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Applied Sciences
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Advanced Technology and Applications of Artificial Intelligence in Wastewater Treatment
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Advanced Technology and Applications of Artificial Intelligence in Wastewater Treatment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 7200

Special Issue Editors

Dr. Laurentiu Luca

E-Mail Website
Guest Editor
Department of Automation and Electrical Engineering, Dunărea de Jos University of Galati, Galati, Romania
Interests: wastewater treatment plants; sewer networks; wastewater treatment; water quality
Dr. Sergiu Caraman

E-Mail Website
Guest Editor
Department of Control Systems and Electronics, Dunărea de Jos University of Galati, Galati, Romania
Interests: modeling and control of biotechnological systems; fuzzy and nonlinear control; expert systems

Special Issue Information

Dear Colleagues,

The field of wastewater treatment has been researched by numerous interdisciplinary teams, including process engineers, chemical engineers, microbiologists and control engineers, collaborating to discover optimal solutions to the complex problems of wastewater treatment. Considering that these processes are nonlinear, highly complex and strongly affected by uncertainties (parameter and model uncertainties), these processes have become a significant challenge for modeling, identification and control specialists to develop advanced methods in order to increase the efficiency of the wastewater treatment process.

Research in the field of wastewater treatment has evolved in two important directions:

  1. The modernization of infrastructure for wastewater collection and treatment in urban areas in parallel with the development of new wastewater treatment technologies;
  2. The development and use of new automation methods (mathematical modeling, advanced control strategies, process optimization, applications of artificial intelligence) in order to increase the efficiency of wastewater treatment processes.

In particular, artificial intelligence applications have the potential to solve the problems faced by wastewater treatment systems, the results of which manifest as significant cost savings through the accurate prediction of process behavior and efficient operational optimization.

We can successfully apply solutions based on advanced technology and artificial intelligence that solve the following problems:

  • Optimizing control processes within a wastewater treatment plant.
  • The predictive maintenance of assets within treatment plants.
  • Intelligent monitoring of quality parameters of treated water.
  • Optimizing anaerobic digestion processes to maximize biogas production and reduce carbon footprint.
  • Intelligent management of energy-consuming operations based on scheduled operation in the minimum cost period.
  • The dynamic optimization of treatment processes by adjusting the operational parameters.
  • Optimizing the dosing processes within treatment plants

Dr. Laurentiu Luca
Dr. Sergiu Caraman
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advanced control strategy
  • process optimization
  • optimal setpoint-based control
  • predictive maintenance
  • artificial intelligence application

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Published Papers (2 papers)

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Research

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19 pages, 925 KB  
Article
LSTM-Based Neural Network Controllers as Drop-In Replacements for PI Controllers in a Wastewater Treatment Plant
by Muhammad Adil and Ramon Vilanova
Appl. Sci. 2025, 15(22), 12046; https://doi.org/10.3390/app152212046 - 12 Nov 2025
Viewed by 350
Abstract
Wastewater Treatment Plants (WWTPs) rely on automatic control strategies to regulate pollutant concentrations and comply with environmental standards. Among them, Proportional Integral (PI) controllers are widely adopted for their simplicity and robustness, yet their effectiveness is limited by the nonlinear and time-varying dynamics [...] Read more.
Wastewater Treatment Plants (WWTPs) rely on automatic control strategies to regulate pollutant concentrations and comply with environmental standards. Among them, Proportional Integral (PI) controllers are widely adopted for their simplicity and robustness, yet their effectiveness is limited by the nonlinear and time-varying dynamics of biological processes. In this work, Long Short-Term Memory (LSTM)-based Artificial Neural Network (ANN) PI controllers are proposed as data-driven replacements for conventional PIs in key WWTP feedback loops. Using the Benchmark Simulation Model No. 1 (BSM1), ANN controllers were trained to replicate the behavior of default nitrate and nitrite nitrogen (SNO,2) and dissolved oxygen (SO,5) loops, under both time-agnostic and time-aware strategies with three- and four-input configurations. The four-input time-aware model delivered the best results, reproducing PI behavior with high accuracy (coefficient of determination, R2≈0.99) and considerably reducing control errors. For instance, under storm influent conditions, the SO,5 controller reduced the Integral of Squared Error (ISE) and Integral of Absolute Error (IAE) by 84.7% and 68.4%, respectively, compared with the default PI. Beyond loop-level improvements, a Transfer Learning (TL) extension was explored: the trained SO,5 controller was directly applied to additional aerated reactors (SO,3 and SO,4) without retraining, replacing fixed aeration and demonstrating adaptability while reducing design effort. Plant-wide evaluation with the SNO,2 loop and three dissolved oxygen loops (SO,3–SO,5), all controlled by LSTM-based PI controllers, under storm influent conditions, showed further reductions in the Effluent Quality Index (EQI) and the Overall Cost Index (OCI) by 0.84% and 1.47%, respectively, highlighting simultaneous gains in effluent quality and operational economy. Additionally, the actuator and energy analyses showed that the LSTM-based controllers produced realistic and smooth control signals, maintained consistent energy use, and ensured stable overall operation, confirming the practical feasibility of the proposed approach. Full article
(This article belongs to the Special Issue Advanced Technology and Applications of Artificial Intelligence in Wastewater Treatment)
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Review

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54 pages, 5068 KB  
Review
Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
by Florin-Stefan Zamfir, Madalina Carbureanu and Sanda Florentina Mihalache
Appl. Sci. 2025, 15(15), 8360; https://doi.org/10.3390/app15158360 - 27 Jul 2025
Cited by 7 | Viewed by 6241
Abstract
The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) [...] Read more.
The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. This research aims to study the ML and DL systematic applications in optimizing the wastewater treatment processes from an industrial plant, such as the modeling of complex physical–chemical processes, real-time monitoring and prediction of critical wastewater quality indicators, chemical reactants consumption reduction, minimization of plant energy consumption, plant effluent quality prediction, development of data-driven type models as support in the decision-making process, etc. To perform a detailed analysis, 87 articles were included from an initial set of 324, using criteria such as wastewater combined with ML, DL, and artificial intelligence (AI), for articles from 2010 or newer. From the initial set of 324 scientific articles, 300 were identified using Litmaps, obtained from five important scientific databases, all focusing on addressing the specific problem proposed for investigation. Thus, this paper identifies gaps in the current research, discusses ML and DL algorithms in the context of optimizing wastewater treatment processes, and identifies future directions for optimizing these processes through data-driven methods. As opposed to traditional models, IA models (ML, DL, hybrid and ensemble models, digital twin, IoT, etc.) demonstrated significant advantages in wastewater quality indicator prediction and forecasting, in energy consumption forecasting, in temporal pattern recognition, and in optimal interpretability for normative compliance. Integrating advanced ML and DL technologies into the various processes involved in wastewater treatment improves the plant systems’ predictive capabilities and ensures a higher level of compliance with environmental standards. Full article
(This article belongs to the Special Issue Advanced Technology and Applications of Artificial Intelligence in Wastewater Treatment)
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