Journal of Water Process Engineering 63 (2024) 105486

Contents lists available at ScienceDirect

Journal of Water Process Engineering

journal homepage: www.elsevier.com/locate/jwpe

Revolutionizing wastewater treatment toward circular economy and carbon

neutrality goals: Pioneering sustainable and efficient solutions for
automation and advanced process control with smart and
cutting-edge technologies
Stefano Cairone a, Shadi W. Hasan b, Kwang-Ho Choo c, Demetris F. Lekkas d, Luca Fortunato e, f,
Antonis A. Zorpas g, Gregory Korshin h, Tiziano Zarra a, Vincenzo Belgiorno a,
Vincenzo Naddeo a, *
a
Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II #132, 84084 Fisciano, SA, Italy
b
Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical and Environmental Engineering, Khalifa University of Science and
Technology, 127788, Abu Dhabi, United Arab Emirates
c
Department of Environmental Engineering, Kyungpook National University (KNU), 80 Daehak-ro, Bukgu, Daegu 41566, Republic of Korea
d
Waste Management Laboratory, Department of the Environment, University of the Aegean, 81100 Mytilene, Greece
e
Water Desalination and Reuse Center (WDRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
f
MANN+HUMMEL Water & Fluid Solutions S.p.A., Italy
g
Laboratory of Chemical Engineering and Engineering Sustainability, Faculty of Pure and Applied Sciences, Open University of Cyprus, Giannou Kranidioti 89, Latsia,
2231 Nicosia, Cyprus
h
Department of Civil and Environmental Engineering, University of Washington, 352700, Seattle, WA 98105-2700, United States

A R T I C L E I N F O A B S T R A C T

Editor: Wenshan Guo Wastewater treatment plants (WWTPs) play a crucial role in ensuring a safe environment by effectively removing
contaminants and minimizing pollutant discharges. Compliance with stringent regulations and the search for
Keywords: sustainable treatment processes pose new challenges and provide opportunities for innovative solutions. These
Wastewater treatment automation solutions include using wastewater as a resource to recover value-added by-products, such as clean water,
Digital water
renewable energy, and nutrients, while optimizing energy consumption and reducing operating costs without
Advanced control
compromising treatment performance. To drive continuous innovation in wastewater treatment, the integration
Process optimization
Wastewater data analytics of advanced treatment technologies with robust monitoring and control systems is imperative.
This review explores advancements in automation and advanced process control within WWTPs. In this
context, technologies such as Internet of Things (IoT), cloud computing, big data analytics, artificial intelligence

Abbreviations: ADE, Anaerobic digestion enhancement; ADM1, Anaerobic digestion model no.1; AI, Artificial intelligence; ANFIS, Adaptive neuro-fuzzy inference
system; AnMBR, Anaerobic membrane bioreactor; ANN, Artificial neural network; AR, Augmented reality; ASMs, Activated sludge models; ASP, Activated sludge
process; BOD, Biochemical oxygen demand; CC, Cloud computing; CECs, Contaminants of emerging concern; CI, Communication infrastructure; CMS, Central
monitoring station; COD, Chemical oxygen demand; DBPs, Disinfection by-products; DKELM, Dynamic kernel-based extreme learning machine; DL, Deep learning;
DNN, Deep neural network; DOC, Dissolved organic carbon; DT, Digital twin; EO, Electro-oxidation; F/M, Food to microorganism ratio; FCM, Fuzzy c-means; FF,
Feedforward; FI, Field instrumentation; FO, Forward osmosis; GA, Genetic algorithm; GHG, Greenhouse gas; HMI, Human-machine interface; HRT, Hydraulic
retention time; IOMS, Instrumental odor emissions system; IoT, Internet of Things; IT, Information technology; IWA, International Water Association; LCA, Life cycle
assessment; LSTM, Long short-term memory; MBBR, Moving-bed biofilm reactor; MBR, Membrane bioreactor; MEC-AD, Microbial electrolysis cell-assisted anaerobic
digestion; ML, Machine learning; MLP, Multi-layer perceptron; MOSC, Multi-objective supervisory control; MR, Mixed reality; NRM, Nonlinear regression model;
NSGA-II, Non-dominated sorting genetic algorithm II; PLCs, Programmable logic controllers; PSO, Particle swarm optimization; PT, Physical twin; RF, Random forest;
RRF, Resource recovery facility; RSM, Response surface methodology; RTUs, Remote terminal unit; SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2;
SBR, Sequential batch reactor; SRT, Sludge retention time; SVI, Sludge volume index; SCADA, Supervisory control and data acquisition; SDG, Sustainable devel­
opment goal; SiOMS, Smart instrumental odor monitoring station; SVM, Support vector machine; TMP, Transmembrane pressure; TN, Total nitrogen; TP, Total
phosphorus; TSS, Total suspended solids; VR, Virtual reality; VSL, Variation sliding layer; WBE, Wastewater-based epidemiology; WWTP, Wastewater treatment
plant.
* Corresponding author.
E-mail address: vnaddeo@unisa.it (V. Naddeo).

https://doi.org/10.1016/j.jwpe.2024.105486
Received 3 March 2024; Received in revised form 24 April 2024; Accepted 12 May 2024
Available online 30 May 2024
2214-7144/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
S. Cairone et al. Journal of Water Process Engineering 63 (2024) 105486

(AI), blockchain, robotics, drones, virtual/augmented reality (VR/AR), and digital twin are identified as prom­
ising tools for developing innovative, smart, and efficient monitoring and control systems. While the integration
of these tools offers many benefits, further research is essential to optimize their performance and cost-
effectiveness. A detailed overview of the current and future applications of these advanced tools and smart
systems is provided, emphasizing their strengths, limitations, and opportunities for future research and
improvements.

