Fundamentals and Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Photo- and Electrochemical-Based 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: 10 September 2026 | Viewed by 121
Special Issue Editors
Interests: designing novel reactor configurations for the electrochemical degradation of pollutants; applying simulation, modeling, optimization, and control to green processes; integrating circular economy principles to recover valuable resources; conducting techno-economic analysis (TEA) of physical and chemical removal processes
Special Issues, Collections and Topics in MDPI journals
Interests: wastewater treatment through electrolysis; simulation and modeling of green processes
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We invite you to contribute to a Special Issue of the journal Processes. It is entitled as follows: Fundamentals and Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Photo- and Electrochemical-Based Wastewater Treatment.
The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) is driving transformative innovation across scientific disciplines. In the field of advanced wastewater treatment, these computational tools offer unprecedented potential for enhancing prediction accuracy, optimizing processes, enabling intelligent automation, and accelerating material discovery.
This Special Issue aims to curate cutting-edge research that bridges AI/ML/DL methodologies with photo- and electrochemical advanced oxidation processes. We seek to showcase work that fundamentally improves efficiency, understanding, design, and control of next-generation water remediation systems.
Scope
We welcome original research articles and comprehensive reviews that explore the application of intelligent computational systems to advance sustainable wastewater treatment technologies. Relevant topics include, but are not limited to, the following:
- Prediction and Optimization: Using AI, ML, and DL to predict, model, and optimize the performance of photo- and electrochemical treatment systems.
- Physics-Informed Machine Learning (PIML): Developing hybrid models that integrate physical laws with data-driven approaches for environmental reaction systems.
- ML for Process Modeling: Employing ML techniques for solving governing ordinary and partial differential equations in process simulation.
- Process Control and Automation: Implementing intelligent systems for real-time monitoring, adaptive control, and automation of photo- and electrochemical processes.
- AI-Driven Material Design: Accelerating the discovery and design of novel photo- and electrocatalytic materials through AI-aided methods.
- Device and System Design: Applying AI to the design, scaling, and integration of photo- and electrochemical devices and reactors.
- Digital Twins: Creating and utilizing digital twins for simulation, prediction, and optimization of photo- and electrochemical processes.
We believe this collection will serve as a valuable resource for researchers and practitioners, fostering the development of smarter, more efficient, and sustainable solutions for water purification.
We look forward to receiving your outstanding contributions.
Prof. Dr. Alejandro Regalado-Méndez
Prof. Dr. Ever Peralta Reyes
Guest Editors
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 250 words) can be sent to the Editorial Office for assessment.
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 semimonthly 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
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Deep Learning (DL)
- process prediction and optimization
- intelligent process control and automation
- photo- and electrocatalytic materials
- photo- and electrochemical devices
- wastewater treatment
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
