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208
Innovations in Wastewater Treatment – Harnessing Mathematical
Modeling and Computer Simulations with Cutting-Edge
Technologies and Advanced Control Systems
Jakub Drewnowski1, Bartosz Szeląg2, Fabrizio Sabba3, Magdalena Piłat-Rożek4,
Adam Piotrowicz5*, Grzegorz Łagód6
1 Department of Sanitary Engineering, Faculty of Civil and Environmental Engineering, Gdansk University of
Technology, ul. G. Narutowicza 11/12, 80-233 Gdańsk, Poland
2 Department of Hydraulics and Sanitary Engineering, Institute of Environmental Engineering, Warsaw
University of Life Sciences – SGGW, ul. Nowoursynowska 159, 02-797 Warsaw, Poland
3 Black & Veatch, 11401 Lamar Ave, Overland Park, KS 66211, USA
4 Department of Applied Mathematics, Faculty of Mathematics and Information Technology, Lublin University
of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland
5 Department of Environmental Protection Engineering, Faculty of Environmental Engineering, Lublin
University of Technology, ul. Nadbystrzycka 40B, 20-618 Lublin, Poland
6 Department of Water Supply and Wastewater Disposal, Faculty of Environmental Engineering, Lublin
University of Technology, ul. Nadbystrzycka 40B, 20-618 Lublin, Poland
* Corresponding author’s email: a.piotrowicz@pollub.pl
ABSTRACT
The wastewater treatment landscape in Central Europe, particularly in Poland, has undergone a profound trans-
formation due to European Union (EU) integration. Fueled by EU funding and rapid technological advance-
ments, wastewater treatment plants (WWTPs) have adopted cutting-edge control methods to adhere to EU Water
Framework Directive mandates. WWTPs contend with complexities such as variable ow rates, temperature
uctuations, and evolving inuent compositions, necessitating advanced control systems and precise sensors to
ensure water quality, enhance energy eciency, and reduce operational costs. Wastewater mathematical model-
ing provides operational exibility, acting as a virtual testing ground for process enhancements and resource
optimization. Real-time sensors play a crucial role in creating these models by continuously monitoring key
parameters and supplying data to predictive models. These models empower real-time decision-making, result-
ing in minimized downtime and reduced expenses, thus promoting the sustainability and eciency of WWTPs
while aligning with resource recovery and environmental stewardship goals. The evolution of WWTPs in Central
Europe is driven by a range of factors. To optimize WWTPs, a multi-criteria approach is presented, integrating
simulation models with data mining methods, while taking into account parameter interactions. This approach
strikes a balance between the volume of data collected and the complexity of statistical analysis, employing
machine learning techniques to cut costs for process optimization. The future of WWTP control systems lies in
“smart process control systems”, which revolve around simulation models driven by real-time data, ultimate-
ly leading to optimal biochemical processes. In conclusion, Central Europe’s wastewater treatment sector has
wholeheartedly embraced advanced control methods and mathematical modeling to comply with EU regulations
and advance sustainability objectives. Real-time monitoring and sophisticated modeling are instrumental in driv-
ing ecient, resource-conscious operations. Challenges remain in terms of data accessibility and cost-eective
online monitoring, especially for smaller WWTPs.
Keywords: wastewater treatment, mathematical modelling, data mining, articial intelligence, multi-criteria ap-
proach, control strategies.
Journal of Ecological Engineering
Received: 2023.09.20
Accepted: 2023.10.20
Published: 2023.11.04
Journal of Ecological Engineering 2023, 24(12), 208–222
hps://doi.org/10.12911/22998993/173076
ISSN 2299–8993, License CC-BY 4.0
209
Journal of Ecological Engineering 2023, 24(12), 208–222
INTRODUCTION
After joining the European Union (EU), Po-
land and several other Central European nations
embarked on a monumental transformation of
their wastewater treatment plants (WWTPs). This
extensive overhaul extended beyond equipment
and device replacement, incorporating cutting-
edge technological solutions that epitomized the
forefront of wastewater treatment innovation.
This ambitious modernization initiative found its
impetus in a dual force – the availability of EU
funding opportunities and the rapid evolution of
pioneering projects and technologies within the
wastewater treatment sector. Consequently, this
catalyzed the adoption of state-of-the-art con-
trol methods in WWTPs, not conned to Poland
but cascading across neighboring nations in the
region. This shift carries immense signicance,
chiey in its imperative to align with and satisfy
the rigorous mandates delineated in the European
Union Water Framework Directive [European
Commission, 2023].
