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Processes
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Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0
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Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 5885

Special Issue Editors

Dr. Lee Tin Sin

E-Mail Website
Guest Editor
Department of Chemical Engineering, Universiti Tunku Abdul Rahman, Kajang, Malaysia
Interests: biodegradable polymers; extrusion; polymer characterizations; polymer composites; polymer nanocomposites; rubber insulation
Prof. Dr. Thomas S.Y. Choong

E-Mail Website
Guest Editor
Department of Chemical and Environmental Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Interests: adsorption; modeling and simulation; biofuel; environmental engineering; circular economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Process systems engineering (PSE) plays a crucial role in enhancing the efficiency and sustainability of industrial systems by employing advanced methodologies to optimize processes. PSE allows for the meticulous design, modeling, and control of complex industrial systems, ensuring that every component operates at peak efficiency. With the advent of artificial intelligence (AI), these capabilities have been significantly augmented. AI-driven modeling and analysis tools enable more accurate predictions, better decision-making, and seamless integration of various subsystems, leading to improved overall performance. By leveraging AI, PSE can now tackle increasingly complex challenges, making it possible to manage and optimize large-scale industrial operations with unprecedented precision and efficiency.

In parallel, the global push towards carbon reduction has intensified the focus on environmental considerations within industrial operations. As industries are being held to stricter environmental standards, there is a growing need to evaluate and optimize processes not just for economic gain but also for their environmental impact. This holistic approach requires the analysis of industrial systems through multiple approaches, including energy consumption, waste generation, and emissions. PSE provides the tools to perform such multi-faceted analyses, enabling industries to balance economic performance with environmental responsibility. By integrating these methodologies, PSE not only helps industries to meet their carbon reduction targets but also ensures that these efforts are economically viable and technologically feasible. This systemic approach is essential for driving the transition towards more sustainable industrial practices while maintaining competitiveness in a rapidly evolving market.

The latest research in this intriguing field was presented and discussed at the 2024 PSE Asia International Symposium (https://www.pseasia2024.org/)held in Penang, Malaysia, on 5-8 August 2024. The PSE Asia series is a biennial international symposium in Asia, bringing together researchers and practitioners to discuss recent developments in Process Systems Engineering. PSE Asia 2024 is the 11th symposium, following previous events in Kyoto (2000), Taipei (2002), Seoul (2005), Xi’an (2007), Singapore (2010), Kuala Lumpur (2013), Tokyo (2016), Bangkok (2019), Taipei (2020), and Chennai (2022). This Special Issue is a reflection of the high-quality papers presented at the 2024 PSE ASIA International Symposium. This Special Issue aims to showcase the most recent advances in process dynamics and control, product and process design, supply chain management, PSE for circular economy/sustainable processes, PSE for process safety and operations,  process integration and optimization, artificial intelligence and big data, and education in process systems engineering and industry applications.

Dr. Lee Tin Sin
Prof. Dr. Thomas S.Y. Choong
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. 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

  • process dynamics
  • product and process design
  • process integration and optimization
  • artificial intelligence
  • big data
  • education
  • supply chain management
  • circular economy
  • sustainability
  • process safety

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Published Papers (8 papers)

