Framework for merging AI and Human Intelligence
In the past few years, artificial intelligence (AI) has experience significant growth, allowing industries to automate processes and boost operational efficiency.
A comprehensive feature in AIChE journal delineates the advantages and obstacles associated with implementing Intelligence Augmentation (IA) in process safety systems.
This work includes contributions form Dr. Faisal Khan, department head and professor of chemical engineering at Texas A&M University, Dr. Strotos Pistikopoulos, director of the Energy Institute and professor, and Docs. Rajeevan Arunthavanthan, Tanjin Amin and Zaman Sajid from the Mary Kay O'Connor Safety Center.
Dr. Yuhe Tian of West Virginia University also provided a unique perspective on integrating AI into process plants with a focus on safety.
According to Khan, the research focuses on employing AI for process safety in conjunction with human operators, rather than substituting them in decision-making process.
Arunthavanathan explained, "Our research seeks to establish a robust framework leveraging Intelligence Augmentation (AI) to merge AI with Human Intelligence (HI) in process safety systems, there by boosting safety and operational efficiency. Our goal is to elucidate the capabilities and constraints of AI, and to propose IA strategies for effective risk mitigation and safety enhancement."
Supporting human roles, rather than substituting them
According to Amin, the research analyzes the hurdles of deploying AI technology in industrial contexts and evaluates how IA can advance process monitoring, fault detection, and decision-making, thereby enhancing process safety.
According to Khan, AI's ability to analyze real-time data, forecast maintenance requirements, and detect faults autonomously will bolster safety. Additionally, employing an IA strategy that integrates human decision-making is expected to lower incident rates, cut operational costs, and increase system reliability.
According to Sajid, the challenges associated with applying AI in chemical engineering imply that it cannot fully guarantee process safety on its own. To mitigate these issues, IA is employed to support human expertise rather than substitute it.
This research identifies key risk related to the integration of AI and Intelligence Augmentation (IA) in process industries. For AI, the risks include concerns about data quality, excessive reliance on AI systems, inadequate contextual comprehension, model misinterpretation, and challenges with training and adaption. Meanwhile, IA-related risks involve potential human error in feedback, disagreements in AI-human decision-making, bias, complexity in execution, and issues with reliability.
According to Tian, the researchers are deeply interested in addressing the challenges posed by AI and envisioning IA kas a means to support human decision-making in process safety. they are captivated by AI's capacity for accurate and rapid responses based on data analysis, alongside the broader insights and contextual understanding that human intelligence can provide, including ethical and social dimensions.
According to Khan, this study highlights the critical need for developing AI systems that are reliable, credible, and safe, with a focus on their suitability for industrial settings.
According to Khan, the partnership between AI and human intelligence is vital for improving process safety. Pistikopoulos remarked that persistent exploration of this collaboration will not only bolster AI's functionality but also ensure that establishment of effective risk management systems for evolving industrial safety needs.
Further details: Rajeevan Arunthavanathan et al. explore the role of AI and IA in enhancing process safety in their article, 'Process Safety 4.0: Artificial Intelligence or Intelligence Augmentation for Safer Process Operation?' published in AIChE Journal (2024). DOI: 10.1002/aic.18475
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