Soft-sensing and BNR optimization using Advanced Process Simulation
Tuesday, September 29, 2026 3:00 PM to 4:30 PM · 1 hr. 30 min. (US/Central)
New Orleans Convention Center
Technology Spotlight
Hall H Booth 7249
Information
Wastewater treatment processes are inherently complex and opaque systems where biological nutrient removal (BNR) performance is governed by dynamic interactions between physical, biological, and chemical mechanisms. While online sensors for ammonia, nitrate, nitrite, and phosphate provide valuable insights into these processes, translating their signal into actionable items is not always straightforward. Measurements are strongly influenced by probe location, process conditions, and system dynamics, often leading to uncertainty in assessing true process state. This Technology Spotlight demonstrates how process simulation can be used to improve the selection of sensor locations and support the data generated by sensors to improve interpretation and enhance BNR process understanding.
In this session, attendees will be introduced to the GPS X process simulation platform. GPS X is an engineering simulation tool used to create digital representations of wastewater treatment plants based on established industry process models for biological, physical, and chemical processes. By simulating system behavior under dynamic operating conditions, the platform provides insight into nutrient concentrations and process performance throughout the treatment train, many of which cannot be directly measured.
Attendees will be presented with a digital representation of a multi-stage BNR process developed in GPS X which was designed as part of Water Research Foundation project 5087 focused on BNR sensors and control systems. Using this model, the session will explore how sensor selection and probe location influence measured nutrient signals and the resulting interpretation of process performance. Concentration profiles will be generated across the BNR to visualize how ammonia, nitrate, and phosphate concentrations evolve throughout the treatment train. These results will demonstrate how measurements taken at different locations can lead to fundamentally different conclusions about system behavior, highlighting the importance of strategic sensor placement in supporting effective operational decision-making.
The impact of probe location on process performance will be demonstrated through the implementation of an ammonia-based aeration control (ABAC) strategy using sensors placed at different points along the treatment train. ABAC systems use online ammonia measurements to dynamically adjust dissolved oxygen setpoints, optimize aeration energy consumption with process treatment requirements. By applying this control approach with sensors at varying locations, attendees will observe how probe placement influences controller response, process stability, and overall operating cost. These comparisons will highlight how sensor selection and positioning directly affect control effectiveness and the ability to achieve reliable nutrient removal.
In addition, it will be demonstrated how process simulation can be used to extend the value of physical instrumentation through the development of soft sensors. By combining available sensor measurements with dynamic simulation, soft sensors can be used to estimate process variables that cannot be directly measured. This approach provides deeper insight into underlying process conditions, enabling validation of measured data and improving confidence in system interpretation. As a result, operators and engineers can make more informed decisions to optimize plant performance.
In this session, attendees will be introduced to the GPS X process simulation platform. GPS X is an engineering simulation tool used to create digital representations of wastewater treatment plants based on established industry process models for biological, physical, and chemical processes. By simulating system behavior under dynamic operating conditions, the platform provides insight into nutrient concentrations and process performance throughout the treatment train, many of which cannot be directly measured.
Attendees will be presented with a digital representation of a multi-stage BNR process developed in GPS X which was designed as part of Water Research Foundation project 5087 focused on BNR sensors and control systems. Using this model, the session will explore how sensor selection and probe location influence measured nutrient signals and the resulting interpretation of process performance. Concentration profiles will be generated across the BNR to visualize how ammonia, nitrate, and phosphate concentrations evolve throughout the treatment train. These results will demonstrate how measurements taken at different locations can lead to fundamentally different conclusions about system behavior, highlighting the importance of strategic sensor placement in supporting effective operational decision-making.
The impact of probe location on process performance will be demonstrated through the implementation of an ammonia-based aeration control (ABAC) strategy using sensors placed at different points along the treatment train. ABAC systems use online ammonia measurements to dynamically adjust dissolved oxygen setpoints, optimize aeration energy consumption with process treatment requirements. By applying this control approach with sensors at varying locations, attendees will observe how probe placement influences controller response, process stability, and overall operating cost. These comparisons will highlight how sensor selection and positioning directly affect control effectiveness and the ability to achieve reliable nutrient removal.
In addition, it will be demonstrated how process simulation can be used to extend the value of physical instrumentation through the development of soft sensors. By combining available sensor measurements with dynamic simulation, soft sensors can be used to estimate process variables that cannot be directly measured. This approach provides deeper insight into underlying process conditions, enabling validation of measured data and improving confidence in system interpretation. As a result, operators and engineers can make more informed decisions to optimize plant performance.