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 May 2024 | Viewed by 965

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Department of Mechanical Engineering, University of West Attica, Athens, Greece
Interests: mechanical processes; optimization; chemical engineering
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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 (1 paper)

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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
Viewed by 576
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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