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Research

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20 pages, 4164 KiB  
Article
MAL-XSEL: Enhancing Industrial Web Malware Detection with an Explainable Stacking Ensemble Model
by Ezz El-Din Hemdan, Samah Alshathri, Haitham Elwahsh, Osama A. Ghoneim and Amged Sayed
Processes 2025, 13(5), 1329; https://doi.org/10.3390/pr13051329 - 26 Apr 2025
Viewed by 107
Abstract
The escalating global incidence of malware presents critical cybersecurity threats to manufacturing, automation, and industrial process control systems. Given the fast-developing web applications and IoT devices in use by industry operations, securing a transparent and effective malware detection mechanism has become imperative to [...] Read more.
The escalating global incidence of malware presents critical cybersecurity threats to manufacturing, automation, and industrial process control systems. Given the fast-developing web applications and IoT devices in use by industry operations, securing a transparent and effective malware detection mechanism has become imperative to operational resilience and data integrity. Classical methods of malware detection are conventionally opaque “black boxes” with limited transparency, thus eroding trust and hindering deployment in security-sensitive contexts. In this respect, this research proposes MAL-XSEL—a malware detection framework using an explainable stacking ensemble learning approach for performing high-accuracy classification and interpretable decision-making. MAL-XSEL explicates the model predictions through Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), which enable security analysts to validate how the detection logic works and prioritize the features contributing to the most critical threats. Evaluated on two benchmark datasets, MAL-XSEL outperformed conventional machine learning models, achieving top accuracies of 99.62% (ClaMP dataset) and 99.16% (MalwareDataSet). Notably, it surpassed state-of-the-art algorithms such as LightGBM (99.52%), random forest (99.33%), and decision trees (98.89%) across both datasets while maintaining computational efficiency. A unique interaction of ensemble learning and XAI is employed for detection, not only with improved accuracy but also with interpretable insight into the behavior of malware, thereby allowing trust to be substantiated in an automated system. By closing the divide between performance and interpretability, MAL-XSEL enables cybersecurity practitioners to deploy transparent and auditable defenses against an ever-growing resource of threats. This work demonstrates how there can be no compromise on explainability in security-critical applications and, as such, establishes a roadmap for future research on industrial malware analysis tools. Full article
(This article belongs to the Special Issue Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0)
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21 pages, 652 KiB  
Article
Trends in Sustainable Inventory Management Practices in Industry 4.0
by Silvia Carpitella and Joaquín Izquierdo
Processes 2025, 13(4), 1131; https://doi.org/10.3390/pr13041131 - 9 Apr 2025
Viewed by 689
Abstract
This study examines 52 recently published papers on sustainable inventory management in Industry 4.0, intending to bridge theory and practice through a comprehensive literature review. By analyzing the latest advancements discussed over the past two years, covering 2024 and 2025, we identify key [...] Read more.
This study examines 52 recently published papers on sustainable inventory management in Industry 4.0, intending to bridge theory and practice through a comprehensive literature review. By analyzing the latest advancements discussed over the past two years, covering 2024 and 2025, we identify key trends shaping the field and highlight existing gaps that may require further exploration. Focusing on this time frame is particularly relevant because it reflects how companies have recently started using artificial intelligence more practically to support sustainability goals. During these years, AI has been applied to improve how inventory is tracked, how demand is predicted, and how resources are managed to reduce waste. These tools are making supply chains more efficient while helping organizations to lower their environmental impact. In this regard, our work aims to provide a deeper understanding of how sustainable strategies are evolving in response to technological innovations, offering insights for researchers and practitioners seeking to enhance efficiency and environmental responsibility in modern supply chains. Full article
(This article belongs to the Special Issue Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0)
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18 pages, 7627 KiB  
Article
Prototype of a Multimodal Platform Including EEG and HRV Measurements Intended for Neuroergonomics Applications
by Awad M. Aljuaid
Processes 2025, 13(4), 1074; https://doi.org/10.3390/pr13041074 - 3 Apr 2025
Viewed by 369
Abstract
Drowsiness and stress greatly influence worker health and productivity and workplace safety. Conflict between workplace expectations and employee control results in stress, which causes mental and physical reactions that affect performance and raise the risk of accidents at work. A common precursor to [...] Read more.
Drowsiness and stress greatly influence worker health and productivity and workplace safety. Conflict between workplace expectations and employee control results in stress, which causes mental and physical reactions that affect performance and raise the risk of accidents at work. A common precursor to inadvertent drowsiness increases workplace risks and costs due to lost productivity and accidents. Developments in the interdisciplinary subject of neuroergonomics enable the creation of novel systems to track and minimize these issues. This work introduces prototype testing to demonstrate the system’s ability to detect stress and drowsiness. Along with other indicators such as body temperature, heart rate (HR), and SpO2 levels, the system incorporates electroencephalography (EEG) and heart rate variability (HRV). By analyzing these biosignals, the system detects stress and drowsiness in real time, providing alerts to both users and the supervisor’s BI dashboard. The design is flexible, offering two wearable forms: a headband and an armband. Prototype testing demonstrates the system’s ability to detect stress and drowsiness effectively, paving the way for safer and more productive workplace environments. Full article
