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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Adding realism to simulated sensors and actuators.

C Rosen¹, U Jeppsson, L Rieger

¹Department of Industrial Electrical Engineering and Automation, Lund University, Box 118, SE-221 00 Lund, Sweden. christian.rosen@iea.lth.se

Water Science and Technology : a Journal of the International Association on Water Pollution Research

|March 1, 2008

Summary

This study introduces a statistical framework using Markov chains to simulate sensor and actuator faults in wastewater treatment operations. This approach enhances control strategy development by realistically modeling failures, improving full-scale implementation.

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Area of Science:

Environmental Engineering
Control Systems Engineering
Statistical Modeling

Background:

Sensor and actuator faults are critical but often ignored in wastewater treatment simulations.
These faults significantly hinder the successful implementation of control strategies in real-world facilities.

Purpose of the Study:

To develop a statistical theoretical framework for integrating sensor and actuator faults into dynamic wastewater treatment simulations.
To provide a realistic simulation of faults for improved control strategy testing.

Main Methods:

The proposed framework utilizes Markov chains for fault incorporation.
It integrates sensor and actuator dynamics models with Markov theory.
This allows for the generation of realistic fault scenarios based on historical data.

Main Results:

A novel framework for simulating sensor and actuator faults in dynamic wastewater treatment operations is presented.
The framework effectively models fault generation using Markov chains and system dynamics.
It facilitates a more accurate representation of operational challenges.

Conclusions:

The developed framework offers a robust method for incorporating faults into simulations.
This improves the reliability of control strategy development and testing for wastewater treatment plants.
Realistic fault simulation is crucial for bridging the gap between simulation and full-scale application.