The operation of wastewater treatment plants
inherently unfolds within a complex realm, sus-
ceptible to a myriad of external and internal dis-
ruptions. These disruptions, characterized by
variable ow intensity, temperature uctuations,
dynamic concentration levels, and uctuating in-
uent compositions, underscore the pressing need
for specialized solutions in this domain [Revol-
lar et al., 2017]. Consequently, the adoption of
increasingly sophisticated measurement devices,
complemented by advanced control and automa-
tion systems, has emerged as a pivotal aspect of
modern wastewater treatment processes [Chauhan
et al., 2022]. This step is not solely geared towards
meeting stringent wastewater quality standards; it
is equally driven by the pursuit of heightened en-
ergy eciency and the curtailment of operational
costs. In recent times, the wastewater treatment
landscape has undergone a profound transforma-
tion driven by the visionary principle of “self-suf-
ciency”. This paradigm shift places a strong em-
phasis on extracting biogenic and other valuable
chemical compounds from wastewater [Battista
et al., 2020], transcending the constraints of con-
ventional treatment approaches. It has introduced
the innovative concept of “Water Resource Re-
covery Facilities” [Zhang et al., 2020], redening
the objectives of wastewater treatment to not only
purify water but also recover valuable resources.
At the heart of this transformative journey lies
the growing reliance on wastewater mathematical
modeling. This powerful tool allows wastewater
treatment facilities to operate with unprecedent-
ed precision, optimizing processes, conserving
resources, and achieving sustainable outcomes.
Wastewater mathematical modeling entails the
creation of intricate mathematical representations
of the treatment processes, taking into account the
myriad variables that inuence wastewater com-
position and quality.
The integration of real-time sensing, data anal-
ysis, and dependable online parameter control, all
facilitated by mathematical models, has become
integral to contemporary wastewater treatment
systems. Real-time sensors continuously moni-
tor key parameters such as ow rates, chemical
concentrations, and water quality indicators. This
data is then fed into sophisticated mathematical
models that predict how the treatment processes
will respond to changing conditions. These pre-
dictions empower operators to make informed
decisions in real-time, adjusting treatment param-
eters and chemical dosages as needed to maintain
optimal performance. One of the primary advan-
tages of wastewater mathematical modeling is its
ability to anticipate and mitigate signicant pro-
cess uctuations and malfunctions. By simulating
dierent scenarios and predicting potential is-
sues, operators can proactively address problems
before they escalate, minimizing downtime and
costly repairs. This predictive capability is cru-
cial for ensuring the reliability and eciency of
wastewater treatment plants.
Moreover, wastewater mathematical model-
ing enhances the dexterity of technological op-
erations. It provides a virtual testing ground for
experimenting with process improvements and
optimizing resource utilization. This means that
wastewater treatment plants can continually ne-
tune their operations to achieve the highest levels
of eciency and sustainability. In essence, the
integration of wastewater mathematical modeling
represents a signicant leap forward in the eld of
wastewater treatment. It empowers treatment fa-
cilities to not only meet regulatory requirements
but also extract valuable resources from wastewa-
ter, thereby aligning with the principles of sustain-
ability and self-suciency. As this transformative
paradigm continues to gain momentum, wastewa-
ter mathematical modeling will remain an indis-
pensable tool in shaping the future of wastewa-
ter treatment, ensuring that these systems reach
their full potential in terms of eciency, resource
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Journal of Ecological Engineering 2023, 24(12), 208–222
recovery, and environmental stewardship [Solon
et al., 2017; Sabba et al., 2023]. The evolution of
wastewater treatment practices in Central Euro-
pean countries, subsequent to their EU accession,
reects a dynamic process propelled by a conu-
ence of external and internal factors. The relent-
less pursuit of excellence in wastewater treatment
has triggered a profound shift in focus, center-
ing on sustainability, resource recovery, and op-
erational eciency. This transformative journey
underscores the pivotal role of advanced control
systems, real-time monitoring, and wastewater
mathematical modeling as bedrock elements in
shaping the future of wastewater treatment within
the region. The use of simulation software and
optimization procedures for designing technolog-
ical systems is expected to become increasingly
prevalent in the near future, rendering simple cal-
culation software obsolete. In this paper previous
and current control systems employed in WWTPs
were reviewed, and a multi-criteria approach
of an integrated system for the optimization of
WWTP operation was also presented.
