships, engineering constraints, and aeration logic required for the application.
Shadow Mode — Validate in Parallel
Operate AerationAI alongside the existing plant control system without changing its physical operation. Predictions, recommendations, information reliability, and process response are evaluated against actual plant performance.
Advisory Operation — Recommend With Operator Review
Provide validated recommendations to authorized facility personnel for review. Operators retain responsibility for determining whether and how recommendations are implemented.
Governed Control — Authorized Bounded Actions
Following successful validation and explicit facility authorization, AerationAI may support specifically authorized supervisory actions within approved engineering, treatment, equipment, cybersecurity, operational, and organizational boundaries.
Historical Analysis → Shadow Mode → Advisory Operation → Governed Control
Operational authority expands only as evidence, performance, engineering review, and site authorization justify it.
Progression is not automatic. A facility may remain in Advisory Operation indefinitely if that best meets its operational or organizational requirements.
12. Governance and Deployment Safeguards
AerationAI is designed to improve aeration performance without removing the protections that already govern plant operations.
Secure Industrial Deployment
AerationAI can be configured for deployment in a manner consistent with facility IT/OT and cybersecurity requirements.
Implementation does not inherently require unrestricted PLC access, continuous external connectivity, or removal of existing cybersecurity controls.
The specific cybersecurity architecture is established for the facility and intended level of operational authority.
Any external connectivity, remote support, data transfer, or plant control interface is established in accordance with the facility’s approved IT/OT architecture, cybersecurity policies, access controls, and explicit authorization.
The facility determines what information AerationAI can access and what level of operational authority, if any, it receives.
Preservation of Existing Plant Control
AerationAI operates as a supervisory Engineering Intelligence capability and does not require elimination of the facility’s existing PLC-based aeration-control strategy.
Existing PLC interlocks, equipment protections, cybersecurity controls, fallback provisions, and operator override remain in place.
Authorized personnel can pause AerationAI, reject recommendations, restrict operational authority, override governed actions, and return operation to approved existing plant control according to the site configuration.
The graphic illustrates how AerationAI can operate within the existing plant-control environment while preserving bounded authority, operator oversight, and the ability to return to existing PLC-based aeration control.
Continuous Performance Evaluation
Operational performance, information quality, treatment response, and applicable constraints can continue to be evaluated after deployment.
Where required conditions are no longer satisfied, operational authority can be restricted and operation returned toward an approved fallback condition.
Treatment performance, equipment protection, and operator authority remain ahead of energy optimization.
13. Relationship to AerationAI-SND™
AerationAI and AerationAI-SND share a facility-specific, predictive, and governed Engineering Intelligence foundation but address different operating objectives.
AerationAI™
AerationAI provides Real-Time Dynamic Aeration Optimization for a broader range of biological wastewater treatment and water recycling applications.
It seeks to provide the aeration required for reliable biological treatment while reducing unnecessary energy use.
AerationAI-SND™
AerationAI-SND extends this foundation to low-DO operating conditions intended to support simultaneous nitrification and denitrification where the facility and biological process are suitable.
It incorporates specialized biological safeguards and governed low-DO operation for facilities seeking improved nitrogen removal and aeration efficiency.
AerationAI optimizes aeration broadly. AerationAI-SND addresses the specialized biological requirements of governed low-DO SND operation.
14. Proprietary Technology and Intellectual Property
AerationAI incorporates proprietary and patent-pending technologies for facility-specific aeration prediction, optimization, information validation, process-response evaluation, engineering safeguards, and governed operational use.
AerationAI’s protected technical value is not based on a single generic AI model. It integrates facility-specific operational information, predictive aeration requirements, process response, engineering constraints, and governed decision-making within a deployable wastewater-treatment application.
Additional implementation methods, facility-specific models, configuration practices, validation procedures, and deployment methodologies remain proprietary.
15. AerationAI Technology Evaluation
An initial AerationAI evaluation examines the facility’s aeration system, treatment objectives, operational information, existing controls, and evidence of opportunities to improve aeration performance while protecting treatment.
Evaluation may consider:
Treatment and Aeration Configuration
Biological treatment process, aeration basins or zones, blowers, air-distribution systems, and existing aeration-control configuration.
Operational Information
Available process, aeration, energy, influent, treatment-performance, and other relevant operating information.
Existing Control Strategy
Current aeration-control methods, PLC/SCADA interfaces, equipment protections, operating constraints, and operator practices.
Performance Opportunity
Evidence of unnecessary aeration, energy inefficiency, variable process response, operating inconsistency, or other potential improvement opportunities.
Implementation Environment
Instrumentation suitability, information quality, historian availability, cybersecurity requirements, safeguards, and compatibility with existing infrastructure.
16. Performance Qualification and Implementation Readiness
AerationAI performance and implementation readiness should be established using actual facility information, engineering analysis, historical operating conditions, and prospective operational evaluation rather than assumptions based solely on results from other facilities.
Qualification may consider prediction performance, process response, treatment performance, information and instrumentation reliability, operating constraints, Shadow Mode validation, operator review, energy performance, and validation across representative operating conditions.
Implementation readiness is evaluated through four areas:
1. Facility and Process Assessment
Evaluate treatment configuration, aeration infrastructure, instrumentation, operating history, equipment capability, treatment objectives, engineering constraints, and existing controls.
2. Information and Integration Assessment
Evaluate required operational information, historian and PLC/SCADA interfaces, information quality, cybersecurity requirements, safeguards, and material information gaps.
3. Historical Opportunity Assessment
Evaluate facility-specific evidence of unnecessary aeration, operating inefficiency, or other performance-improvement opportunities while protecting treatment performance.
4. Pilot Validation
Establish a facility-specific pathway through Historical Analysis and Shadow Mode, followed where appropriate by Advisory Operation or Governed Control.
McC AI Group does not assume that results demonstrated at one facility will automatically transfer to another.
Performance claims should be based on documented, facility-specific validation.
PREDICT the Requirement → VALIDATE the Information → UNDERSTAND the Response → GOVERN the Action
17. Technology and Deployment Partnerships
AerationAI can be evaluated, validated, and deployed through collaboration with wastewater-treatment facilities, engineering organizations, control-system integrators, technology providers, and qualified implementation partners.
Potential collaborators may include municipal and industrial wastewater facilities, consulting engineers, control-system integrators, equipment manufacturers, engineering contractors, research organizations, and qualified technology and commercialization partners.
Each engagement is structured around the facility’s treatment objectives, aeration infrastructure, existing controls, available operational information, engineering constraints, cybersecurity requirements, safeguards, and approved level of operational authority.
Understand the facility. Validate the opportunity. Protect treatment. Govern the operation.
18. Explore AerationAI for Your Facility
AerationAI can be evaluated using available facility information to determine whether existing treatment conditions, aeration infrastructure, operational information, and control systems provide an appropriate basis for application.
An initial evaluation can identify potential opportunities to improve aeration performance, determine information and integration requirements, and establish an appropriate pathway for further validation.
AerationAI can be introduced progressively through:
Historical Analysis → Shadow Mode → Advisory Operation → Governed Control
Progression is based on demonstrated performance and facility readiness—not simply elapsed time.
Evaluate the opportunity. Validate the technology. Protect treatment. Advance at the facility’s approved level of operational authority.
McC AI Group, Inc. 