Innovative Strategies and Emerging Technologies in Wastewater Treatment

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Environmental and Green Processes".

Deadline for manuscript submissions: 25 October 2025 | Viewed by 221

Special Issue Editor


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Guest Editor
Laboratory of Biotechnology, Bioresources and Bioinformatics (3 Bio), Higher School of Technology of Khenifra, Sultan Moulay Slimane University, Beni Mellal, Morocco
Interests: materials synthesis; photocatalysis; wastewater treatment; response surface methodology; zeolites, clays
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Special Issue Information

Dear Colleagues,

Wastewater treatment plays a vital role in environmental protection and water sustainability, addressing challenges such as industrial discharge, emerging contaminants, and increasing global water demand. Traditional treatment methods are being enhanced with advanced and integrated approaches, incorporating physical, chemical, and biological techniques. Recent developments focus on cost-effective, energy-efficient, and environmentally friendly solutions to improve pollutant removal and resource recovery.

This Special Issue on “Innovative Strategies and Emerging Technologies in Wastewater Treatment” invites high-quality contributions on cutting-edge research and technological advancements in wastewater management. Topics of interest include, but are not limited to, the following:

  • Advanced oxidation processes (AOPs);
  • Nanomaterials and hybrid materials for water purification;
  • Membrane-based separation technologies;
  • Bioremediation and microbial treatment processes;
  • Electrochemical and photocatalytic methods;
  • Resource recovery (nutrients, metals, energy);
  • Process modeling, optimization, and AI-driven treatment solutions;
  • Sustainable and decentralized wastewater treatment systems.

We welcome original research, reviews, and case studies highlighting novel approaches and practical implementations for improved wastewater management.

Dr. Abdelali El Gaidoumi
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

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

  • wastewater treatment
  • advanced oxidation processes (AOPs)
  • nanomaterials for water purification
  • membrane separation technologies
  • bioremediation and microbial treatment
  • electrochemical water treatment
  • photocatalysis for wastewater remediation
  • resource recovery and circular economy
  • process modeling and optimization
  • sustainable and decentralized treatment systems

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Published Papers (1 paper)

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Research

17 pages, 2729 KiB  
Article
Intelligent Effluent Management: AI-Based Soft Sensors for Organic and Nutrient Quality Monitoring
by Fathima Reneeth, Tabassum-Abbasi, Tasneem Abbasi and S. A. Abbasi
Processes 2025, 13(6), 1664; https://doi.org/10.3390/pr13061664 - 26 May 2025
Viewed by 54
Abstract
Modular wastewater treatment units in large residential complexes in India’s crowded cities often lack stringent monitoring due to cost constraints and limited technical manpower. Although these plants must meet effluent standards, testing often requires sending samples to external labs, causing delays and added [...] Read more.
Modular wastewater treatment units in large residential complexes in India’s crowded cities often lack stringent monitoring due to cost constraints and limited technical manpower. Although these plants must meet effluent standards, testing often requires sending samples to external labs, causing delays and added costs. As a result, they are rarely monitored, risking improper effluent discharge. Quick, cost-effective assessments of effluent quality could significantly improve plant operation and maintenance. Addressing the special challenges faced by such wastewater treatment systems, artificial intelligence (AI)-based soft sensors and virtual instruments have been developed to forecast effluent quality with the help of a water quality parameter that is inexpensively, easily, and immediately measurable with a hand-held device. In this study, advanced artificial neural network (ANN)-based soft sensors were developed to enhance the monitoring and management of effluent quality in five modular wastewater treatment plants in Bangalore. The models serve as virtual instruments for the measurement of total suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), using the wastewater turbidity as the input parameter. By using these AI models, operators can better anticipate and manage water quality, ultimately contributing to more efficient and effective wastewater treatment operations. This innovative approach represents a significant advancement in wastewater treatment technology providing a practical and efficient solution to streamline monitoring and enhance overall plant performance. Full article
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