(This article belongs to the Special Issue Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0)
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30 pages, 2270 KiB  
Article
Implementation of a Sustainable Framework for Process Optimization Through the Integration of Robotic Process Automation and Big Data in the Evolution of Industry 4.0
by Leonel Patrício, Leonilde Varela and Zilda Silveira
Processes 2025, 13(2), 536; https://doi.org/10.3390/pr13020536 - 14 Feb 2025
Viewed by 812
Abstract
This study explores the integration of Robotic Process Automation (RPA) and Big Data within a sustainable framework for process optimization in the context of Industry 4.0. As industries strive to enhance operational efficiency while maintaining sustainability, the need for innovative solutions has become [...] Read more.
This study explores the integration of Robotic Process Automation (RPA) and Big Data within a sustainable framework for process optimization in the context of Industry 4.0. As industries strive to enhance operational efficiency while maintaining sustainability, the need for innovative solutions has become crucial. The research applies the PICO methodology (Population, Intervention, Comparison, Outcome) to assess the impact of combining these technologies on process optimization and sustainability. Through a real-world case study, the study demonstrates that the integration of RPA and Big Data significantly reduces execution times, minimizes operational errors, and promotes sustainable business practices. The results show that the combined framework not only enhances efficiency but also contributes to lower economic, environmental, and social impacts. The findings validate the research hypotheses, proving that the proposed framework fosters a balance between technological advancement and sustainability. This study provides valuable insights into the potential of Industry 4.0 technologies to drive both operational efficiency and corporate responsibility, offering a novel approach for industries seeking to embrace digital transformation while achieving long-term sustainability. The research contributes to the growing body of knowledge on the synergy between RPA, Big Data, and sustainability in industrial contexts. Full article
(This article belongs to the Special Issue Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0)
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28 pages, 5156 KiB  
Article
Esterification of Kenaf Core Fiber as a Potential Adsorbent for Oil Removal from Palm Oil Mill Effluent (POME)
by Nor Halaliza Alias, Luqman Chuah Abdullah, Thomas Choong Shean Yaw, Siti Nurul Ain Md Jamil, Teo Ming Ting, Ahmad Jaril Asis, Chuan Li Lee and Abel Adekanmi Adeyi
Processes 2025, 13(2), 463; https://doi.org/10.3390/pr13020463 - 8 Feb 2025
Viewed by 516
Abstract
Palm oil mill effluent (POME) is a major contributor to industrial oily wastewater in Malaysia, demanding effective treatment solutions. This study explores the potential of esterified kenaf core (EKC) fiber as an oil adsorbent for oil removal from POME, optimized using a full [...] Read more.
Palm oil mill effluent (POME) is a major contributor to industrial oily wastewater in Malaysia, demanding effective treatment solutions. This study explores the potential of esterified kenaf core (EKC) fiber as an oil adsorbent for oil removal from POME, optimized using a full central composite design (CCD) within the response surface methodology (RSM) framework. The optimum conditions achieved 76% oil removal efficiency, with a 1:0.5 ratio of mercerized kenaf core to stearic acid (MKC:SA), 15 wt% of catalyst, and 1 h reflux time during the esterification process. The regression model exhibited strong predictive capability, with a significant quadratic correlation and an R2 value of 0.94. The Fourier transform infrared (FTIR) spectroscopy revealed the existence of ester functional groups characterized by significant hydrophobicity and a decrease in hydroxyl groups, indicating the chemical changes of EKC. Moreover, the scanning electron microscopy (SEM) research demonstrated structural alterations in EKC, including heightened surface roughness, fibrillation, and pore development, which improved oil adhesion relative to raw kenaf core (RKC). These findings indicate that EKC provides an effective, environmentally sustainable solution for managing oil wastewater issues in the palm oil sector, facilitating enhanced ecological sustainability and resource management. Full article
(This article belongs to the Special Issue Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0)
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18 pages, 2849 KiB  
Article
A Two-Level Facility Layout Design Method with the Consideration of High-Risk Facilities in Chemical Industries
by Guanxin Xu, Siyu Xu and Yufei Wang
Processes 2025, 13(1), 161; https://doi.org/10.3390/pr13010161 - 9 Jan 2025
Viewed by 1202
Abstract
Understanding facility layout design in chemical industries requires multi-disciplinary knowledge and experience. The recent work mainly focuses on improving safety and calculating the efficiency of the design. However, in chemical industries, facilities are always located in frames, so both facility layout and frame [...] Read more.
Understanding facility layout design in chemical industries requires multi-disciplinary knowledge and experience. The recent work mainly focuses on improving safety and calculating the efficiency of the design. However, in chemical industries, facilities are always located in frames, so both facility layout and frame layout should be considered in the design, as well as safety. Such a situation has not been well studied. In this work, facilities are divided into several frames and then placed in a fixed area. The risk resources located in the frames and out of the frames are both contained, and the safety distances are compliant with relative regulations. Optimization and some heuristic rules are applied to obtain the layout of each frame and the whole plant. Moreover, fire embankments are considered to achieve a more realistic and reasonable final layout. As a result, compared with the initial one, the actual and potential safety factors and the reasonable degree of the factory layout are both improved. The total costs are reduced by 7.38 × 104 $·a−1. Through these steps, the effectiveness of the proposed approach is proven. Full article
(This article belongs to the Special Issue Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0)
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Review