1. Introduction underutilization is mainly due to the lack of reliable analytical tools and
gaps in operators' knowledge, which hinder effective and rapid data
Disposal of inadequately treated wastewater into the environment processing, analysis, and interpretation. Therefore, these aspects need
poses substantial risks to public health and ecosystem quality [1–3]. improvement to obtain “data mines” from which valuable information
WWTPs face the challenge of producing treated effluent that complies can be extracted [4].
with regulatory standards despite the variable characteristics of influent A proper real-time and data-based monitoring system enables rapid
wastewater, which is subject to hourly, daily, and seasonal fluctuations detection of process changes and anomalies. However, the fluctuations
in quantity and quality. Moreover, WWTPs must adapt to the evolving in quantity and composition of influent wastewater, coupled with the
challenges in wastewater treatment, including the removal of CECs and complex nonlinear relationships among factors affecting treatment
the reduction of GHG emissions [4]. In recent years, the paradigm is processes, pose significant challenges to effective process control [12].
shifting from the conventional concept of wastewater treatment plant to To address this challenge, effective modeling and simulation of pro­
the sustainable and circular economy-based approach of RRF (Fig. 1). cesses are crucial. Among conventional methods, ASMs, established
This approach aims not only to effectively remove contaminants from since the 1980s, are widely applied to simulate wastewater treatment
wastewater produced by domestic, industrial, and agricultural activities processes. These deterministic models, although valuable, are limited by
but also to recover valuable products such as clean water, energy, and various assumptions and simplifications, hindering their applicability to
nutrients [5,6]. To achieve these goals, process optimization plays a key any conditions (e.g., they are valid only for specific temperature and pH
role, requiring attention not only during the design phase but also ranges) and do not fully address the high variability and complexity of
throughout the operational and management phases by defining the processes [13]. In recent years, advances in data analysis and AI
appropriate control strategies [7]. have led to the development and widespread adoption of ML models as a
The fourth industrial revolution, often referred to as “Industry 4.0”, viable alternative to conventional deterministic models. ML models,
aims to optimize and automate industrial processes through digital based on algorithms that can derive nonlinear relationships between
technologies such as the IoT, AI, and big data [8]. Industry 4.0 has the input and output variables, have shown promise in effectively simu­
potential to drive technological innovation and improve the sustain­ lating and predicting time series, overcoming the limitations of tradi­
ability of industrial sectors in the social, economic, and environmental tional models [14,15]. Integrating AI techniques into wastewater
spheres [9]. In the context of wastewater treatment, Industry 4.0 in­ treatment modeling offers an opportunity to effectively manage the
volves the application of technologies that can accelerate technological complexity and variability of processes using models that do not
development, promote green transition, and drive digital trans­ explicitly take into account complex physical processes and reaction
formation. In this context, modern WWTPs must pursue a multitude of mechanisms [16,17].
objectives, including real-time monitoring, predictive maintenance, Harnessing real-time data analysis through appropriate data-driven
process optimization, energy efficiency, and resource recovery. Overall, tools offers rapid and effective decision support means, enabling
Industry 4.0 can enhance the efficiency and sustainability of WWTPs, timely detection and response to inefficiencies, anomalies, and failures
resulting in better resource utilization (e.g., energy, chemicals, and in the wastewater treatment process. This proactive approach can result
materials), reduced operating costs, extended equipment lifespan, and in reduced downtime, fewer effluent discharge violations, lower
minimized expenditures [8]. In this pursuit, a growing emphasis is resource consumption, and enhanced WWTPs performance [18]. Data-
placed on data-driven monitoring and control systems [10]. driven monitoring can also promote process automation: through spe­
Installing appropriate sensors in treatment units facilitates the cific devices and computer tools, data measured by sensors can be
implementation of online monitoring systems, replacing expensive and automatically transferred and processed to provide inputs to actuators of
time-consuming offline analysis of wastewater parameters [11]. The control systems acting on the process.
increasing availability and practical use of reliable sensors for measuring This review aims to provide a comprehensive overview of the ap­
many parameters in (almost) real-time enable the collection of signifi­ plications of advanced tools and smart systems to support the automa­
cant amounts of data from wastewater treatment processes, offering a tion and control of wastewater treatment processes. Several promising
potential source of valuable information to optimize WWTP perfor­ approaches and cutting-edge technologies presented in recent scientific
mance. However, this potential often remains untapped, with large literature are discussed, highlighting their potential benefits and limi­
amounts of data accumulating in “data graveyards”. This tations. Following a critical analysis of the recent progress in this field,

Fig. 1. Concept of a sustainable Resource Recovery Facility (RRF).

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S. Cairone et al. Journal of Water Process Engineering 63 (2024) 105486

the main challenges are outlined. Finally, an analysis of the environ­

mental, economic, and social perspectives of the approaches discussed is
provided. To the best of the authors' knowledge, this is the first review
that has addressed this topic.

2. Advanced tools to promote automation and control in WWTPs

Effective control and monitoring of treatment processes in WWTPs

are essential for achieving optimal performance and meeting regulatory
standards. In this context, SCADA systems play a key role. SCADA sys­
tems have been in use for several decades, providing continuous moni­
toring and control capabilities, even in the remote mode. These systems
allow for rapid adjustments of operating parameters and proactive in­
terventions in the case of malfunctions [19].
A typical SCADA system comprises several key components [20]:

• HMI: This interface connects operators to the system, enabling them

to monitor and control the process based on the data collected;
• CMS: This component is responsible for acquiring information
collected from RTUs and defining appropriate actions for each
detected event;
• RTUs: These units automatically collect data from connected sensors
and transmit them to the CMS;
• PLCs: These controllers are programmed to interpret and compare
received data with desired values, taking corrective actions via
actuators;
• CI: This component connects RTUs and PLCs to the SCADA system;
• FI: This includes sensors, actuators, and other equipment that
communicate with and are monitored and controlled by the SCADA
system.

The integration of SCADA systems in WWTPs has played a crucial

role in controlling treatment processes, resulting in reduced energy and
chemical consumption, as well as operational and maintenance costs
[21]. Filipe et al. (2019) [22] proposed a predictive model that uses data
collected from the SCADA system installed in the pumping station of a
WWTP to optimize the control of variable-speed pumping units,
reducing energy consumption and operating costs. They achieved
around 17 % reduction in energy consumption and a better control of
the wastewater level in the tanks (the number of exceedances of the alert
level decreased by 97 %). Sean et al. (2020) [23] integrated power
meters and water quality sensors into a SCADA system capable of Fig. 2. Cumulative documents (a) and citations by year (b) corresponding to
the TITLE-ABS-KEY search «(“internet of things” OR “cloud computing” OR
wireless data transmission to an online cloud server, facilitating the
“data analytics” OR “data analysis” OR “artificial intelligence” OR “machine
monitoring and optimization of a WWTP's aeration system. By applying
learning” OR “deep learning” OR “blockchain” OR “robot*” OR “drone” OR
this approach, they estimated an energy-saving rate of about 20 %.
“virtual reality” OR “augmented reality” OR “digital twin”) AND (“wastewater
Further steps in automation and advanced control of wastewater treatment” OR “waste water treatment”)» in the Scopus database.
treatment processes can be realized through the application of cutting-
edge tools such as the IoT, cloud computing, big data analytics, AI,
pronounced in the last few years (Fig. 2b). Analyzing the results of the
blockchain, robotics, drones, VR/AR, and digital twins. These tools,
previously mentioned research indicates that currently the highest
commonly used in various engineering applications, are increasingly
number of studies have focused on IoT, data analytics, and AI modeling,
employed in environmental technologies. Prior studies have demon­
while other technologies such as blockchain, drones, VR/AR, and digital
strated their potential in optimizing wastewater treatment processes,
twins are emerging.
gradually shifting the control of WWTPs from manual to automated and
smart [24,25]. Researchers are increasingly focusing on integrating
cutting-edge technologies into wastewater treatment, as evidenced by 2.1. Internet of things
the substantial number of related papers published over the years.
Currently, there are 3251 documents matching the TITLE-ABS-KEY The IoT refers to a network of interconnected devices capable of
search query string «(“internet of things” OR “cloud computing” OR automatically collecting and wirelessly transferring data measured by
“data analytics” OR “data analysis” OR “artificial intelligence” OR sensors. The integration of IoT in WWTPs aims to enhance data detection
“machine learning” OR “deep learning” OR “blockchain” OR “robot*” and collection, thereby improving the efficiency of the monitoring sys­
OR “drone” OR “virtual reality” OR “augmented reality” OR “digital tem and reducing response time during crises [8]. By integrating IoT into
twin”) AND (“wastewater treatment” OR “waste water treatment”)» in WWTPs, extensive data can be collected, enabling advanced monitoring
the Scopus database, with a notable rise in publications in recent years and automated real-time control of treatment processes. This integration
(Fig. 2a). These documents are gaining increasing impact within the optimizes the quality of treated wastewater, reduces energy consump­
scientific community, as evidenced by the continuous increase in the tion and operational costs, and improves plant management by
number of citations by year in the Scopus database, particularly enhancing equipment operational status supervision, reducing plant

