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

Vasile Mircea Cristea, Cristian Pop, Paul Serban Agachi

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publication19th European Symposium on Computer Aided Process Engineering
EditorsJacek Jezowski, Jan Thullie
Pages1183-1188
Number of pages6
DOIs
Publication statusPublished - Jun 26 2009

Publication series

NameComputer Aided Chemical Engineering
Volume26
ISSN (Print)1570-7946

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

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    Cristea, V. M., Pop, C., & Serban Agachi, P. (2009). Artificial Neural Networks Modelling of PID and Model Predictive Controlled Waste Water Treatment Plant Based on the Benchmark Simulation Model No.1. In J. Jezowski, & J. Thullie (Eds.), 19th European Symposium on Computer Aided Process Engineering (pp. 1183-1188). (Computer Aided Chemical Engineering; Vol. 26). https://doi.org/10.1016/S1570-7946(09)70197-X