Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Load-frequency control01:28

Load-frequency control

320
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
320
Control Systems01:10

Control Systems

1.6K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.6K
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

264
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
264
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

231
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
231
Factors Affecting Activity Coefficient01:17

Factors Affecting Activity Coefficient

1.3K
The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size. 
The activity coefficient value for an ion is close to one when the solution has almost zero ionic strength, i.e., when the solution shows close to ideal behavior. As the ionic strength of the solution increases from 0 to 0.1 mol/L, a...
1.3K
Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

205
Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
205

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Using a hybrid modelling approach for high time-resolution prediction of influent orthophosphate load in a water resource recovery facility.

Water research·2025
Same author

Differentiating fouling from ageing for a condition-based diffuser maintenance.

Water research·2024
Same author

Automated data transfer for digital twin applications: Two case studies.

Water environment research : a research publication of the Water Environment Federation·2024
Same author

Making waves: A vision for digital water utilities.

Water research X·2023
Same author

Comparison of guideline- and model-based WWTP design for uncertain influent conditions.

Water science and technology : a journal of the International Association on Water Pollution Research·2023
Same author

To calibrate or not to calibrate, that is the question.

Water research·2022

Related Experiment Video

Updated: Nov 11, 2025

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation
08:17

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation

Published on: August 14, 2020

5.5K

Sensor bias impact on efficient aeration control during diurnal load variations.

Oscar Samuelsson1, Gustaf Olsson2, Erik Lindblom3

  • 1IVL Swedish Environmental Research Institute, Stockholm Sweden E-mail: oscar.samuelsson@ivl.se; Division of Systems and Control, Department of Information Technology, Uppsala University, Uppsala, Sweden.

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|March 26, 2021
PubMed
Summary

Sensor bias significantly impacts automatic control systems in water treatment, reducing aeration energy efficiency by up to 25% in activated sludge processes. Accounting for bias in controller setpoints can mitigate negative effects.

More Related Videos

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.5K
Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors
07:59

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

Published on: December 6, 2018

8.4K

Related Experiment Videos

Last Updated: Nov 11, 2025

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation
08:17

Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation

Published on: August 14, 2020

5.5K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.5K
Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors
07:59

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

Published on: December 6, 2018

8.4K

Area of Science:

  • Environmental Engineering
  • Process Control
  • Wastewater Treatment

Background:

  • Sensor drift and bias are critical but often overlooked factors in automatic control systems.
  • Harsh measurement environments in water resource recovery facilities degrade sensor data quality.

Purpose of the Study:

  • To investigate the impact of sensor bias on aeration energy efficiency and nitrogen removal in activated sludge processes.
  • To assess the combined effects of multiple biased sensors on process performance.

Main Methods:

  • Simulations were employed to model the effects of sensor bias on an ammonium cascade feedback controller.
  • Response surface methodology was utilized to optimize simulation parameters and analyze combined sensor bias effects.

Main Results:

  • Negative bias in ammonium and suspended solids sensors, along with flow variations, reduced nitrification aeration energy efficiency by 7-25%.
  • Total nitrogen removal showed less sensitivity to the tested sensor biases.
  • A specific interaction was observed between biased ammonium and dissolved oxygen sensors.

Conclusions:

  • Sensor bias poses a significant challenge to the performance of automatic control systems in wastewater treatment.
  • Considering expected sensor bias direction in controller setpoint definitions can help limit negative impacts.