AI-Driven and Digital Systems Engineering for Optimization of Complex Environmental and Industrial Processes

Dear Colleagues,

In this Special Issue, we invite academics, researchers, PhD students, and industry professionals to contribute original work in the field of Artificial Intelligence and Digital Systems engineering, with applications in the optimization of complex and highly nonlinear environmental and industrial processes. The subject of this Special Issue focuses on recent advances in Artificial Intelligence-driven and Digital Systems engineering for complex environmental and industrial processes modeling, control, optimization, and monitoring. Its primary goal is to explore how Artificial Intelligence techniques (machine learning-ML, deep learning-DL, fuzzy logic, artificial neural networks-ANNs, neuro-fuzzy systems-ANFIS, knowledge-based systems-KBS, expert systems) can be effectively combined with digital systems (IoT-enabled sensor networks and software-intensive architectures). This integration employs engineering principles like algorithm design, modeling, simulation, control, monitoring, optimization, structured/object-oriented programming-OOP, and web programming to address complex environmental and industrial processes, including wastewater treatment processes (pH neutralization, activated sludge, coagulation), environmental quality management (water and air), hybrid intelligent control, and industrial process automation, all addressed through integrated monitoring, diagnosis, prediction, and control.

The Special Issue goal is to emphasize the integrated nature of modern engineered systems, where Artificial Intelligence and Digital Systems infrastructures are brought under the same umbrella (rather than being used as independent methods) within a unified framework, with real benefits in complex, high-nonlinear, and dynamic processes modeling, control, simulation, optimization, and decision-making.

Topics of interest include (but are not limited to) the following:

• Fuzzy logic, ANNs, and ANFIS for intelligent monitoring and control of industrial processes;
• Artificial Intelligence-Driven and Digital Systems Engineering for complex environmental and industrial processes, parameter prediction, modeling, and control;
• ML and DL-based data-driven models for prediction, modeling, and optimization of complex environmental and industrial processes;
• Expert systems and KBS for decision-making in complex environmental and industrial processes;
• IoT-enabled sensor networks for real-time monitoring and data acquisition in environmental and industrial systems;
• Algorithm design and software engineering for Digital Systems development;
• Artificial Intelligence-based techniques and Digital Systems for environmental quality monitoring, diagnosis, and prediction;
• Hybrid and intelligent control strategies for complex, highly nonlinear, and dynamic industrial processes;
• Digital Systems and cyber-physical systems for industrial process automation and control;
• Web-based platforms and cloud infrastructures for remote monitoring, data processing, and optimization of environmental processes;
• Computer vision and DL applications for monitoring and analysis of environmental and industrial processes;
• Unified computational intelligence frameworks integrating Artificial Intelligence and Digital Systems systems engineering for complex environmental and industrial processes optimization.

This Special Issue, in the editor's opinion, extends current literature by addressing gaps between Artificial Intelligence approaches, Digital Systems design, and environmental engineering applications, providing a unified and integrated system framework, fostering interdisciplinary research that generates robust, adaptive, and extensible solutions in order to deal with the multiple challenges from complex environmental and industrial processes.

We look forward to receiving your contributions.

Dr. Madalina Cǎrbureanu
Dr. Florin Stefan Zamfir
Guest Editors

Manuscript Submission Information

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems 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.

• artificial intelligence
• digital systems engineering
• machine learning
• deep learning
• software engineering
• intelligent control
• internet of things
• optimization
• complex environmental processes
• computational intelligence frameworks