The rise of artificial intelligence (AI) has revolutionized industries by streamlining operations and enhancing efficiency. In the realm of process industries, AI plays a pivotal role in creating intelligent models to predict and monitor equipment failures, reduce downtime, and optimize maintenance schedules.
Despite the potential for AI to replace human intervention, concerns about safety in process industries arise. Thus, a collaborative approach between AI and humans, known as intelligence augmentation (IA), is essential. A feature article in the AIChE Journal addresses the challenges and benefits of incorporating Intelligence Augmentation (IA) into process safety systems.
The article features contributions from Dr. Faisal Khan, Professor and Head of the Chemical Engineering Department at Texas A&M University, Dr. Stratos Pistikopoulos, Professor and Director of the Energy Institute, as well as Drs. Rajeevan Arunthavanathan, Tanjin Amin, and Zaman Sajid from the Mary Kay O’Connor Safety Center. In addition, Dr. Yuhe Tian from West Virginia University provides a unique perspective on incorporating AI into process plants, focusing on safety.
According to Khan, the research aims to utilize an AI-based approach to enhance process safety in collaboration with humans rather than replacing human involvement in operational decision-making.
“This research aims to develop a comprehensive framework based on IA that integrates AI and Human intelligence (HI) into process safety systems, ensuring enhanced safety and efficiency,” Arunthavanathan said. “We aim to provide a clear understanding of the potential and limitations of AI, propose IA strategies for their effective implementation to minimize risks and improve safety outcomes.”
Khan is a strong proponent of integrating AI and human intelligence, dispelling concerns about AI replacing humans as it advances. Amin’s study delves into the challenges of implementing AI in real-world industrial applications and how it can enhance process monitoring, fault detection, and decision-making for improved safety.
Khan argues that AI can enhance safety through real-time data analysis, predictive maintenance, and automated fault detection. However, leveraging human decision-making, the IA approach is also projected to decrease incident rates, reduce operational costs, and boost reliability.
“The application of AI in chemical engineering presents significant challenges, which means it is not enough to ensure comprehensive process safety,” Sajid said. “To overcome these limitations, IA is introduced to work alongside human expertise rather than replace it.”
According to the research, implementing AI and IA in process industries poses several risks. These include data quality issues, overreliance on AI, lack of contextual understanding, model misinterpretation, and AI training and adaptation challenges. As for IA, the risks involve human error in feedback, conflict in AI-HI decision-making, biased judgment, complexity in implementation, and reliability issues.
“The researchers are particularly interested in the challenges of AI and conceptualize IA to augment human decision-making in process safety,” Tian said. “They are fascinated by how AI can provide accurate and prompt responses based on data analysis while human intelligence can offer broader insights and considerations, including ethical and social factors.”
Khan emphasizes the significance of developing reliable, trustworthy, and safe AI systems tailored to industrial applications, as highlighted in this research.
“The collaboration between AI and human intelligence is seen as essential for advancing process safety,” Khan said. “Ongoing exploration of this synergy to meet the evolving demands of industrial safety will continue to enhance AI’s capabilities while ensuring robust risk management frameworks are in place,” Pistikopoulos added.
Journal reference:
- Rajeevan Arunthavanathan, Zaman Sajid, Md. Tanjin Amin, Yuhe Tian, Faisal Khan, Efstratios Pistikopoulos. Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation? AIChE Journal, 2024; DOI: 10.1002/aic.18475