OPTIMIZATION AND CONTROL
METHODS FOR WASTEWATER
TREATMENT PLANTS: INSIGHTS
THROUGH MATHEMATICAL MODELING
AND COMPUTER SIMULATIONS
Continuous progress in the eld of mathemat-
ical modeling of biochemical processes related
to wastewater treatment has led to the develop-
ment of comprehensive activated sludge models
(ASM). Among the ASM models, ASM1 stands
out as the most notable, providing a framework
for understanding the removal of carbon and ni-
trogen compounds in wastewater. Subsequent
models, including updated versions like ASM2
and modications like ASM2d or ASM3, have
been introduced [Henze et al., 2000]. These
models have become instrumental in optimizing
wastewater treatment processes, especially in
the context of Central Europe, and particularly
in Poland, where their adoption has been rapidly
increasing, driven by various objectives outlined
earlier. Both mathematical modeling and comput-
er simulation methods hold immense potential to
make substantial contributions to the design, op-
eration, and optimization of wastewater treatment
processes, ultimately leading to the development
of highly ecient wastewater treatment systems.
However, it’s essential to recognize that the ex-
tent and application of these methods can vary
signicantly based on their intended purposes and
the specic needs of each wastewater treatment
facility [Mąkinia et al., 2002; Brdys et al., 2008;
Piotrowski et al., 2023].
Hauduc et al. [2009] have shed light on the
multifaceted roles that ASM models predomi-
nantly serve. These models are primarily em-
ployed for process optimization (59%), process
design (42%), and forecasting corrective mea-
sures for wastewater treatment processes (21%).
The diverse goals and applications of these mod-
els hinge on the preferences and requirements of
the users. In Europe, these models nd their pri-
mary utility among scientists engaged in research
endeavors aimed at optimizing process eciency
and energy consumption. On the other hand, in
North America, particularly in the United States
and Canada, private companies primarily employ
ASMs for process design [Mazurkiewicz, 2016].
Notably, private enterprises have recently in-
troduced novel models and treatment technologies
with a common overarching objective: enhancing
the sustainability of wastewater treatment pro-
cesses [Sabba et al., 2017; Cerruti et al., 2021].
Concurrently, advancements in information tech-
nology and the rapid enhancement of computa-
tional capabilities within available hardware have
paved the way for the application of more sophis-
ticated computational tools in the optimization of
biochemical processes, particularly those rooted
in activated sludge systems [Drewnowski and
Szeląg, 2020]. This transition is driven not only
by formal and legal requirements, such as the ne-
cessity to conduct simulation studies during the
process design phase employing appropriate data
acquisition and processing methods but also by
economic factors, including the decreasing costs
of hardware and software [Mazurkiewicz, 2016].
Presently, more than three decades after
the groundbreaking publication by Henze et
al. [1987], the ASM1 model continues to stand
as the standard for describing activated sludge
processes [Gernaey et al., 2004; Mąkinia, 2010;
Khalaf et al., 2021]. It serves as a reference point
for a multitude of scientic and practical projects,
often being adapted or extended within commer-
cially available software packages like GPS-X,
WEST, SUMO, BIOWIN, DESASS, AQUASIM,
and SIMBA [Rieger et al., 2013; Kirchem et al.,
2020; Nadeem et al., 2022]. These software tools
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Journal of Ecological Engineering 2023, 24(12), 208–222
are widely used for modeling and simulating the
operations of wastewater treatment plants, with a
particular focus on nitrogen removal [Copp, 2002;
Eldyasti et al., 2011; Cao et al., 2021]. This is fur-
ther evidenced by the extensive adoption of these
models across various platforms [Mąkinia, 2010;
Jafarinejad, 2020], as well as by the research
ndings in Hauduc et al. [2009] and Henze et al.
[2000]. Consequently, for modeling purposes, the
biokinetic ASM1 model [Henze et al., 1987] is
employed in 57% of cases, while ASM2d [Henze
et al., 1999] is utilized in 32% of cases. Recently,
ASM3 [Gujer et al., 1999] has gained comparable
popularity among all stakeholders, including sci-
entists, local authorities, and the commercial in-
dustry. Additionally, ASM2d and TUD [Smolders
et al., 1995], as well as New General [Barker and
Dold, 1997], have garnered widespread adoption,
particularly among governmental organizations,
spanning regions like the United States, Cana-
da, Switzerland, as well as several EU countries
such as the Netherlands, Germany, and Belgium.
