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Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 1807

Special Issue Editors

Dr. Filipe Pereira

E-Mail Website
Guest Editor
1. INEGI–Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
2. MEtRICs Research Center, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
Interests: automation and industrial control; Industry 4.0; mechatronics; artificial intelligence; industrial, mobile and colaborative robots and industrial network protocols and advanced communication networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carlos Felgueiras

E-Mail Website
Guest Editor
CIETI, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal
Interests: energy quality; energy and buildings; energy and environmental indicators; renewable energy systems; sustainable energy systems; engineering education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vítor Carvalho

E-Mail Website
Guest Editor
1. The Applied Artificial Intelligence Laboratory (2Ai) of the School of Technology (EST), Polytechnic Institute of Cávado and Ave (IPCA), 4750-810 Barcelos, Portugal
2. Algoritmi R&D Centre, Minho University, 4710-057 Braga, Portugal
Interests: sensors; data acquisition; serious games; education; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro M. B. Torres

E-Mail Website1 Website2
Guest Editor
1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. SYSTEC—Research Center for Systems and Technologies, ARISE—Advanced Production and Intelligent Systems Associated Laboratory, 4200-465 Porto, Portugal
Interests: electronics; instrumentation; automation; control; robotics; cyber-physical systems; computer vision; image processing and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of artificial intelligence (AI) in industrial environments is revolutionizing production, automation, and process management, surpassing the limits of Industry 4.0 and paving the way for Industry 5.0. This Special Issue invites original research, case studies, and review articles that present AI applications in diverse industrial areas. Topics of interest include AI-driven predictive maintenance, quality control, autonomous optimization, vision-based inspection, mobile robotics and smart navigation in industrial environments, the development of smart sensors for industrial AI, automation and control based on neural networks and deep learning, and finally AI for energy management and industrial energy efficiency. Additionally, we encourage applications with an focusing on integrating AI into cyber–physical systems, digital twins, collaborative and autonomous robotics, and industrial internet of things (IIoT) frameworks, highlighting how these advances drive smart, data-driven manufacturing.

Dr. Filipe Pereira
Prof. Dr. Carlos Felgueiras
Prof. Dr. Vítor Carvalho
Dr. Pedro M. B. Torres
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 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. Applied Sciences 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

  • industrial artificial intelligence
  • Industry 4.0
  • Industry 5.0
  • predictive maintenance
  • quality control
  • cyber–physical systems
  • collaborative robotics
  • digital twins
  • IIoT
  • smart factories

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Published Papers (3 papers)

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Research

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20 pages, 1274 KiB  
Article
Development and Optimization of an Automated Industrial Wastewater Treatment System Using PLC and LSTM Neural Network
by Žydrūnas Kavaliauskas, Giedrius Blažiūnas, Igor Šajev, Aleksandras Iljinas and Dovilė Gimžauskaitė
Appl. Sci. 2025, 15(16), 8990; https://doi.org/10.3390/app15168990 (registering DOI) - 14 Aug 2025
Abstract
This study presents an automated industrial wastewater treatment system based on Siemens programmable logic controller (PLC) that optimizes reagent dosing, aeration, sedimentation, and sludge separation. The system uses accurate pH sensors, dosing pumps, solenoid valves, and a human–machine interface (HMI), and real-time monitoring [...] Read more.
This study presents an automated industrial wastewater treatment system based on Siemens programmable logic controller (PLC) that optimizes reagent dosing, aeration, sedimentation, and sludge separation. The system uses accurate pH sensors, dosing pumps, solenoid valves, and a human–machine interface (HMI), and real-time monitoring is provided by a Teltonika TRB255 communication module (<45 sec. response time). As a result, the treatment cycle time was reduced by 31%, reagent consumption by 30%, and operator intervention was reduced from 95 to less than 15 min per day, achieving a pollutant removal efficiency of 89%. A two-layer LSTM architecture developed on the PyTorch platform predicts pH (6.7–7.7), temperature (12–20 °C), and reagent consumption (~9.8 kg/cycle). The model was trained with 240 h of data (64 neurons, learning rate 0.001). The validation loss remained stable, indicating reliable learning. The study confirms that AI-based automation provides greater process stability, meets environmental standards, and promotes sustainable resource use. The scientific novelty of this study is the application of an advanced long short-term memory (LSTM) model to predict wastewater treatment process parameters, allowing for accurate prediction of pH, temperature, flow, and reagent consumption, etc. This provides an opportunity to optimize the process and reduce costs, while ensuring high treatment efficiency and stability. Although there are several publications on the application of artificial intelligence models in the field of industrial wastewater treatment, this is a relatively new field, and there are little data in the scientific literature. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

