Improving the wastewater treatment plant performance through model predictive control strategies

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

doi:10.1016/B978-0-444-63428-3.50315-5

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 proceeding › Chapter › Scientific › peer-review

4 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 language	English
Title of host publication	26 European Symposium on Computer Aided Process Engineering, 2016
Publisher	Elsevier
Pages	1863-1868
Number of pages	6
Volume	38
ISBN (Print)	9780444634283
DOIs	https://doi.org/10.1016/B978-0-444-63428-3.50315-5
Publication status	Published - 2016
MoE publication type	A3 Book section, Chapters in research books
Event	European Symposium on Computer Aided Process Engineering - Grand Hotel Bernardin Congress Centre, Portorož, Slovenia Duration: 12 Jun 2016 → 15 Jun 2016 Conference number: 26 http://escape26.um.si/

Publication series

Name	Computer Aided Chemical Engineering
Volume	38
ISSN (Print)	15707946

Conference

Conference	European Symposium on Computer Aided Process Engineering
Abbreviated title	ESPACE
Country/Territory	Slovenia
City	Portorož
Period	12/06/2016 → 15/06/2016
Internet address	http://escape26.um.si/

Keywords

BSM1
Dynamic Matrix Control
operational cost
Process identification

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/B978-0-444-63428-3.50315-5

Cite this

@inbook{a30272f255ee40f8ab4f15c7caf48bf8,

title = "Improving the wastewater treatment plant performance through model predictive control strategies",

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.",

keywords = "BSM1, Dynamic Matrix Control, operational cost, Process identification",

author = "Chiara Foscoliano and {Del Vigo}, Stefania and Michela Mulas and Stefania Tronci",

year = "2016",

doi = "10.1016/B978-0-444-63428-3.50315-5",

language = "English",

isbn = "9780444634283",

volume = "38",

series = "Computer Aided Chemical Engineering",

publisher = "Elsevier",

pages = "1863--1868",

booktitle = "26 European Symposium on Computer Aided Process Engineering, 2016",

address = "Netherlands",

note = "European Symposium on Computer Aided Process Engineering, ESPACE ; Conference date: 12-06-2016 Through 15-06-2016",

url = "http://escape26.um.si/",

}

Foscoliano, C, Del Vigo, S, Mulas, M & Tronci, S 2016, Improving the wastewater treatment plant performance through model predictive control strategies. in 26 European Symposium on Computer Aided Process Engineering, 2016. vol. 38, Computer Aided Chemical Engineering, vol. 38, Elsevier, pp. 1863-1868, European Symposium on Computer Aided Process Engineering, Portorož, Slovenia, 12/06/2016. https://doi.org/10.1016/B978-0-444-63428-3.50315-5

Improving the wastewater treatment plant performance through model predictive control strategies. / Foscoliano, Chiara; Del Vigo, Stefania; Mulas, Michela et al.
26 European Symposium on Computer Aided Process Engineering, 2016. Vol. 38 Elsevier, 2016. p. 1863-1868 (Computer Aided Chemical Engineering; Vol. 38).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific › peer-review

TY - CHAP

T1 - Improving the wastewater treatment plant performance through model predictive control strategies

AU - Foscoliano, Chiara

AU - Del Vigo, Stefania

AU - Mulas, Michela

AU - Tronci, Stefania

N1 - Conference code: 26

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - BSM1

KW - Dynamic Matrix Control

KW - operational cost

KW - Process identification

UR - http://www.scopus.com/inward/record.url?scp=84994247005&partnerID=8YFLogxK

U2 - 10.1016/B978-0-444-63428-3.50315-5

DO - 10.1016/B978-0-444-63428-3.50315-5

M3 - Chapter

AN - SCOPUS:84994247005

SN - 9780444634283

VL - 38

T3 - Computer Aided Chemical Engineering

SP - 1863

EP - 1868

BT - 26 European Symposium on Computer Aided Process Engineering, 2016

PB - Elsevier

T2 - European Symposium on Computer Aided Process Engineering

Y2 - 12 June 2016 through 15 June 2016

ER -

Improving the wastewater treatment plant performance through model predictive control strategies

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this