These models collectively represent a robust tool-
kit for addressing the complexities and challenges
inherent in wastewater treatment processes, of-
fering valuable insights and solutions for a more
sustainable and ecient future in the wastewater
treatment sector.
USE OF DATA MINING METHODS
FOR MODELING WASTEWATER
TREATMENT PLANTS
In addition to the mechanistic models dis-
cussed earlier, statistical models employing data
mining methods can also be utilized for modeling
wastewater treatment processes. In this approach,
models are constructed based on long-term mea-
surement series that encompass the quality of
wastewater at the outlet, the quality of waste-
water at the inlet, and operational parameters of
the bioreactor. Data mining represents a set of
data analysis techniques aimed at extracting and
organizing knowledge from raw data. It encom-
passes various computational methods, including
the computation of descriptive statistics, explo-
ration of multivariate data, and the use of linear
models such as time series analysis. Addition-
ally, data mining involves data visualization tech-
niques, articial intelligence, and machine learn-
ing models [Gorunescu, 2011; Scott-Fordsmand
and Amorim, 2023]. The process of knowledge
discovery in databases can be outlined as follows:
(1) selection of the dataset to be analyzed, which
may involve working with a subset of raw data;
(2) dataset preparation, including data cleaning
and addressing missing data through imputation;
(3) dimensionality reduction and data transfor-
mation; (4) splitting of the data to learning, test
and optionally validation sets; (5) application of
the chosen data mining technique; (6) interpreta-
tion and evaluation of the correctness of the re-
sults obtained, with the possibility of revisiting
earlier steps; (7) implementation of the acquired
knowledge. The individual steps of the process
are shown in Figure 1 [Fayyad et al., 1996; Mi-
raftabzadeh et al., 2023].
Articial intelligence plays a role in devel-
oping systems designed to achieve intelligence
equal to or even surpassing that of humans. Con-
sequently, AI, by certain denitions, is associated
with the notion of behaving or thinking rationally
to mitigate human systematic errors [Russell and
Norvig, 2010]. This is further reinforced by the
fact that machine learning is employed to create
precise regression or classication models, posi-
tioning this scientic domain as an integral com-
ponent of articial intelligence. The fundamental
categorization of machine learning models falls
into two main groups: supervised and unsuper-
vised models. In unsupervised learning, the de-
pendent variables play no role in constructing the
model, while supervised learning involves the
inclusion of output variables within the model
domain. In supervised learning methods, the pro-
cess of model creation occurs in two stages: in
the initial stage, the model’s parameters are esti-
mated using the learning dataset, and in the sub-
sequent stage, the model is tested using the test
dataset [Hastie et al., 2009]. This latter stage is
crucial for assessing the predictive capabilities
of the mathematical model under development.
In more contemporary learning scenarios, addi-
tional model types have emerged, including semi-
supervised learning. In semi-supervised learning,
the learning process involves observations with
known information about the dependent vari-
able alongside those for which this information is
missing, with predictions made during the learn-
ing process [Mohri et al., 2018]. A diverse range
of machine learning methods has been employed
for modeling wastewater treatment plants. These
methods encompass multiple regression and its
variations, such as MARS (Multivariate Adap-
tive Regression Splines), neural networks and
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Journal of Ecological Engineering 2023, 24(12), 208–222
their adaptations, fuzzy models, and regression
tree methods and their enhancements [Güçlü and
Dursun, 2010; Abba and Elkiran, 2017; Santín et
al., 2018]. In the realm of regression tree models,
improvements have been achieved through the
introduction of methods such as random forests
or gradient boosting. These modications have
signicantly enhanced the predictive capabilities
of the regression tree model [Zhou et al., 2019;
Wang et al., 2022; Wodecka et al., 2022]. The ran-
dom forest model has also found utility in classi-
cation tasks, demonstrating nearly awless accu-
racy when categorizing observations into the rele-
vant stages of wastewater treatment [Piłat-Rożek
et al., 2023]. An illustrative instance is provided
by the publication of Szeląg et al. [2020], which
serves as an exemplar of constructing a sludge
bulking simulation model within WWTP using
a range of machine learning models, including
random forest, boosted trees, support vector ma-
chine, multilayer perceptron neural networks, and
logistic regression.