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S. Cairone et al. Journal of Water Process Engineering 63 (2024) 105486

downtime, and optimizing personnel management [26]. present challenges in ensuring adequate control and monitoring activ­
Sensors capable of measuring operating parameters or contaminant ities of WWTPs [41]. Moreover, WWTPs are significant consumers of
concentrations and transmitting data to cloud-based infrastructures can resources and energy [42]. However, taking appropriate action can lead
be successfully used in WWTPs. Kumar and Hong (2022) [27] proposed to substantial operational cost savings, especially in terms of energy and
an IoT system employing a smart sensor to monitor water quality in a personnel [43]. For example, Torregrossa et al. (2019) [44] proposed a
WWTP, resulting in improved quality of treated effluent. Kodali et al. methodology based on the analysis of daily data to identify plant energy
(2019) [28] developed an IoT system using reliable and low-cost inefficiencies, supporting the design and operational stages of WWTPs to
microcontrollers and sensors to monitor relevant parameters (waste­ ensure an efficient energy use. Analyzing historical and real-time mea­
water temperature, flow rate, water level in the tank) at regular intervals surements can provide valuable insights needed to monitor operating
and alert the WWTP operators in critical situations. Khatri et al. (2018) conditions and optimize the performance of WWTPs. However, inade­
[29] devised a cost-effective IoT system to monitor and control pH levels quate raw data processing due to the lack of tools and/or specialized
in real-time in a municipal WWTP, ensuring the suitability of the knowledge among WWTPs staff remain a significant hurdle [18]. In
effluent for reuse in agriculture and gardening. Rishitha and Ullas response to these challenges, several studies have proposed innovative
(2019) [30] implemented an IoT system, using sensors to monitor water approaches to monitor and analyze wastewater treatment processes
level and gas emissions (CO2 and NH3), to promote high treatment ef­ based on big data analytics and cloud computing.
ficiency and low energy consumption while reducing operating and Recent advances have enabled the collection of large volumes of data
maintenance costs. Hasan et al. (2020) [31] used IoT to build a smart through increasingly sophisticated sensors. Big data analytics involves
system for WWTP monitoring, connecting smart sensors capable of the collection, processing, and analysis of large datasets, aiding opera­
measuring various parameters (temperature, pH, turbidity, total dis­ tors in managing these vast amounts of data and gaining a deeper un­
solved solids, and dissolved oxygen) to a microcontroller that transfers derstanding of the operational status. Integrating big data analytics with
data to a web server via a modem. Su et al. (2020) [32] applied IoT to accurate predictive models can significantly enhance the performance of
ensure remote monitoring and process control of a piggery wastewater WWTPs [45]. The mere use of the raw data collected is insufficient to
treatment system. They achieved high removal efficiencies of BOD, ensure adequate information while having accurate and advanced pre­
COD, and suspended solids - about 90 % or more for each parameter - dictive models. Data preparation, processing, and analysis are essential
with comparable values between sensors measurements and data ob­ steps to obtain a high-quality dataset suitable for extracting reliable
tained from chemical analysis. Zhang et al. (2021) [33] applied IoT to a information. Additionally, minimizing measurement errors is critical in
MBR treating industrial wastewater to provide real-time, continuous, data-driven approaches. Online measurements in WWTPs are typically
and accurate process monitoring, enhancing treatment efficiency. Mar­ performed using calibrated sensors. Despite advancements in the quality
tínez et al. (2020) [34] integrated a wireless sensor network into a of such sensors, actual measurements can be affected by various factors,
WWTP to improve monitoring and control operations, enabling auto­ such as solids deposition, biofilm formation, and calibration drift, which
matic and real-time adjustment of operating parameters, resulting in affect data quality and consequently analysis [18]. In fact, if the
enhanced treatment performance. Karn et al. (2023) [35] proposed an measured data are affected by errors, the results of the data analysis will
IoT system to monitor key parameters affecting wastewater treatment also have errors, and thus the information obtained may not be repre­
(temperature, pH, conductivity, turbidity, dissolved oxygen, and atmo­ sentative of the treatment process being analyzed.
spheric conditions such as humidity, pressure, and solar radiation) in The rapid spread of big data analytics is promoted by the possibility
real-time, providing meaningful information on treatment progress and of storing data on “cloud” storage systems, in addition to local storage
facilitating process control under both ordinary circumstances and un­ devices [26]. CC provides a flexible, efficient, and cost-effective solution
foreseen events. Narayanan et al. (2023) [36] designed an IoT-based for data storage in a virtual environment accessible remotely from any
monitoring system for municipal WWTP using sensors to measure authorized device connected to the Internet. This approach significantly
several parameters (pH, conductivity, temperature, color, and smell), a improves the ease and speed of dataset sharing, enhancing collaboration
microcontroller to process data collected, and a module to send data to a and decision-making processes. Moreover, the risk of data loss due to
cloud server. hardware failure is minimized in cloud-based storage systems because of
IoT devices have been applied to optimize wastewater treatment and their data backup capabilities. The use of CC in WWTPs can simplify
minimize negative impacts associated with WWTPs. For example, IoT data storage, supporting efficient monitoring and analysis of effluent
devices integrated with physical, chemical, and biological treatment quality and process parameters without incurring the costs associated
systems can efficiently control and mitigate odors emitted from WWTPs with traditional data collection methods. Furthermore, CC facilitates
[37]. Integrating IoT systems with other advanced technologies such as real-time access to data, enabling operators to make informed decisions
ML-based modeling enables the development of advanced and smart swiftly. O'Donovan et al. (2015) [46] integrated a CC-based data
systems. Oliva et al. (2021) [38] presented an advanced prototype of collection system into WWTPs to monitor and analyze treatment pro­
IOMS, using a ML predictive model for the continuous assessment of cesses, facilitating timely access to data collected. Salem et al. (2022)
odors emitted by complex industrial facilities, including WWTPs. Zarra [47] integrated CC and IoT devices to build an inexpensive, easily
et al. (2022) [39] proposed an innovative IoT-based system, called configurable, flexible, and portable system that can continuously
SiOMS, to monitor and control odor emissions from WWTPs in real-time, monitor and control influent wastewater in a municipal WWTP, pre­
using an ANN to elaborate and dynamically update the odor monitoring venting the occurrence of negative effects on the treatment process. This
classification and quantification models. Prudenza et al. (2023) [40] system collects and transmits sensor data on influent wastewater vari­
confirmed the enormous potential of IOMSs mitigating the odor impact ables (pH and temperature) in real-time to a cloud service allowing
of a WWTP. They used a multi-sensor system to detect unexpected remote monitoring. In addition, it alerts the operators to abnormal sit­
changes in odor emissions from a WWTP in real-time and consequently uations via SMS and alarms and adjusts inlet valves to prevent waste­
acting for mitigating odors emissions that would cause nuisance and water with unsuitable characteristics from entering the plant (for
complaints of the population living in the proximity of the plant. example, industrial wastewater can be detected and directed to appro­
priate treatment plants).
2.2. Big data analytics and cloud computing
2.3. Artificial intelligence
Wastewater treatment processes are inherently complex due to var­
iations in environmental conditions, process parameters, flow rates, and AI is the ability of a computer-controlled system to perform tasks
contaminant concentrations in influent wastewater. These variations such as reasoning, decision-making, and learning from past experiences.

