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Energy optimization of a wastewater treatment plant based on energy audit data: small investment with high return

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Abstract

Ambitious energy targets in the 2020 European climate and energy package have encouraged many stakeholders to explore and implement measures improving the energy efficiency of water and wastewater treatment facilities. Model-based process optimization can improve the energy efficiency of wastewater treatment plants (WWTP) with modest investment and a short payback period. However, such methods are not widely practiced due to the labor-intensive workload required for monitoring and data collection processes. This study offers a multi-step simulation-based methodology to evaluate and optimize the energy consumption of the largest Italian WWTP using limited, preliminary energy audit data. An integrated modeling platform linking wastewater treatment processes, energy demand, and production sub-models is developed. The model is calibrated using a stepwise procedure based on available data. Further, a scenario-based optimization approach is proposed to obtain the non-dominated and optimized performance of the WWTP. The results confirmed that up to 5000 MWh annual energy saving in addition to improved effluent quality could be achieved in the studied case through operational changes only.

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Abbreviations

ASM:

Activated sludge model

bA :

Autotrophic decay rate

BME:

Combined blower and motor efficiency

BNRAS:

Biological nutrient removal activated sludge

BOD5 :

5-Day biochemical oxygen demand

BSM1:

Benchmark simulation model no 1

Cc:

Clarification coefficient

COD:

Chemical oxygen demand

CODs :

Soluble chemical oxygen demand

CODt :

Total chemical oxygen demand

C p :

Heat capacity of air at constant pressure

CSTR:

Completely stirred tank reactor

d a :

Airflow per diffuser

d d :

Diffuser submergence depth

d de :

Diffuser density

DO:

Dissolved oxygen concentration

e :

Combined blower and motor efficiency

E Ca :

Aeration energy consumption

E Cm :

Mixing energy consumption

E Cp :

Pumping energy consumption

E Ct :

Total energy consumption

E Pw :

Total energy produced from WAS

EQI:

Effluent Quality Index

F c :

Correction factor

F f :

Fouling factor

GHG:

Greenhouse gas

HC-D:

High-load condition in dry-weather operational mode

HC-W:

High-load condition in wet-weather operational mode

H d :

Dynamic head

HRT:

Hydraulic retention time

H s :

Pumping head

H st :

Static head

I c :

Current absorption

IMLR:

Internal mixed liquor recycle

K :

Dynamic head-loss coefficient

K c :

Proportional gain

K OA :

Oxygen half-saturation index for autotrophic biomass

MLE:

Modified Ludzack-Ettinger

MLSS:

Mixed liquor suspended solids

NC-D:

Normal condition in dry-weather operational mode

OTE:

Oxygen Transfer Efficiency

PAC:

Performance assessment criterion

P D :

Delivered power blower

P e :

Pump efficiency

P FL :

Pipe friction loss

PI:

Proportional Integral

P PUV :

Power per unit volume of mixing

PS:

Primary sludge

P s :

Barometric pressure

Q :

Pumping flow rate

Q IMLR :

Internal mixed liquor recirculation flowrate

Q N :

Normalized air flux

Q RAS :

Return activated sludge flowrate

R :

Universal gas constant

RAS:

Return activated sludge

RWS:

Reject water from sludge treatment units

SCADA:

Supervisory control and data acquisition

SOTE:

Standard oxygen transfer efficiency

SRT:

Solids retention time

STOWA:

Acronym for the foundation for applied water research in Netherlands

SVI:

Sludge volume Index

T a :

Blower inlet air temperature

T i :

Integral time

TKN:

Total Kjeldahl nitrogen

TN:

Total nitrogen

TP:

Total phosphorous

TSS:

Total suspended solid

VS:

Volatile solids

VSS:

Volatile suspended solids

w :

Mass of the airflow

WAS:

Wasted activated sludge

WWTP:

Wastewater treatment plant

α:

The ratio of process water to clean water mass transfer coefficients

ΔPd :

The pressure drop of the piping and diffuser downstream of the blower

μA :

The maximum specific growth rate for autotrophic biomass

φ :

Power factor

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Acknowledgments

This research was financially supported by Società Metropolitana Acque Torino (SMAT). The authors wish to thank SMAT managing, laboratory, maintenance, and operation personnel for their engagement and cooperation during the sampling campaigns of this project.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 10129, Torino, Italy

    Sina Borzooei, Deborah Panepinto & Maria Chiara Zanetti

  2. Department of Data Analysis and Mathematical Modelling, BIOMATH, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000, Ghent, Belgium

    Youri Amerlinck & Ingmar Nopens

  3. Warwick Water Research Group, School of Engineering, The University of Warwick, Coventry, CV4 7AL, UK

    Soroush Abolfathi

  4. SMAT (Società Metropolitana Acque Torino) Research Center, Corso Unità d’Italia 235/3, 10127, Torino, Italy

    Gerardo Scibilia & Lorenza Meucci

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  1. Sina Borzooei
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Correspondence to Sina Borzooei.

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Borzooei, S., Amerlinck, Y., Panepinto, D. et al. Energy optimization of a wastewater treatment plant based on energy audit data: small investment with high return. Environ Sci Pollut Res 27, 17972–17985 (2020). https://doi.org/10.1007/s11356-020-08277-3

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