Artificial Neural Networks Modelling of PID and Model Predictive Controlled Waste Water Treatment Plant Based on the Benchmark Simulation Model No.1

Conference proceedings article

Authors / Editors

Agachi, Paul

Research Areas

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Publication Details

Author list: Cristea VM, Pop C, Agachi PS

Publisher: Elsevier: Monograph Series

Place: AMSTERDAM

Publication year: 2009

Journal: Computer Aided Chemical Engineering (1570-7946)

Journal acronym: COMPUT-AIDED CHEM EN

Volume number: 26

Start page: 1183

End page: 1188

Number of pages: 6

eISBN: 978-0-444-53433-0

ISSN: 1570-7946

Languages: English-Great Britain (EN-GB)

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Abstract

The paper presents techniques for the design and training of Artificial Neural Networks (ANN) models for the dynamic simulation of the controlled Benchmark Simulation Model no. 1 (BSM1) Waste Water Treatment Plant (WWTP). The developed ANN model of the WWTP and its associated control system is used for the assessment of the plant behaviour in integrated urban waste water system simulations. Both embedded PID (Proportional-Integral-Derivative) control and Model Predictive Control (MPC) structures for the WWTP are investigated. The control of the Dissolved Oxygen (DO) mass concentration in the aerated reactors and nitrate (NO) mass concentration in the anoxic compartments are presented. The ANN based simulators reveal good accuracy for predicting important process variables and an important reduction of the simulation time, compared to the first principle WWTP simulator.

Keywords

Artificial Neural Networks Model, Model Predictive Control, PID control

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