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Processes
Special Issues
Advanced and Novel Physico-Chemical and Biological Wastewater Treatment Technologies
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Advanced and Novel Physico-Chemical and Biological Wastewater Treatment Technologies

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 390

Special Issue Editor

Prof. Dr. Eliseo Cristiani-Urbina

E-Mail Website
Guest Editor
Departamento de Ingeniería Bioquímica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Unidad Profesional Adolfo López Mateos, Avenida Wilfrido Massieu s/n, Delegación Gustavo A. Madero, Ciudad de México 07738, Mexico
Interests: environmental science; biological wastewater treatment; biosorption; industrial biotechnology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous urban, industrial, and technological development of human beings has brought many advantages and challenges. One of the challenges is the serious environmental problem of contamination of water bodies and industrial wastewater by many substances/pollutants that can harm humans, flora, and fauna, even at low concentrations. Therefore, removing pollutants from water and wastewater is crucial to meet quality standards and develop a sustainable future. 

Considering the recent development of innovative and sustainable water and wastewater treatment technologies, a Special Issue that provides a platform for exploring, discussing, and disseminating research trends in this field is needed.

This Special Issue invites the submission of original research papers, review papers, and short communications presenting and discussing the recent advancements and challenges in water and wastewater treatment technologies. These include, but are not limited to, ion exchange, adsorption, biosorption, membrane processes, electrochemical treatment, photocatalytic degradation, chemical oxidation, chemical precipitation, coagulation, heterogeneous catalysis, UV/H2O2, Fenton oxidation, activated sludge processes, aerobic/anaerobic biodegradation processes, biofilm processes, membrane bioreactors, emerging chemical treatment, and combined biological processes.

Prof. Dr. Eliseo Cristiani-Urbina
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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
  • activated sludge processes
  • aerobic/anaerobic biodegradation processes
  • biofilm processes
  • membrane bioreactors
  • emerging chemical treatment
  • combined biological processes

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

28 pages, 5707 KiB  
Article
Optimized Bi-LSTM Networks for Modeling Ni(II) Biosorption Kinetics on Quercus crassipes Acorn Shells
by Juan Crescenciano Cruz-Victoria, Erick Aranda-García, Eliseo Cristiani-Urbina and Alma Rosa Netzahuatl-Muñoz
Processes 2025, 13(4), 1076; https://doi.org/10.3390/pr13041076 - 3 Apr 2025
Viewed by 180
Abstract
Heavy metal pollution from anthropogenic sources poses significant risks to ecosystems and human health. Biosorption offers a sustainable removal method, but kinetics are poorly captured by traditional neural networks. This study introduces optimized Bidirectional Long Short-Term Memory (Bi-LSTM) networks for multivariate modeling of [...] Read more.
Heavy metal pollution from anthropogenic sources poses significant risks to ecosystems and human health. Biosorption offers a sustainable removal method, but kinetics are poorly captured by traditional neural networks. This study introduces optimized Bidirectional Long Short-Term Memory (Bi-LSTM) networks for multivariate modeling of Ni(II) biosorption on Quercus crassipes acorn shells, trained using experimental (EKD), synthetic (SKD), and combined (CKD) datasets. A two-stage hyperparameter optimization with Optuna yielded models with R2 above 0.995 and low RMSE in 5-fold cross-validation. Second-stage models showed high stability, with coefficient of variation (CoV) values below 10% for RMSE. Based on unseen kinetics, production models showed slightly lower performance (R2 = 0.89–0.996): EKD1, EKD2, and CKD1 showed the most consistent performance across challenging conditions with R2 values of 0.9617, 0.9769, and 0.9415, respectively; SKD models achieved strong results under standard conditions (kinetic 1, SKD1 R2 = 0.9963). SHapley Additive exPlanations (SHAP) analysis identified contact time and initial Ni(II) concentration as key variables, with temperature, cation charge, and a salt interference code also contributing to model interpretability. These findings demonstrate that optimized Bi-LSTM networks offer a robust and interpretable data-driven solution for modeling Ni(II) removal under multivariate conditions, including the presence of salts. Full article
(This article belongs to the Special Issue Advanced and Novel Physico-Chemical and Biological Wastewater Treatment Technologies)
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