A critical phase in the development of a statis-
tical model for modeling processes within waste-
water treatment plants is the performance of sen-
sitivity analysis and simulation analyses. These
analyses aim to evaluate the impact of alterations
in the numerical values of input data on simulation
outcomes. This aspect is of utmost importance as
the developed model must accurately reect the
inuence of selected independent variables and
bioreactor operational parameters on the quality of
sewage at the wastewater treatment plant’s inow.
ADVANCEMENTS IN WWTP CONTROL
SYSTEMS AND DEVELOPMENT OF
MATHEMATICAL MODELS BASED ON
EMERGING TREATMENT TECHNOLOGIES
In the 21st century, mathematical models have
continued to evolve, with the introduction of inno-
vative tools such as ASDM and Mantis [Elawwad
et al., 2019; Moragaspitiya et al., 2019; Mu’azu et
al., 2020]. Initially, these models found primary
usage in governmental organizations and private
companies, rather than scientic research units.
However, recent years have witnessed an expan-
sion of research eorts focused on discovering
novel methods for nitrogen removal from waste-
water. This research aims to reduce treatment costs
and has led to the modication of standard ASM
models into more intricate frameworks that in-
corporate anammox bacteria and other emerging
nitrogen metabolisms, exemplied by Mantis2
[Faris et al., 2022; Mehrani et al., 2022a; Pryce et
al., 2022]. These methodologies are rooted in the
partial nitrication (nitritation) and anammox pro-
cesses depicted in Figure 2 [Sobotka et al., 2018;
Drewnowski et al., 2021]. Notably, while the
Figure 1. Individual steps comprising the process of knowledge discovery in databases
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Journal of Ecological Engineering 2023, 24(12), 208–222
anammox process is commonly found in natural
environments such as oceans [Arrigo, 2005], it was
only discovered in the late 1990s [Jetten, 1998;
Strous, 1999a; Strous, 1999b]. Most of the previ-
ous studies mainly focused on ammonia-oxidizing
microorganisms, while ignoring the important role
of nitrite-oxidizing microbes (Meng et al., 2017;
Gao et al., 2018; Zhang et al., 2018). Results of
recent studies show that in addition to ammonia
oxidation activity, activated sludge exhibits strong
nitrite oxidation activity that has to be taken into
consideration (Lu et al., 2021). In wastewater treat-
ment systems involving anammox, suppression of
the growth of nitrite-oxidizing bacteria (NOB) is
one of the most important determinants of highly
ecient nitrogen removal [Lotti et al., 2014]. Co-
occurrence of NOB oxidizing NO2
- to NO3
- under
aerobic conditions with anammox bacteria may
lead to rapid consumption of nitrite by NOB. As
a result, due to an insucient supply of NO2
-, the
growth of anammox bacteria will be restricted [Ma
et al., 2015]. This becomes a serious problem be-
cause controlling the growth of NOB is not an easy
task, especially during simultaneous nitritation and
anammox processes [De Clippeleir et al., 2011;
De Clippeleir et al., 2013].
The concept of the nitrite (NO2
-) shunt is
rooted in inhibiting the nitrication process at the
NO2
- stage by suppressing the growth of bacte-
ria responsible for oxidizing NO2
- to NO3
- (i.e.
NOB) [Cerruti et al., 2021]. This strategy aims
to reduce costs associated with aeration during
the nitrication process and the expense of add-
ing organic carbon during denitrication carried
out by ordinary heterotrophic organisms (OHO).
By converting ammonia nitrogen into NO2
-, it
reduces oxygen demand by approximately 25%,
and the conversion of NO2
- to nitrogen gas (N2)
decreases the demand for organic carbon by about
40% [Roots et al., 2020].
The Anammox process (Anaerobic Ammoni-
um Oxidation) involves the removal of nitrogen
compounds from wastewater using autotrophic
microorganisms known as Planctomycetales.
These bacteria, known as anaerobic ammonia-ox-
idizing bacteria (AnAOB), convert ammonia and
NO2
- (in a ratio of 1:1.3) into N2 (approximately
90%) and NO3
- (around 10%) without requiring
an external source of organic carbon. Conse-
quently, the anammox process proves especially
useful for nitrogen removal from wastewater with
a low BOD5:N ratio, which often arises in water
from sludge dewatering processes after anaerobic
digestion [Kaewyai et al., 2022]. Deammonica-
tion combines nitritation and anammox and can
be executed as a single-step process in SBRs (e.g.,
the DEMON process) [Wett, 2007; Podmirseg et
al., 2022] or in hybrid systems (e.g., AnitATM Mox
process) [Christensson et al., 2011; González-
Martínez et al., 2021]. As mentioned above, de-
ammonication oers signicant advantages,
including reduced electrical energy consumption
for aeration (by approximately 60%), decreased
excess sludge production (by about 90%), elimi-
nation of the organic carbon requirement, and
substantial reduction in CO2 emissions into the at-
mosphere (by over 90%) [Al-Hazmi et al., 2021].