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S. Cairone et al. Journal of Water Process Engineering 63 (2024) 105486

Although numerous challenges exist, notably in data quality, integra­ Additionally, AI models have been utilized to predict the formation of
tion, interpretability, and reliability of AI models, it is evident that AI DBPs, ensuring both the optimization of the disinfection process and
can and will play a crucial role in developing new solutions for envi­ minimization of DBPs formation [68]. Matheri et al. (2021) [69] used an
ronmental protection and sustainability [48]. AI-based tools are AI model to explore the interrelationship between COD and trace metals
increasingly applied in various engineering applications, including using influent and effluent datasets. Yang et al. (2022) [70] developed a
wastewater treatment [49]. As mentioned above, the efficiency of dynamic neural network model to predict COD and TN in the effluent,
wastewater treatment is influenced by several environmental factors obtaining more accurate results compared to conventionally used static
and process parameters, governed by complex and nonlinear relation­ models. Mohammadi et al. (2023) [71] proposed ANN models to esti­
ships [50]. Mathematical modeling of wastewater treatment processes mate the removal of steroid hormones from wastewater using a MBBR
faces challenges due to the interdependent, nonlinear, and nonsta­ system.
tionary nature of the governing physicochemical processes [18]. Unlike In addition to predictive models for effluent quality, predicting
traditional mathematical models, AI models can solve complex influent wastewater characteristics is crucial because their inherent
nonlinear relationships between input and output data without relying variability significantly affects treatment efficiency. To manage fluctu­
on assumptions or complex equations based on biological, physical, or ations in influent loads in WWTPs and maintain acceptable operating
chemical reactions. Furthermore, while mechanical/mathematical conditions, operational strategies involving over-aeration and over­
models are effective with limited input datasets, AI-based models prove dosing of chemicals are often adopted [72]. However, this approach is
advantageous when dealing with large datasets [51]. After a training unsustainable both economically and environmentally. To overcome
phase, AI-based models can also predict water quality in WWTPs, this issue, several studies have reported models for predicting the
significantly reducing the need for time-consuming laboratory analysis. quantity and/or quality of influent wastewater [72–80]. For instance,
Predictive models serve as valuable tools to support WWTP manage­ Heo et al. (2021) [75] proposed a data-driven hybrid model combining
ment, leading to environmental and economic benefits. They can be multimodal and ensemble-based DL algorithms to predict hourly and
used to optimize process parameters, provide information for timely daily influent loads. The developed model exhibited superior prediction
decisions, and adjust the actions of various equipment, either manually performance compared to five widely used neural network-based
or automatically [52]. models. In another study, Heo et al. (2021) [76] devised a MOSC
The scientific community has been exploring applications of AI in strategy integrating a FCM clustering algorithm, a DNN model, and a
wastewater treatment modeling for several decades [53]. With the NSGA-II to determine optimal operating conditions as a function of
remarkable progress achieved in the field of AI algorithms, research influent characteristics. This strategy effectively managed the non-
efforts can now enable their applications in real WWTPs for advanced stationary and non-linear behavior of the influent wastewater in a full-
modeling. AI models can be applied in wastewater treatment for scale WWTP, enhancing process control. Specifically, they obtained an
enhancing plant performance [54], improving effluent quality, pro­ improvement in WWTP performance while reducing operating costs by
moting wastewater reuse, optimizing the recovery of clean water, en­ 8 %.
ergy, and various materials [49], reducing chemicals and energy Predictive models that focus on both influent and effluent are
consumption [55], optimizing the control of pumping units to cut down particularly interesting, holding enormous potential for enhancing
power consumption and thereby reduce operating costs [22], identifying WWTP management. Wang et al. (2022) [81] used historical data to
process failures and anomalies [56], and developing models to simulate, train ANN models predicting the quality and quantity of influent and
predict, and optimize contaminant removal from wastewater [57,58]. effluent, achieving a relatively high prediction accuracy. The authors
Furthermore, AI modeling can provide support to decision-making sys­ highlighted that the use of the proposed model resulted in improved
tems for WWTPs, assisting operators in making accurate and timely WWTP operational strategies two weeks in advance of actual changes of
decisions [59], and facilitating process control and automation [60]. wastewater quality and loads, resulting in a reduction of over 11 % in
Overall, AI has the potential to significantly enhance the sustainability energy costs and over 16 % in material costs.
of WWTPs, encompassing environmental, economic, and social per­
spectives [61]. 2.3.2. AI as an advanced tool for optimizing wastewater treatment
technologies
2.3.1. Predictive control of influent and effluent characteristics using AI Conventional biological wastewater treatment, particularly the ASP,
Extensive research has focused on the predictive control of influent remains widely used in existing WWTPs [82]. AI-based approaches have
and/or effluent quality parameters such as temperature, pH, BOD, COD, emerged as promising tools to enhance ASP efficiency, improving
turbidity, TSS, TN, ammonium (NH4+-N), nitrate (NO3− -N), TP, F/M, effluent quality and reducing energy consumption [83]. Additionally,
HRT, SRT, SVI, and gaseous emissions [62]. Applications of data-driven AI-based monitoring has proven effective in accurately detecting faults
AI algorithms, particularly ML and DL models, have gained prominence in WWTPs, such as sensor malfunctions, toxic shocks, and sludge bulk­
in predicting effluent quality with the objective of reducing the release ing, and has allowed for identifying their causes, providing valuable
of contaminants into the environment and improving socio-economic insights to prevent related problems [56,84]. Bellamoli et al. (2023)
aspects linked to wastewater management [63]. Guo et al. (2015) [64] [85] showed the efficacy of ML models in detecting and classifying
developed two models utilizing ANN and SVM, using daily water quality anomalies in WWTPs. Oulebsir et al. (2020) [86] proposed an ANN-
and meteorological data as input parameters to predict the TN concen­ based method to predict and optimize the energy consumption of the
tration in the effluent of a plant where integrated treatment of food ASP using previously measured data from a real urban WWTP, including
waste and wastewater occurs. In the study conducted by Manu and Talla contaminant concentrations in the influent and treated effluent, influent
(2017) [65], AI models based on a SVM and an ANFIS were employed to temperature and flow rate, recirculated sludge flow to the aeration tank,
predict Kjeldahl Nitrogen removal efficiency in a full-scale domestic and total energy consumption. Zheng et al. (2022) [87] used ANN to
WWTP. Input variables for the models included pH, COD, total solids, investigate the influence of different factors (and different combinations
free ammonia, ammonia nitrogen, and Kjeldahl Nitrogen of the influent of input variables) on the variation of sludge settleability in an ASP.
wastewater. Liu et al. (2020) [66] proposed a DKELM method to predict They proposed a method for selecting appropriate input variables to
COD in the effluent of a papermaking wastewater treatment process, predict such variations. This approach can be applied to optimize the
achieving superior predictions compared to those of conventional process by counteracting sludge bulking, which is a significant problem
modeling methods. AI models have also been employed to predict the in ASP. Mihály et al. (2022) [88] developed dynamic ANN models
removal of fecal coliform and total coliform in a WWTP using an capable of predicting effluent quality and energy consumption of a
intermittent cycle extended aeration-sequential batch reactor [67]. WWTP. These models allowed improving effluent quality and reducing

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S. Cairone et al. Journal of Water Process Engineering 63 (2024) 105486

