AI-Driven Digital Twin for Process Safety in Chemical Engineering
A special issue of ChemEngineering (ISSN 2305-7084).
Deadline for manuscript submissions: 31 August 2026 | Viewed by 1986
Special Issue Editors
Interests: process safety and risk engineering; life cycle assessment; sustainable development; biofuels; biomass technologies; system designing; climate change mitigation; environmental impacts; biodiesel; energy policy development; process economics; process optimization
Special Issues, Collections and Topics in MDPI journals
Interests: AI safety and security; process safety; OT cybersecurity
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As chemical industries increasingly adopt Artificial Intelligence (AI) and related technologies, the potential for improved efficiency, safety, and operational excellence is extensive. However, these advancements also bring significant safety and security challenges. This special issue focuses on addressing these critical challenges, examining both the risks and opportunities of applying AI-driven technologies in chemical engineering and process safety.
AI technologies, including digital twins, dynamic risk analysis, and immersive tools like augmented and virtual reality, are transforming the way chemical industries operate. These innovations enable predictive maintenance, real-time hazard identification, and scenario-based risk simulations. At the same time, they introduce safety concerns such as algorithmic reliability, data integrity, and system robustness, as well as cybersecurity vulnerabilities that could compromise critical operations.
This special issue aims to provide a platform for exploring the safety and security dimensions of AI-driven technologies, highlighting the technical, ethical, and regulatory challenges. Contributions are invited that critically analyze these issues and propose practical solutions for safe and secure implementation.
Topics of Interest
Submissions are welcome on topics including, but not limited to:
Safety Challenges:
- Algorithmic reliability and robustness in dynamic risk modeling.
- Addressing data quality, bias, and validation in AI-powered systems.
- Managing the implications of system errors or failures in real-time monitoring.
- Ethical considerations and unintended safety consequences of AI in process safety.
Security Challenges:
- Cybersecurity vulnerabilities in AI-enhanced digital twin systems.
- Safeguarding sensitive operational data in interconnected industrial environments.
- Resilience against cyber-physical attacks in automated process safety systems.
Applications and Use Cases:
- Fault detection and scenario-based risk simulations.
- Predictive maintenance strategies and process optimization.
- Case studies on implementing secure and reliable AI-driven digital twins in chemical industries.
- AI frameworks for emergency response and operator decision-making in high-risk environments.
Emerging Concerns:
- Socio-technical and organizational challenges in adopting AI for process safety.
- Addressing gaps in regulations, standards, and governance for AI-driven systems.
- Exploring limitations of immersive technologies (AR/VR) for safety training and hazard identification.
Call for Contributions
This special issue encourages original research, case studies, reviews, and technical discussions that critically explore the dual aspects of safety and security in AI-driven technologies for the chemical industry. The goal is to provide actionable insights and frameworks to enhance the responsible deployment of these transformative tools.
Dr. Zaman Sajid
Dr. Rajeevan Arunthavanathan
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. ChemEngineering 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 1800 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
- AI in chemical industries
- process safety
- cybersecurity
- digital twins
- dynamic risk analysis
- ethical AI
- system validation
- data integrity
- AR/VR for safety
- risk mitigation
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