Another recently discovered process found in the
Figure 2. The nitrite (NO2
-) shunt and anammox as well as comammox
processes in relation to the conventional nitrication/denitrication
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Journal of Ecological Engineering 2023, 24(12), 208–222
nitrogen cycle is known as Comammox, which
stands for Complete Ammonia Oxidation. The
main idea behind the process is the conversion of
ammonia to NO3
- (traditionally carried out in two
stages) by a single group of microorganisms re-
ferred to as complete ammonia-oxidizing bacteria
(CAOB). In 2015, microorganisms of the genus
Nitrospira were found to have the capacity for
such conversion, and shortly thereafter a species
of Nitrospira inopinata was isolated in pure cul-
ture. It is anticipated that complete nitriers could
prove very useful in engineering systems, such as
wastewater treatment plants, creating new op-
portunities for nitrogen removal from wastewater
[Maddela et al., 2022]. The occurrence of Nitro-
spira inopinata has been conrmed in activated
sludge reactors, moving-bed biolm reactors, hy-
brid biolms or side stream wastewater [Lu et al.,
2020]. In articial systems, comammox bacteria
coexist together with other microorganisms. Wu
et al. [2019] studied the possibility of removing
ammonia nitrogen from sludge digester liquor as
a result of the simultaneous partial-nitrication,
anammox and comammox processes obtained in
an SBR reactor. The solution turned out to be not
only technologically eective (more than 98% re-
moval ecacy) but also economically ecient.
Another aspect of the issue was pointed out in
a study conducted by Kits et. al [2019]. It was
shown that during nitrication, complete nitriers
can produce less N2O (which is a greenhouse gas)
compared to ammonia-oxidizing bacteria (AOB)
responsible for nitritation. Comammox is also be-
ginning to be reected in modeling studies. Meh-
rani et al. [2022b] using data from the nitrica-
tion process in SBRs expanded the ASM1 model
matrix to include, among other things, two-step
nitrication (based on Mantis2) and also the co-
mammox, which enabled a better representation
of the processes taking place.
Increasingly, mathematical computer-based
models are being utilized for wastewater treat-
ment processes to predict various technological
options, facilitating the identication of optimal
solutions such as the aforementioned NO2
- shunt
method and reductions in aeration costs. Aeration
constitutes the most energy-intensive process in
wastewater treatment plants [Gu et al., 2023], of-
ten exceeding 50% of total energy consumption
[Drewnowski et al., 2019]. Most current aeration
systems rely on measuring oxygen concentrations
in the nitrication tank for control. While this ap-
proach eectively maintains a consistent oxygen
concentration for adequate wastewater treatment,
it tends to be inecient in terms of energy, espe-
cially when inuent pollutant loads uctuate sig-
nicantly. This situation calls for the adoption of
more advanced aeration control processes based
on online measurements of nitrogen compounds
[Åmand et al., 2013]. This has become feasible
with the development of more reliable ammonia
(NH4
+) and nitrite/nitrate (NOx) probes [Åmand
et al., 2013]. In the Ammonia-Based Aeration
Control (ABAC) system, the regulation of oxy-
gen concentration is rooted in NH4
+ concentration
measurements. ABAC oers two control methods
based on the location of NH4
+ concentration mea-
surement: feedback control, when measured at the
nitrication tank outlet, and feedforward control,
at the inlet of the nitrication tank. ABAC-based
regulation results in signicant energy savings
(approximately 10-20%) and improved denitri-
cation with reduced consumption of alkalinity
and organic carbon. While feedforward control
is more complex, it still ensures compliance with
wastewater quality standards with lower energy
consumption. The Ammonia vs. Nitrate/Nitrite
Control (AVN) system was initially developed
for treatments involving shortened nitrication
to eliminate the second nitrication step (oxida-
tion of NO2
- to NO3
-) [Al-Omari et al., 2015].