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57 pages, 14508 KiB  
Review
Artificial Intelligence in Manufacturing Industry Worker Safety: A New Paradigm for Hazard Prevention and Mitigation
by Minahil Khurram, Catherine Zhang, Shalahudin Muhammad, Hitesh Kishnani, Kimi An, Kalana Abeywardena, Utkarsh Chadha and Kamran Behdinan
Processes 2025, 13(5), 1312; https://doi.org/10.3390/pr13051312 - 25 Apr 2025
Viewed by 172
Abstract
The phenomenal rise of artificial intelligence (AI) in the last decade, and its evolution as a versatile addition to various fields, necessitates its usage for novel purposes in multidimensional fields like the manufacturing industry. Even though AI has been rigorously studied for process [...] Read more.
The phenomenal rise of artificial intelligence (AI) in the last decade, and its evolution as a versatile addition to various fields, necessitates its usage for novel purposes in multidimensional fields like the manufacturing industry. Even though AI has been rigorously studied for process optimization, wastage reduction, and other quintessential aspects of the manufacturing industry, there has been limited focus on worker safety as a theme in the current literature. Safety standards contribute to worker safety, but there is no one-size-fits-all approach in these standards or policies, which warrants evaluation and integration of new ideas and technologies to reach the closest to ideal standards. This includes but is not limited to health, regulation of operations, predictive maintenance, and automation and control. The rise of Industry 4.0 and the migration towards Industry 5.0 facilitate easy integration of advanced technologies like AI into the manufacturing industry with real-time predictive capabilities, and this can help reduce human errors and mitigate hazards in processes where sensitivity is crucial or hazards are frequent. Keeping the future outlook in focus, AI can contribute to training workers in risk-free environments, promote engineering education for easy adaptation to new technology, and reduce resistance to changes in the industry. Furthermore, there is an urgent need for standards and regulations to govern and integrate AI technologies judiciously into the manufacturing industry, which holds AI models and their creators accountable for their decisions. This could further extend to preventing the adversarial use of new technology. This study exhaustively discusses the potential and ongoing contributions of this technology to the safety of workers in the manufacturing industry. Full article
(This article belongs to the Special Issue Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0)
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53 pages, 5282 KiB  
Review
A Comparative Review of IG-541 System Use in Total Flooding Application for Energized Electrical Fire
by Kheng Hooi Loo, Tin Sin Lee and Soo Tueen Bee
Processes 2025, 13(2), 485; https://doi.org/10.3390/pr13020485 - 10 Feb 2025
Viewed by 1468
Abstract
Clean agent fire suppression systems are commonly used to protect areas containing valuable or critical equipment, especially in data centers and electrical substations, where traditional fire suppression methods are less effective or pose additional risks. This review evaluates the IG-541 fire suppression system [...] Read more.
Clean agent fire suppression systems are commonly used to protect areas containing valuable or critical equipment, especially in data centers and electrical substations, where traditional fire suppression methods are less effective or pose additional risks. This review evaluates the IG-541 fire suppression system as an alternative to halocarbon-based agents like HFC-227ea and FK-5-1-12, which are being phased out under environmental regulations, focusing on their application in energized electrical fires. IG-541 offers environmental advantages, including zero ozone depletion potential, no global warming potential, and negligible atmospheric lifetime, making it compliant with stringent environmental regulations. This review compares IG-541 with halocarbon agents across parameters such as extinguishing efficacy, safety considerations, environmental impacts, cost impacts, and system design considerations. Key findings underscore IG-541’s effectiveness in reducing fire damage without producing harmful by-products or exacerbating climate change. Furthermore, the study highlights the regulatory frameworks influencing the phase-out of halocarbon agents and the transition toward environmentally sustainable alternatives. While IG-541 emerges as a promising replacement for halocarbon agents, further exploration into its application in varied fire scenarios and energy-intensive environments is recommended to optimize its deployment. Full article
(This article belongs to the Special Issue Process Systems Engineering-Incubating Sustainability for Industrial Revolution 4.0)
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