the energy consumptions of the ASP by adjusting operating conditions, constructed wetlands and predict the removal of nitrogen and phos­
such as nitrate and activated sludge recirculation rates and aeration phorus based on data obtained from laboratory experiments conducted
intensity, presenting an alternative to conventional process control over a two-year period. Nguyen et al. (2022) [107] combined RSM,
based on operator experience. Wang et al. (2023) [89] applied an en­ ANN, and PSO to model and optimize a MEC-AD system. The resulting
gineering approach based on the VSL for intelligent air demand pre­ predictive model displayed a high accuracy, demonstrating its potential
diction and control to enhance aeration efficiency in a full-scale WWTP. for real-time optimization and control of MEC-AD applications. They
They achieved a 16 % reduction in air demand compared to conven­ estimated that by applying the optimal voltage value determined by
tional control, where aeration is based on a preset dissolved oxygen their model, net energy output and net monetary value increased by up
level. Wang et al. (2023) [90] achieved aeration energy savings of about to 160 % and 300 %, respectively, compared to anaerobic digestion
49 % compared to the manual operation mode in a pilot-scale WWTP by alone. Tufaner and Demirci (2020) [108] developed ANN and NRM to
applying an online intelligent management method based on the inte­ predict the biogas production rate in an anaerobic treatment process for
gration of MLP regression model with ASMs. synthetic wastewater. Such models help in controlling operating pa­
Among treatment methods, membrane-based technologies have rameters, thereby enhancing treatment efficiency, increasing biogas
attracted increasing interest due to their potential to achieve advanced production, and reducing operating costs.
wastewater treatment. These technologies facilitate safe wastewater
reuse and sustainable resource recovery, minimizing negative environ­ 2.4. Blockchain technology
mental impacts [91]. AI modeling has been successfully applied to
optimize membrane technologies for wastewater treatment. Im et al. Wastewater can be considered a valuable data source, providing
(2022) [92] proposed AI models for predicting process parameters meaningful information about the lifestyle and health of a specific
(water flux, membrane fouling, and removal efficiencies of DOC, TN, population within a given area. Concentrations of various substances in
and TP) in a FO membrane system, supporting the decision-making wastewater can be utilized to estimate their prevalence in the corre­
process. Viet and Jang (2023) [93] explored the application of ML sponding population. This fundamental concept is the basis of WBE, a
models to simulate the behavior of micropollutants in water and well-established tool used to obtain real-time insights into the con­
wastewater treatment using a FO membrane process. These simulations, sumption and misuse of drugs, whether legal or illegal, in a population.
based on a large dataset collected from previously published studies, However, WBE can extended its efficacy to other domains, including
proved effective in predicting the impact of operating parameters on assessing exposures to various chemical and biological agents (e.g.,
micropollutant removal by the osmotic membrane. This approach also cosmetics, pesticides, pollutants, and pathogens), determining the
provided information for improving the design and operation of the prevalence of specific diseases (including allergies, diabetes, and cancer)
system to maximize the removal of each micropollutant. In addition to within a defined area, and understanding various aspects of the pop­
supporting process optimization and control, AI-based approaches were ulation's lifestyle (e.g., consumption of controlled substances) [109]. In-
also proven adequate for optimizing membrane fabrication techniques, depth analysis of wastewater enables the monitoring of the circulation of
leading to the production of membranes with advantageous features for both viral and bacterial pathogens within a population. For instance,
wastewater treatment [94–96]. Furthermore, membrane fouling, a since viruses cannot replicate in wastewater, their concentrations
major issue in membrane-based processes, can be effectively predicted directly correlate with the number of infected individuals in a popula­
by AI models [51,97]. These models could serve as useful tools to define tion [110]. A notable example is the correlation observed during the
and implement effective fouling mitigation strategies. Im et al. (2021) COVID-19 pandemic, where the concentration of SARS-CoV-2 in
[98] coupled real-time monitoring using non-invasive technologies with wastewater was shown to directly mirror the number of infected in­
DL models to predict and manage fouling in a FO membrane system. dividuals in cities [111,112]. Given the wealth of information that can
Kovacs et al. (2022) [99] employed ML algorithms (RF, ANN, and LSTM) be extracted from wastewater, it is crucial to address potential chal­
to develop data-driven models capable of predicting TMP in a MBR. lenges associated with data usage and storage. These challenges
Cámara et al. (2023) [100] conducted real-time predictions of mem­ encompass cybersecurity issues, including the vulnerability of data to
brane fouling in an AnMBR by combining FF and LSTM neural networks. hacking, as well as the potential for misinterpretation and/or inaccurate
Membrane fouling predictions provided valuable information for presentation of data to third parties. To address the data security chal­
decision-making, optimizing maintenance procedures, and allowing lenges, Hakak et al. (2020) [113] proposed the integration of Blockchain
adjustments to operating conditions before reaching a critical state. technology as a viable solution for enhancing WWTP management.
Among other treatment processes, AI has been employed to predict Blockchain technology finds various applications in wastewater man­
the optimal coagulant doses for the coagulation-flocculation process, agement [114]. Iyer et al. (2019) [115] utilized Blockchain technology
considering water quality metrics such as pH, turbidity, and color. This within a control system aimed at incentivizing wastewater reuse by
method proves to be a viable approach for optimizing chemical usage, providing tokens to industries based on the quantity and quality of
resulting in significant positive effects on economic, environmental, and reused wastewater. They employed blockchain to securely store data
social aspects [101]. Wang et al. (2021) [102] showcased that using the and prevent potential tampering of measurements collected by IoT
optimal coagulant dose obtained employing a data-driven optimization meters. A similar approach was proposed by Alzahrani et al. (2023)
model resulted in a 10 % reduction in costs at a WWTP under exami­ [116], highlighting the potential of blockchain to enhance wastewater
nation. AI modeling has also been applied to optimize electrochemical management and encourage wastewater reuse in smart cities. Sundar­
processes used in water and wastewater treatment, such as electro­ esan et al. (2021) [117] showed that implementing a reliable and secure
oxidation, electrocoagulation, electro-Fenton, and electrodialysis [103]. system based on blockchain technology can safeguard water resources
For example, Shirkoohi et al. (2022) [104] developed AI models capable and foster the development of smart cities where drinking water use is
of predicting phosphate removal efficiency from wastewater using the optimized, water scarcity and water pollution are reduced, and waste­
electrocoagulation process. Picos-Benítez et al. (2020) [105] utilized water is effectively managed.
ANN and a GA to optimize operating conditions of an EO process con­
ducted in a filter-press type reactor used for dye wastewater treatment. 2.5. Robotics and drones
They achieved a high removal efficiency of bromophenol blue
(approximately 89 %) from synthetic wastewater while reducing energy Automation in wastewater treatment can be improved through the
consumption (maximum specific energy consumption per unit COD utilization of robots and drones, facilitating inspections, monitoring
mass of 0.26kWh/gCOD at 60 min of EO treatment). Kiiza et al. (2020) activities, and maintenance operations, particularly in difficult-to-access
[106] developed ANN models to optimize the configurations of areas. Drones can capture images and video, enabling the timely

