Recent perspective articles published in engineering We explore the application of artificial intelligence (AI) in process manufacturing (PM), exploring ways to integrate AI with process systems engineering (PSE) methods and tools to address various challenges in the field.
A key activity in chemistry, biochemistry and related engineering, PM involves converting raw materials into products. However, it faces many complex issues, including continuous or batch operations, quality control, and safety hazards. AI has the ability to deliver innovative solutions and is attracting a lot of attention. This paper focuses on the concept of hybrid AI, combining machine learning (ML) methods with first principles-based symbolic AI methods to create more powerful tools for PSE.
The author first defines four important topics within the PM. Chemical product design, process synthesis and design, process control and monitoring, and process safety and danger. Next, check the current state of AI applications in these areas. In chemical product design, AI is used in the design of computer-aided molecules or mixtures, with advances in molecular structural representation and characterization prediction. Hybrid AI approaches have been developed to find the optimal processing route and design, taking into account sustainability and other criteria for process synthesis and design. Although issues such as system safety and stability remain in process control and monitoring, methods such as neural network modeling and reinforcement learning (RL) have been adopted. Regarding process safety and dangers, AI reduces the time and effort of process hazard analysis and helps identify potential risks.
In the future, this paper outlines several challenges and opportunities. Chemical product design requires better use of chemical libraries, more efficient computational algorithms, and improved handling of complexity with hybrid AI. In process synthesis and design, it is important to integrate process flowsheets with integrated databases, integration of sustainability into flowsheet development, and enhanced integration of optimization-based methods and hybrid AI. For process control and monitoring, adapting to changes in operational conditions, processing limited feedback signals, incorporating a variety of measurement signals, and implementing AI-Aigmented Control algorithms are key focus areas. In the safety and danger of processes, it is essential to create a database of dangerous chemicals, develop better language models, and integrate risk and safety issues more effectively.
AI shows promises in PM, but there is still a lot to do. Developing Ai-Aigmented PSE tools that can efficiently transfer data to model-based process simulation and optimization techniques is necessary for obstacle-free decision making in PM. This research provides valuable insights to engineers and researchers working in this field and guides future efforts to leverage AI for more sustainable and efficient process manufacturing.
“A Perspective on Artificial Intelligence in Process Manufacturing” written by Vipul Mann, Jingyi Lu, Venkat Venkatasubramanian and Rafiqul Gani. Full Open Access Paper: https://doi.org/10.1016/j.eng.2025.01.014. For more information engineeringplease visit the https://www.sciencedirect.com/journal/engineering website.
