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AI in Wastewater Treatment
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AI 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 November 2025 | Viewed by 2448

Special Issue Editor

Dr. George Besseris

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of West Attica, Athens, Greece
Interests: product development; product design and development; design engineering; mechanical processes; creativity and innovation; sustainability; optimization; production; production engineering; operations management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water is a vital resource for human survival and development. Sustainable water resources and water environment are a matter of human health and economic prosperity. Therefore, optimizing the recycling of water resources and ensuring water supply safety is essential for human development. Rapid advances in artificial intelligence technology are providing new ideas and methods to achieve this goal. The deep learning of collected data, analysis of sewage treatment patterns, and prediction and control of wastewater treatment quality can effectively improve the stability and accuracy of the wastewater treatment process. As a result, this technology can provide intelligent support for the wastewater treatment and control process.

This Special Issue aims to address the topics of applying Nature-inspired and machine learning techniques such as swarm intelligence, genetic algorithms and random forests to wastewater recycling quality by examining the effects of various influences on relevant water quality indices in applications that range from crop production improvement to effective environmental predictions in pollution control. Works on recurrent neural networks, wavelet neural networks, Elman neural networks, deep neural networks, support vector machines, fuzzy logic and adaptive neuro fuzzy inference systems, classification/clustering-based algorithms and other supervised, semi-supervised and unsupervised learning algorithms are also welcomed. Control studies using quantum walker approaches and dynamic non-linear autoregressive networks in predicting effluent quality variability are particularly desired. Case studies may also feature the prediction of optimal long-term wastewater treatment operations, the quantification of the effects of effluent trace metals on COD, dissolved oxygen optimization, the control of salt accumulation, the enhancement of filtration performance, the minimization of conductivity and membrane fouling as well as the maximization of flux in osmotic bioreactors. Other processes such as membrane distillation, electrodialysis, and micro-, ultra-, and nanofiltration-based operations may be also illustrated, among other relevant topics.

Dr. George Besseris
Guest Editor

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Published Papers (3 papers)