Review

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17 pages, 910 KiB  
Review
A Framework for Integrating Robotic Process Automation with Artificial Intelligence Applied to Industry 5.0
by Leonel Patrício, Leonilde Varela, Zilda Silveira, Carlos Felgueiras and Filipe Pereira
Appl. Sci. 2025, 15(13), 7402; https://doi.org/10.3390/app15137402 - 1 Jul 2025
Viewed by 700
Abstract
The transition to Industry 5.0 highlights the growing integration of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in industrial ecosystems. However, adoption remains fragmented, lacking standardized frameworks to align intelligent automation with human-centric principles. While RPA improves operational efficiency and AI enhances [...] Read more.
The transition to Industry 5.0 highlights the growing integration of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in industrial ecosystems. However, adoption remains fragmented, lacking standardized frameworks to align intelligent automation with human-centric principles. While RPA improves operational efficiency and AI enhances cognitive decision-making, challenges such as organizational resistance, interoperability, and ethical governance hinder scalable and sustainable implementation. The envisioned scenario involves seamless RPA-AI integration, fostering human–machine collaboration, operational resilience, and sustainability. Expected outcomes include (1) hyperautomation for efficiency gains, (2) agile, data-driven decision-making, (3) sustainable resource optimization, and (4) an upskilled workforce focusing on innovation. This study proposes a structured five-stage framework for RPA-AI deployment in Industry 5.0, combining automation, cognitive enhancement, and human–machine symbiosis. A systematic literature review (PICO method) identifies gaps and supports the framework’s design, validated through operational, human-impact, and sustainability metrics. Incorporating ethical governance and continuous upskilling, the model ensures technological advancement aligns with societal and environmental values. Results demonstrate its potential as a roadmap for responsible digital transformation, balancing efficiency with human-centricity. Future research should focus on empirical validation and sector-specific adaptations. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)
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22 pages, 4096 KiB  
Review
AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0
by Albérico Travassos Rosário and Ricardo Jorge Gomes Raimundo
Appl. Sci. 2025, 15(13), 7264; https://doi.org/10.3390/app15137264 - 27 Jun 2025
Viewed by 452
Abstract
This study conducts a systematic bibliometric literature review to explore the conceptual and technological transition from Industry 4.0 to Industry 5.0, focusing on the roles of artificial intelligence (AI), optimization, and human values. Applying the PRISMA 2020 protocol, the analysis includes 53 peer-reviewed [...] Read more.
This study conducts a systematic bibliometric literature review to explore the conceptual and technological transition from Industry 4.0 to Industry 5.0, focusing on the roles of artificial intelligence (AI), optimization, and human values. Applying the PRISMA 2020 protocol, the analysis includes 53 peer-reviewed sources from the Scopus database, emphasizing the integration of advanced technologies such as cyber–physical systems, the Internet of Things, collaborative robotics, and explainable AI. While Industry 4.0 is marked by intelligent automation and digital connectivity, Industry 5.0 introduces a human-centric paradigm emphasizing sustainability, resilience, and co-creation. The findings underscore the significance of human–machine collaboration, process personalization, AI education, and ethical governance as foundational pillars of this new industrial era. This review highlights the emerging role of enabling technologies that reconcile technical performance with social and environmental values, promoting a more inclusive and sustainable model for industrial development. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)
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