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detection of problems and facilitating prompt actions. Beyond conven­ due to its constant connection with the PT. The models in a DT must be
tional cameras, drones can be equipped with various sensors to obtain dynamically updated or adjusted based on the data obtained from the
additional valuable data. For instance, Burgués et al. (2021) [118] uti­ PT. In turn, action must be taken in the PT, either manually or auto­
lized a drone equipped with low-cost gas sensors to monitor real-time matically, based on the results obtained from the DT [126,127].
odor emissions from a WWTP. To avoid interference from the drone's The application of DTs can facilitate dynamic process simulations,
propellers on gas concentration measurements, sampling was conducted offering significant support in the development of intelligent water
using a 10-m tube suspended from the drone. Burgués et al. (2022) [119] resource management systems and cutting-edge treatment technologies
installed an electronic nose on a drone, enhancing the real-time moni­ [128]. Implementing DTs in the management of WWTPs (Fig. 3) allows
toring and mapping of odor emissions from a WWTP. They estimated for optimizing chemical and energy consumption, reducing operating
odors perceived by humans using multivariate predictive models. costs, enhancing data collection, and preventing the discharge of inad­
Guerra et al. (2016) [120] proposed an automated sampling system in a equately treated wastewater [55]. Furthermore, DTs have the potential
WWTP, employing a drone operated by a robotic platform to collect to increase contaminant removal, improve resource recovery efficiency,
samples. This automation replaced the need for manual sampling by and reduce GHG emissions [127]. Additionally, a DT helps monitoring
operators, enabling consistent and precise sampling, even in hard-to- equipment conditions in WWTPs, supporting predictive maintenance
reach areas of the plant, while significantly reducing operator expo­ strategies [129]. DTs can also simplify monitoring, remote control, and
sures to potential hazards. maintenance operations of WWTPs, particularly those in remote com­
munities [124].
2.6. Virtual and augmented reality Various models can be integrated into a DT associated with a WWTP.
For instance, Moretta et al. (2021) [130] proposed the ADE model,
The advent of VR and AR has opened up new prospects in various derived from a generalization of the ADM1 of the IWA Task Group for
fields. VR can be defined as technology that immerses the user in near- Mathematical Modeling of Anaerobic Digestion Processes. The ADE
real experiences within a virtual environment, while AR is the tech­ model has shown potential as a DT for optimizing biogas production in
nology that overlays/combines computer-generated information, such anaerobic digestion processes, maximizing methane (CH4) content, and
as visualizations and audio, with the real world, assisting users in real- minimizing hydrogen sulfide (H2S) content. Simulations using this
life activities [121]. VR and AR find applications as support tools in model in a full-scale anaerobic co-digester indicated that adjusting the
WWTPs for several purposes, including practical training for new plant oxygen rate could increase CH4 content in the biogas by 4%vol. Al and
operators [122] and monitoring treatment processes [26]. One partic­ Sin (2021) [131] developed a DT to optimize both the design and
ularly interesting application of these technologies involves conducting operational phases of a WWTP, with the goal of minimizing operating
virtual inspections of plants located in remote areas. Cerniauskas and costs while ensuring adequate effluent quality.
Werth (2022) [123] described a virtual inspection solution allowing
personnel to connect to sensors and 360-degree cameras installed at the 3. Critical analysis of the application of cutting-edge tools in
WWTP to be inspected using a VR headset. Through this, personnel can wastewater treatment
have a real-time and immersive view of the plant, providing the feeling
of being there. Additionally, the solution provides a data interface The integration of cutting-edge tools, such as IoT, cloud computing,
enabling real-time visualization of operational data, offering insights big data analytics, AI, blockchain, robotics, drones, VR/AR, and digital
into the WWTP's operating status. In addition to the time and cost sav­ twins, into WWTPs has the potential to drive the development of
ings compared to traditional field inspections, conducting inspections in advanced and smart systems that ensure high treatment performance
virtual mode ensures that they can be performed from anywhere and at and optimized monitoring, control, and management operations.
any time, further promoting increased inspection frequency. This Table 1 provides a concise overview of the roles, advantages, and
approach can also significantly reduce on-site expert technical super­
vision, as technical specialists can remotely view the plant and provide
necessary guidance to on-site operators.
Lian et al. (2022) [124] combined digital twins (see Section 2.7) of
WWTPs located in remote areas with MR technology, a fusion of VR and
AR, allowing remote process monitoring and virtual operator training.
They highlighted that staff training utilizing VR and AR technologies
demonstrated superior results compared to conventional methods.
Similarly, VR has also been proven effective as a teaching aid for stu­
dents in wastewater treatment technical courses, enhancing the prepa­
ration of future operators and engineers involved in WWTPs. Through
simulations and the utilization of VR, students can observe treatment
units in 3D, gaining a comprehensive understanding of WWTPs. For
instance, they can monitor process conditions, familiarize themselves
with the equipment used in the plants, and operate virtually through
interactive functions (e.g., adjusting operating parameters, regulating
valves, pumps and other components, and practicing interventions in
scenarios simulating potential issues) [125].

2.7. Digital twin

A digital twin is a virtual replica of an object, system, or plant,

referred to as the physical twin. A DT is a model based on information
and data collected by continuously monitoring the PT. The outputs of
the DT help operators deal with any operational problems in the PT by
predicting failures and facilitating proactive maintenance [55]. A DT
differs from a control system based on conventional simulation models Fig. 3. Schematic representation of a digital twin (DT) for a WWTP.

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Table 1
Roles, advantages, and limitations of cutting-edge tools implemented in WWTPs.
Technology Role Advantages Limitations

- Need for significant data storage capacity

- Improved monitoring system
IoT Automatic data collection and transmission - Increased maintenance requirements for
- Extensive and real-time data collection
sensors and equipment
- Enhanced data accessibility
Cloud - Data security concerns
Remote data storage and analysis - Cost-effective, scalable, and flexible data storage
computing - Dependency on internet connectivity
solutions
- Challenges in managing and protecting
Big data Extracting valuable insights from wastewater - Improved prediction and prevention of inefficiencies
sensitive data
analytics treatment processes - Enhanced data-driven decision making
- Requirement for skilled data analysts
- High dependence on complete and accurate
- Enhanced process optimization and control
Advanced modeling of wastewater treatment datasets
AI - Increased treatment efficiency, optimized resource use,
processes - Challenges with model interpretability and
and reduced operating costs
explainability
Ensuring data integrity and traceability within - High implementation costs
Blockchain - Enhanced data security and transparency
a secure platform - Significant technological complexity
Automation of maintenance and control - Reduced personnel risks - High initial costs and integration complexity
Robotics
activities - Minimized human error - Potential job displacement concerns
Enhancing inspections and monitoring - Advanced monitoring capabilities - Regulatory restrictions on flights
Drones
activities - Access to difficult-to-reach areas - Operation affected by weather conditions
Process simulation, virtual inspections, and - Safe and immersive training environment - High costs
VR/AR
personnel training - Facilitated remote assistance and collaboration - Complexity in creating realistic scenarios
- Significant investment in modeling and
Continuous monitoring and modeling of the - Advanced real-time monitoring and control
Digital twin simulation tools
plant - Optimized predictive maintenance strategies
- Extensive IT infrastructure requirements

limitations of these advanced tools within the WWTPs. consumption, operating costs, chemical use, and environmental im­
Based on the review presented in Section 2, the key aspects related to pacts. Advanced modeling and simulations play a critical role in pre­
the application of these innovative technologies are summarized below: venting malfunctions and defining effective actions in emergency
Advanced sensors: The continuous technological evolution facilitates situations, providing valuable support to operators in responding to
the widespread implementation of advanced sensors in WWTPs. These potential problems and preventing (or otherwise limiting) failures,
sensors enable real-time and continuous monitoring of numerous oper­ resulting in reduced plant downtime. However, in addition to the
ating parameters that influence the treatment process, such as flow rate, imperative of ensuring complete and accurate datasets to obtain reliable
temperature, pH, conductivity, redox potential, dissolved oxygen, and results, AI modeling encounters limitations in interpretability and
contaminant concentrations. Monitoring these parameters provides explainability. In fact, current AI models employed in wastewater
valuable insights into treatment progress, enhancing the accuracy and treatment modeling act as black boxes, failing to provide the necessary
timeliness of process control. However, it is crucial to emphasize the information for understanding the mechanisms and relationships among
importance of selecting appropriate sensors for the specific wastewater the variables involved in the treatment process [133].
treatment application [7] and ensuring their proper maintenance to Automation and advanced control: Utilizing cutting-edge technolo­
obtain accurate measurements while reducing issues like fouling and gies promotes automation and advanced control of wastewater treat­
interference, which could lead to inaccurate measurements and, ment, potentially resulting in highly efficient processes characterized by
consequently, unrepresentative information about the monitored pro­ flexibility and adaptability in changing operating conditions. Automa­
cess [132]. tion minimizes the dependence of the process on direct operator inter­
Data analytics: The utilization of advanced sensors results in the vention, reducing human error and minimizing operator exposure to the
collection of large amounts of data, creating a valuable “information health and safety risks related to wastewater facilities. Moreover, this
mine”. Effective big data analytics is essential to derive meaningful in­ approach facilitates the management and operation of dynamic systems
sights from this data. Advanced data analytics techniques play a crucial as it provides real-time control of operating parameters. Advanced
role in extracting relevant information, such as identifying trends and technologies also enable remote monitoring and control while
anomalies, thereby supporting decision-making and improving process enhancing plant management and reducing response time to critical
control to enhance WWTP performance. The advent of IoT further fa­ situations.
cilitates real-time transfer of measurements through interconnected Integration of technologies not conventionally used in WWTPs:
devices, sensors, and data management platforms. This not only en­ Ongoing research is focusing on exploring the integration of technolo­
hances monitoring operations but also integrates data management, gies, typically applied in other sectors, within WWTPs. For instance,
providing updated information for decision-making. However, it is drones have emerged as valuable tools for supporting monitoring ac­
essential to address potential challenges associated with data, including tivities, while AR and VR technologies support staff training, inspection,
accurate interpretation and cybersecurity concerns (addressed with and maintenance operations, especially in WWTPs situated in remote
appropriate tools like blockchain technology). areas.
Advanced simulation and modeling: While operating parameters are The application of advanced technologies in wastewater treatment,
adjusted based on operators' knowledge and water quality data from in both existing and proposed facilities, offers multiple advantages,
online measurements and offline analysis, progress in science and including reduced operating costs, lower energy consumption, appro­
technology is revolutionizing the simulation and modeling of waste­ priate use of resources, improved maintenance operations, more timely
water treatment processes. This represents a powerful strategy to ability to detect anomalies, reduced plant downtime, and minimized
enhance process control. The rise of AI models, particularly ML and DL negative environmental impacts. These cutting-edge technologies have
models, has garnered significant interest, offering advanced predictive the potential to improve WWTP sustainability (as discussed in the
capabilities based on real-time data. These predictive models effectively following Section 4) while promoting technological development, digi­
anticipate the evolution of the treatment process, optimizing WWTP tal transformation, green transition, and circular economy (Fig. 4).
performance in terms of contaminant removal efficiency, energy However, it is essential to consider the disadvantages and challenges