Additionally, the application of AVN controls
may enhance nitrogen removal eciency in con-
ventional nitrication-denitrication processes
[Regmi et al., 2022]. Adjustment of the NO3
- load
for denitrication is achieved by conguring spe-
cic concentrations and NH4
+ to NOx ratios at the
outlet [Mehrani et al., 2022b].
Electronic nose systems, due to the gas sen-
sors employed in them, are used to analyze and
classify gas mixtures and, in particular, distin-
guish between components present in a given
mixture [Piłat-Rożek et al., 2023]. Multivariate
data from the sensors also allow prediction of
parameters related to wastewater quality such as
COD, ammonia nitrogen (AN), TN and TP [Wang
et al., 2023] or other environmental parameters
associated with air and odor pollution [Guz et al.,
2015]. Monitoring of wastewater treatment plants
is one of the future applications of e-noses as
they can be used to classify samples from dier-
ent stages of treatment [Piłat-Rożek et al., 2023]
and identify sources or assess concentration of
the odors [Giuliani et al., 2012]. Since gas sen-
sor arrays enable the dierentiation of contami-
nants, electronic noses can be employed to detect
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Journal of Ecological Engineering 2023, 24(12), 208–222
unusual situations in wastewater treatment plants
that may lead to failures [Bourgeois et al., 2003].
These anomalies can be distinguished from the
normal operation of wastewater treatment plants
using supervised learning algorithms. Described
system can also be applied in fast and cheap es-
timation of treated wastewater and used for man-
aging and control processes occurring in WWTP
devices [Guz et al., 2015; Łagód et al., 2022].
The ongoing developments in mathematical
modeling and its application in wastewater treat-
ment have led to groundbreaking advancements.
Emerging methodologies like the NO2
- shunt and
the utilization of advanced control systems have
the potential to revolutionize wastewater treat-
ment processes, making them more ecient,
cost-eective, and environmentally friendly.
These innovations mark a signicant step toward
achieving sustainability in wastewater treatment,
addressing both economic and environmen-
tal concerns. As research continues to push the
boundaries of what is possible in this eld, the
future of wastewater treatment holds promise for
more ecient and sustainable practices.
BALANCING ECONOMIC AND
ECOLOGICAL ASPECTS: A MULTI-
CRITERIA APPROACH TO OPTIMIZE
WASTEWATER TREATMENT
PLANT OPERATIONS
The future of control systems in WWTPs is
moving towards what is commonly referred to as
“smart process control systems”. These systems
will primarily feature a simulation model imple-
mented within computer software, complete with
suitable algorithms for controlling biochemical
processes. The success of these systems relies on
real-time measurements taken at selected points
within a bioreactor. Unfortunately, this approach,
which falls under the country’s priorities, does
not adequately address environmental concerns
related to greenhouse gas emissions reduction.
WWTPs have been identied as contributors to
greenhouse gas emissions [Daelman et al., 2013;
Zaborowska et al., 2019; Szeląg et al., 2023], es-
pecially during biological nitrogen removal pro-
cesses [Sabba et al., 2018]. Furthermore, critical
aspects such as optimizing the selection of sam-
ple collection points within the WWTP, acquir-
ing measurement data for model calibration, and
considering the interactions between calibrated
kinetic parameters have often been overlooked
[Andraka et al., 2018]. The uncertainties associ-
ated with identied WWTP model parameters are
not factored in during system construction, which
can aect settings and simulation outcomes
[Szeląg et al., 2022]. The procedures devised for
calibrating and optimizing model parameters often
involve iteratively adjusting parameter values un-
til a strong correlation between calculated results
and measurements is achieved [Mąkinia and Za-
borowska, 2020]. However, this correlation does
not always guarantee satisfactory results. Despite
the signicant inuence of wastewater quality at
the inlet on the chosen optimization strategies,
there have been no eorts to establish a method-
ology for optimizing wastewater quality predic-
tion, accounting for the duration of the conducted
studies. Consequently, the development and im-
plementation of these systems require multi-year
and costly investigations, limiting their practical-
ity [Barbusiński et al., 2020; Szeląg et al., 2020].
In Figure 3, these aforementioned limitations are
addressed and a methodology for designing an in-
tegrated system to optimize WWTP operations is
presented [Drewnowski and Szeląg, 2020].