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Research

21 pages, 3297 KiB  
Article
Machine Learning Methods for the Prediction of Wastewater Treatment Efficiency and Anomaly Classification with Lack of Historical Data
by Igor Gulshin and Olga Kuzina
Appl. Sci. 2024, 14(22), 10689; https://doi.org/10.3390/app142210689 - 19 Nov 2024
Viewed by 337
Abstract
This study examines an algorithm for collecting and analyzing data from wastewater treatment facilities, aimed at addressing regression tasks for predicting the quality of treated wastewater and classification tasks for preventing emergency situations, specifically filamentous bulking of activated sludge. The feasibility of using [...] Read more.
This study examines an algorithm for collecting and analyzing data from wastewater treatment facilities, aimed at addressing regression tasks for predicting the quality of treated wastewater and classification tasks for preventing emergency situations, specifically filamentous bulking of activated sludge. The feasibility of using data obtained under laboratory conditions and simulating the technological process as a training dataset is explored. A small dataset collected from actual wastewater treatment plants is considered as the test dataset. For both regression and classification tasks, the best results were achieved using gradient-boosting models from the CatBoost family, yielding metrics of SMAPE = 9.1 and ROC-AUC = 1.0. A set of the most important predictors for modeling was selected for each of the target features. Full article
(This article belongs to the Special Issue AI in Wastewater Treatment)
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32 pages, 4909 KiB  
Article
Non-Linear Saturated Multi-Objective Pseudo-Screening Using Support Vector Machine Learning, Pareto Front, and Belief Functions: Improving Wastewater Recycling Quality
by George Besseris
Appl. Sci. 2024, 14(21), 9971; https://doi.org/10.3390/app14219971 - 31 Oct 2024
Viewed by 457
Abstract
Increasing wastewater treatment efficiency is a primary aim in the circular economy. Wastewater physicochemical and biochemical processes are quite complex, often requiring a combination of statistical and machine learning tools to empirically model them. Since wastewater treatment plants are large-scale operations, the limited [...] Read more.
Increasing wastewater treatment efficiency is a primary aim in the circular economy. Wastewater physicochemical and biochemical processes are quite complex, often requiring a combination of statistical and machine learning tools to empirically model them. Since wastewater treatment plants are large-scale operations, the limited opportunities for extensive experimentation may be offset by miniaturizing experimental schemes through the use of fractional factorial designs (FFDs). A recycling quality improvement study that relies on non-linear multi-objective multi-parameter FFD (NMMFFD) datasets was reanalyzed. A published NMMFFD ultrafiltration screening/optimization case study was re-examined regarding how four controlling factors affected three paper mill recycling characteristic responses using a combination of statistical and machine learning methods. Comparative machine learning screening predictions were provided by (1) quadratic support vector regression and (2) optimizable support vector regression, in contrast to quadratic linear regression. NMMFFD optimization was performed by employing Pareto fronts. Pseudo-screening was applied by decomposing the replicated NMMFFD dataset to single replicates and then testing their replicate repeatability by introducing belief functions that sought to maximize credibility and plausibility estimates. Various versions of belief functions were considered, since the novel role of the three process characteristics, as independent sources, created a high level of conflict during the information fusion phase, due to the inherent divergent belief structures. Correlations between two characteristics, but with opposite goals, may also have contributed to the source conflict. The active effects for the NMMFFD dataset were found to be the transmembrane pressure and the molecular weight cut-off. The modified adjustment was pinpointed to the molecular weight cut-off at 50 kDa, while the optimal transmembrane pressure setting persisted at 2.0 bar. This mixed-methods approach may provide additional confidence in determining improved recycling process adjustments. It would be interesting to implement this approach in polyfactorial wastewater screenings with a greater number of process characteristics. Full article
(This article belongs to the Special Issue AI in Wastewater Treatment)
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30 pages, 4844 KiB  
Article
Datacentric Similarity Matching of Emergent Stigmergic Clustering to Fractional Factorial Vectoring: A Case for Leaner-and-Greener Wastewater Recycling
by George Besseris
Appl. Sci. 2023, 13(21), 11926; https://doi.org/10.3390/app132111926 - 31 Oct 2023
Cited by 1 | Viewed by 944
Abstract
Water scarcity is a challenging global risk. Urban wastewater treatment technologies, which utilize processes based on single-stage ultrafiltration (UF) or nanofiltration (NF), have the potential to offer lean-and-green cost-effective solutions. Robustifying the effectiveness of water treatment is a complex multidimensional characteristic problem. In [...] Read more.
Water scarcity is a challenging global risk. Urban wastewater treatment technologies, which utilize processes based on single-stage ultrafiltration (UF) or nanofiltration (NF), have the potential to offer lean-and-green cost-effective solutions. Robustifying the effectiveness of water treatment is a complex multidimensional characteristic problem. In this study, a non-linear Taguchi-type orthogonal-array (OA) sampler is enriched with an emergent stigmergic clustering procedure to conduct the screening/optimization of multiple UF/NF aquametric performance metrics. The stochastic solver employs the Databionic swarm intelligence routine to classify the resulting multi-response dataset. Next, a cluster separation measure, the Davies–Bouldin index, is used to evaluate input and output relationships. The self-organized bionic-classifier data-partition appropriateness is matched for signatures between the emergent stigmergic clustering memberships and the OA factorial vector sequences. To illustrate the proposed methodology, recently-published multi-response multifactorial L9(34) OA-planned experiments from two interesting UF-/NF-membrane processes are examined. In the study, seven UF-membrane process characteristics and six NF-membrane process characteristics are tested (1) in relationship to four controlling factors and (2) to synchronously evaluate individual factorial curvatures. The results are compared with other ordinary clustering methods and their performances are discussed. The unsupervised robust bionic prediction reveals that the permeate flux influences both the UF-/NF-membrane process performances. For the UF process and a three-cluster model, the Davies–Bouldin index was minimized at values of 1.89 and 1.27 for the centroid and medoid centrotypes, respectively. For the NF process and a two-cluster model, the Davies–Bouldin index was minimized for both centrotypes at values close to 0.4, which was fairly close to the self-validation value. The advantage of this proposed data-centric engineering scheme relies on its emergent and self-organized clustering capability, which retraces its appropriateness to the fractional factorial rigid structure and, hence, it may become useful for screening and optimizing small-data wastewater operating conditions. Full article
(This article belongs to the Special Issue AI in Wastewater Treatment)
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