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Fig. 4. Advanced tools to promote technological development, digital transformation, ecological transition, circular economy, and sustainable development in the
field of wastewater treatment.

associated with adopting these technologies, including the significant analytics, and advanced modeling, can significantly enhance the overall
initial investment required to acquire devices, sensors, and software. performance of WWTPs. From an environmental perspective, this inte­
The capital costs, however, can be recouped over time through the po­ grated approach can significantly increase contaminant removal effi­
tential reduction in operating costs resulting from optimized process ciency, resulting in safer effluents for discharge into the environment
efficiency and enhanced plant management. Ongoing technological and for reuse. The consequential reduction in the release of contami­
advancements also foster the production and commercialization of nants minimizes potential negative environmental impacts, reducing
affordable instruments, thereby reducing initial investment costs. pollution, conserving aquatic ecosystems, and preserving biodiversity.
Staff training is another crucial aspect to consider. It is necessary to Moreover, these advanced strategies facilitate the early detection of
ensure that personnel possess the required skills to understand the critical situations, allowing for the timely implementation of corrective
functionalities and operate technologically advanced systems. Notably, actions to prevent the release of harmful substances into the environ­
the integration of advanced technologies into WWTPs is not expected to ment through improperly treated effluent.
negatively impact employment, as skilled staff remains essential for Implementing advanced automation, optimization, and control sys­
monitoring systems, ensuring proper maintenance, and addressing tems is crucial for adapting the plant's operating conditions to the actual
critical situations. State-of-the-art technologies, such as AI, are consid­ needs of the treatment process, significantly reducing energy con­
ered tools that support operators, enhancing the quality of their work sumption [137]. WWTPs are among the largest global energy con­
and WWTP performance. However, trained operators play a crucial role, sumers, accounting for about 3 % of worldwide electricity consumption
particularly in supervising these advanced technologies and preventing [138]. Consequently, ensuring energy efficiency in wastewater treat­
significant problems, in case of system failures [134]. ment is a significant environmental and economic challenge. Energy
Among the limitations of integrating intelligent automation and consumption in WWTPs depends on several factors, including plant size
control strategies into WWTPs, there is the possibility that such systems (or served population equivalent), treatment technologies, influent
may be based on measurements that are incorrect and/or insufficiently quality, effluent discharge standards, and geographical location
representative of the treatment process. In fact, if the sensors are not [139,140]. A substantial portion of energy consumption in a WWTP
reliable and/or are not regularly or properly maintained, the actions (between 60 and 80 % of the total energy consumption) is attributed to
taken by automation and smart control systems will lose all the potential the biological treatment alone [141]. Specifically, the highest energy
benefits associated with their application. The number and deployment consumption is typically attributed to the aeration system (between 0.18
of sensors in different points and units in a WWTP can also affect the and 0.80 kWh/m3 of treated wastewater), followed by the pumping of
reliability and the information content of the data and thus the effec­ wastewater, activated sludge recirculation, and sludge management
tiveness of control operations. For example, a single sensor measuring a [140]. The main portion of energy consumption in WWTPs is for elec­
single parameter in a large tank probably does not reflect the condition trical energy (more than 80 % of the energy consumption), followed by
of the entire tank. In accordance with Bahramian et al. (2023) [135], the mechanical energy (about 15 %) and minor contributions from manual
installation of multiple sensors at different locations is recommended. and chemical energy [142].
Reducing energy consumption in WWTPs is pivotal for decreasing
4. Sustainability of wastewater treatment promoted by GHG emissions associated with wastewater treatment, aligning with
advanced technologies global efforts to mitigate climate change and achieve carbon neutrality.
WWTPs are recognized as major contributors to GHG emissions,
Sustainability is anchored to three fundamental pillars: the envi­ responsible for about 1.6 % of global GHG emissions and 4.6–5.2 % of
ronmental, economic, and social dimensions [136]. WWTPs play a key total global non-CO2 GHG emissions (e.g., CH4 and N2O) [138]. Without
role in achieving sustainable development goals, particularly the SDG 6 effective strategies, the expected increase in wastewater production and
(“Ensure availability and sustainable management of water and sanita­ contaminant loads due to population growth, urbanization, and indus­
tion for all”). However, water holds a universal value across all the trialization could exacerbate this issue [137]. WWTPs contribute
SDGs, and wastewater treatment can directly or indirectly impact on directly and indirectly to GHG emissions. Direct GHG emissions are
many of them [3]. Consequently, the development and implementation associated with sludge management, off-gas from wastewater collection
of strategies to enhance the sustainability of wastewater treatment can systems, and biological removal of carbon, nitrogen, and phosphate,
yield multiple benefits. This section provides an analysis of environ­ while indirect emissions are linked to the import of electrical and
mental, economic, and social perspectives concerning the integration of thermal energy, the production and transportation of chemicals and
advanced tools and smart systems in wastewater treatment. fuels, and the disposal of waste [138]. Sharawat et al. (2021) [142]
estimated the direct and indirect GHG emissions from an SBR-based
WWTP amount to 105 tCO2e/year and 1316 tCO2e/year, respectively.
4.1. Environmental perspective He et al. (2023) [143] presented a LCA of a conventional WWTP, pre­
senting that indirect GHG emissions from electricity use in the operation
The integration of advanced treatment technologies and intelligent phase (86.4 % of GHG emissions) represented the largest carbon
control systems, supported by real-time monitoring systems, data

