Improving the wastewater treatment plant performance through model predictive control strategies

Chiara Foscoliano, Stefania Del Vigo, Michela Mulas, Stefania Tronci

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

2 Citations (Scopus)

Abstract

Using the Benchmark Simulation Model No. 1 as virtual plant, the development of model based control strategies for an activated sludge process was addressed in this work. The dynamic matrix control algorithm was used to obtain the optimal control of ammonia and nitrate concentration by using dissolved oxygen concentrations in the bioreactor, and internal recycle flow rate as manipulated variables. The main goal of the proposed control strategies was the minimization of aeration and pumping energy consumption by guaranteeing good nitrogen removal efficiency. In order to mimic a more realistic situation, process model identification was carried out considering time varying inputs. Recurrent neural network were used to describe the required input-output relationships. Results showed that ammonia and nitrogen removal was enhanced even in the coldest season, with a reduction of energy consumption if compared with BSM1 default control strategy.

Original languageEnglish
Title of host publication26 European Symposium on Computer Aided Process Engineering, 2016
PublisherElsevier
Pages1863-1868
Number of pages6
Volume38
ISBN (Print)9780444634283
DOIs
Publication statusPublished - 2016
MoE publication typeA3 Part of a book or another research book
EventEuropean Symposium on Computer Aided Process Engineering - Grand Hotel Bernardin Congress Centre, Portorož, Slovenia
Duration: 12 Jun 201615 Jun 2016
Conference number: 26
http://escape26.um.si/

Publication series

NameComputer Aided Chemical Engineering
Volume38
ISSN (Print)15707946

Conference

ConferenceEuropean Symposium on Computer Aided Process Engineering
Abbreviated titleESPACE
CountrySlovenia
CityPortorož
Period12/06/201615/06/2016
Internet address

Keywords

  • BSM1
  • Dynamic Matrix Control
  • operational cost
  • Process identification

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