In the adopted approach, the assessment of
WWTP operation is based on parameters such as
wastewater quality at the outlet, energy consump-
tion, and greenhouse gas emissions. Simulation
of these variables is performed using a mecha-
nistic model, specically ASM. To determine the
amount of measurement data and the number of
experiments needed for model calibration, the de-
sign of experiments (DOE) method is employed
[Barbusiński et al., 2021]. This method takes into
consideration the number of parameters (both
kinetic and stoichiometric) to be calibrated. To
optimize the selection of parameters for analysis,
a global sensitivity analysis is conducted. This
helps identify and exclude kinetic and stoichio-
metric parameters that have a negligible impact
on simulation results. Following this multi-cri-
teria approach and employing the DOE method,
data are generated and optimized for use in a sta-
tistical model that predicts inuent wastewater
quality. This is a critical step as online wastewater
quality measurements are often costly and can be
a limiting factor in long-term WWTP optimiza-
tions [Borzooei et al., 2019; Newhart et al., 2019].
The adopted solution allows to nd a bal-
ance between the amount of measurement data
and the complexity of the statistical method used
216
Journal of Ecological Engineering 2023, 24(12), 208–222
to predict wastewater quality. Machine learning
methods, including neural networks and their
various adaptations, are utilized to achieve this,
reducing measurement time and equipment op-
erating costs. The presented methodology also
addresses uncertainty analysis using the GLUE
method during model calibration [Mannina et
al., 2010; Szeląg et al., 2022]. This uncertainty
pertains to the interactions among identied pa-
rameters and their impact on simulation results.
Furthermore, this approach enables the explo-
ration and analysis of the inuence of various
simplications of wastewater quality indicators,
with decisions based on the credibility of the
obtained results. When the uncertainty is within
permissible limits set by a technologist, simpli-
ed testing approaches can be considered. If not,
additional tests are required. The analyses result
in a model with the assumed accuracy, deter-
mined by multidimensional distributions of ki-
netic parameters using the GLUE method. There-
fore, by applying the developed statistical mod-
els for predicting inuent wastewater quality, the
calibrated WWTP model can estimate wastewa-
ter quality, energy consumption, and greenhouse
gas emissions using the GLUE method, consid-
ering the interactions among calibrated param-
eters. The obtained results can exhibit variabil-
ity. To account for this variability, stoichiometric
concepts are integrated into bioreactor settings
Figure 3. Multi-criteria concept of an integrated system for optimization of WWTP operation
217
Journal of Ecological Engineering 2023, 24(12), 208–222
during WWTP optimization. These settings are
designed to ensure that the desired technologi-
cal outcomes are achieved at the lowest possi-
ble cost. This holistic and innovative approach
promises to greatly transform WWTP operations,
aligning them with sustainability goals and ad-
dressing the environmental challenges posed by
greenhouse gas emissions.
CONCLUSIONS
The comparison of dierent WWTP opti-
mization systems conrms that there is a clear
focus on improving wastewater quality and cut-
ting energy use. Despite advanced computational
methods, Poland and Central Europe still prefer
simpler tools like spreadsheets and control soft-
ware. Simulation, integrated software, and AI
are underused in practical wastewater treatment,
primarily serving research rather than operations.
One signicant obstacle to using computer mod-
els for WWTPs is the lack of comparative or op-
erational data needed for model calibration, vali-
dation, sensitivity assessment, and qualitative
analysis. Gathering this data is time-consuming
and costly. Nevertheless, computer models are
increasingly used to predict various technologi-
cal approaches and identify optimal solutions.
Recent studies show the adoption of technolo-
gies like NO2
- shunt and anammox processes in
WWTPs, especially for the treatment of waste-
water with high nitrogen content.
Considering the growing complexity of
NO2
- shunt compared to traditional processes,
mathematical models and computer simula-
tions for WWTP control are expected to become
more common. Thus, besides reviewing current
WWTP control systems, this paper presents a
multi-criteria approach to an integrated system
for WWTP optimization. The application of sim-
ulation software and optimization procedures is
likely to phase out simpler calculation software
in medium and large WWTPs. These systems
will involve complex integrated expert systems,
real-time data collection and processing, and
simultaneous optimization. In contrast, small
WWTPs will adopt these changes more slowly,
continuing to rely on conventional processes and
traditional control software. Data processing will
also remain oine due to the high cost of mea-
surement devices, making investments in online
systems unfeasible.
Acknowledgments
This work was nancially supported by the
National Science Centre as a result of the research
project no. 2017/26/D/ST8/00967 and by the Polish
Ministry of Education and Science under grant
no. FD-20/IŚ-6/029.
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