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footprint in the entire life cycle of the plant. Considering the current communities, individuals, and society. These benefits encompass public
heavy reliance on fossil fuels for electricity production, these data un­ health improvements, enhanced community welfare, and better public
derscore the importance of reducing electricity consumption for envi­ perception. The implementation of advanced control strategies in
ronmental sustainability. Implementing energy-saving measures, WWTPs can improve the treatment performance, ensuring that treated
producing renewable energy, and integrating advanced automation and wastewater complies with regulatory standards. This proactive
control strategies are recognized as priorities [137]. Recognizing the approach directly safeguards public health by minimizing the risk of
pivotal role of automation and control strategies, Rani et al. (2022) waterborne diseases and infections. Moreover, enhanced sanitation
[144] proposed strategies to transform the existing energy-positive leads to healthier living conditions, thereby improving the overall well-
WWTPs into net energy producers, aiming for a net-zero carbon emis­ being and quality of life for the population.
sion sector. They emphasized the use of smart and automated technol­ The adoption of advanced technologies and automation systems re­
ogies to provide clean water while minimizing the energy requirement quires a skilled workforce to operate, maintain, and manage these
of the WWTPs, contributing to their self-sufficiency and reducing carbon technologies and systems, as well as to analyze data properly. This
emissions and negative environmental impacts. requirement generates job opportunities and encourages skill develop­
Similarly, advanced control strategies can significantly reduce ment and professional growth within the community, positively
chemical usage without compromising treatment efficacy, resulting in affecting the local economy. Furthermore, the integration of advanced
both environmental and economic benefits. For instance, recent studies tools in WWTPs demonstrates a commitment to environmental sus­
have utilized AI models to predict and optimize coagulant dosages. tainability and responsible wastewater management. This commitment
Consequently, this strategy holds promise in reducing both direct and can foster a positive public perception of WWTPs, instilling confidence
indirect GHG emissions, contributing to the achievement of carbon in the community regarding the effectiveness of wastewater manage­
neutral wastewater treatment. ment practices and their contribution to environmental protection.
Moreover, the implementation of cutting-edge technologies and
smart control systems can facilitate the transition from WWTP to RRF, 5. Future perspectives
transforming wastewater from a waste stream into a valuable resource.
Notably, this approach can lead to increased recovery of clean water, The integration of cutting-edge technologies, such as IoT, cloud
renewable energy, nutrients, and other valuable products, aligning with computing, big data analytics, AI, blockchain, robotics, drones, VR/AR,
the principles of the circular economy and further reducing negative and digital twins, has shown significant potential in enhancing auto­
environmental impacts and operating costs (see Section 4.2). mation and advanced process control within WWTPs. Table 2 provides a
concise comparison between conventional practices and the potential
4.2. Economic perspective

The economic implications of deploying smart and cutting-edge Table 2

technologies in WWTPs are crucial to consider, as they significantly Comparison of conventional practices and potential unlocked by advanced tools
influence their adoption in full-scale applications. Although the initial implemented in WWTPs.
investment may be considerable, implementing advanced and intelli­ Aspect Conventional practices Potential unlocked by
gent tools can lead to substantial savings in operating costs during the advanced tools
lifetime of the facility. These savings, over time, can offset the initial - Reduced need for offline
investment, contributing to the economic sustainability of this strategy. - Heavy reliance on offline
measurements
During the operational phase of a WWTP, the main cost components - Advanced sensors and IoT
measurements
devices for comprehensive
include energy consumption, sludge management, chemical use, and Monitoring and
- Basic sensors for online
online monitoring
maintenance operations. The literature review presented in Section 2 measurements
data - Improved data storage
- Limited data collection
highlights how the application of advanced and smart tools enables the management capacity and security
and storage capabilities
development of efficient control strategies. Among substantial benefits, - Data often improperly
protocols
- Big data analytics for deep
these advanced control strategies enhance the performance of WWTPs analyzed
insights and informed
and reduce resource consumption by optimizing energy and chemicals decision-making
use. This not only results in significant environmental benefits (see - Limited use of modeling - Advanced modeling and
Section 4.1) but also provides economic advantages. Furthermore, the Modeling and
and simulation tools simulation tools integrated
adoption of these technologies supports the transition from traditional - Conventional mechanical/ into daily operations
simulation
mathematical modeling - Enhanced support for
WWTPs to RRFs. The enhanced recovery of valuable resources, which
approaches decision-making process
can be either reused or introduced into the market, leads to direct eco­ - Heavy reliance on staff
- Proactive control strategies
nomic benefits [5,145]. experience and preset
Optimization and - Adaptive responses for
Continuous and real-time monitoring provides deep insights into control
thresholds
continuous optimization
- Reactive response to
equipment performance and operational conditions, facilitating an based on real-time data
process anomalies
effective predictive maintenance. This approach minimizes unexpected - Basic automation features
failures and extends equipment lifespan, reducing the replacement and - Extensive automation
Automation - Heavy reliance on manual
capabilities
maintenance costs. Furthermore, WWTPs equipped with state-of-the-art intervention
technology, perceived as technologically advanced and environmentally - Scheduled maintenance
- Predictive maintenance
program
friendly, could attract more investments for expansions, upgrades, or Maintenance - Remote monitoring and
- On-site inspections and
new projects than “ordinary” WWTPs. Additionally, access to real-time intervention capabilities
manual troubleshooting
data and the implementation of advanced data analytics support up- - Straightforward - Increased system
to-date and data-driven decision making, ensuring that investments management complexity
System - Basic training for - Skilled personnel is required
and expenditures in WWTP management are strategically allocated.
management operators - Remote management and
- Frequent manual checks decision-making
4.3. Social perspective and interventions capabilities
- Higher initial investment
- Lower initial investment
The integration of state-of-the-art control and automation tools into Costs offset by reduced long-term
but higher operating costs
operating costs
WWTPs offers significant societal benefits, positively impacting

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S. Cairone et al. Journal of Water Process Engineering 63 (2024) 105486

unlocked by implementing these advanced tools in WWTPs. Further neutral, and circular economy-based wastewater treatment practices.
efforts are needed to overcome the existing limitations that hinder their
widespread implementation in real WWTPs. According to the critical CRediT authorship contribution statement
analysis of the literature reported in Section 3, current challenges in
applying these advanced tools include significant initial investment, the Stefano Cairone: Writing – review & editing, Writing – original
requirement for specialized personnel, the need to collect reliable data draft, Formal analysis, Conceptualization. Shadi W. Hasan: Writing –
representative of the treatment process, the necessity of ensuring review & editing. Kwang-Ho Choo: Writing – review & editing.
adequate data analysis and advanced process modeling, and potential Demetris F. Lekkas: Writing – review & editing. Luca Fortunato:
cybersecurity issues. Writing – review & editing. Antonis A. Zorpas: Writing – review &
Ongoing research and technological advancements can and will lead editing. Gregory Korshin: Writing – review & editing. Tiziano Zarra:
to the development of increasingly sophisticated and intelligent tools Writing – review & editing. Vincenzo Naddeo: Writing – review &
and systems, making wastewater treatment more efficient and sustain­ editing, Writing – original draft, Supervision, Funding acquisition,
able. A key area of research involves the development of advanced Conceptualization.
hardware components, such as IoT devices and advanced sensors, which
could improve monitoring operations and enable more responsive and Declaration of competing interest
optimized control of treatment processes. Similarly, the development of
more accurate and interpretable AI models can significantly enhance the The authors declare that they have no known competing financial
modeling of wastewater treatment processes, offering substantial ben­ interests or personal relationships that could have appeared to influence
efits in advanced and proactive process optimization and control. the work reported in this paper.
A major challenge lies in the development of cutting-edge systems
capable of dynamically adapting operating parameters in real-time to Data availability
changes in environmental conditions and in the qualitative and quan­
titative characteristics of influent wastewater. The research focus should No data was used for the research described in the article.
include the integration of such innovative and intelligent systems with
both conventional treatment approaches widely applied in existing Acknowledgments
WWTPs (i.e., ASP) and advanced/emerging treatment technologies (e.
g., membrane processes and electrochemical processes). Emphasis This work was supported by the Italian Ministry of Foreign Affairs
should be placed on improving contaminant removal efficiency, and International Cooperation through the project coordinated by prof.
reducing operating costs, minimizing energy consumption, and V. Naddeo (grant number: KR23GR05). This work forms part of a
enhancing the recovery of value-added resources such as clean water, research project supported by a grant of the Italian Ministry of Univer­
renewable energy, nutrients, biofuels, and other valuable products. sity and Research (MUR) through the Research project of national in­
Adopting advanced automation and control systems could also terest PRIN 2022 PNRR (D.D. n. 1409 del 14 September 2022) entitled
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