A Perspective on Artificial Intelligence for Process Manufacturing
Process manufacturing (PM) plays a vital role in chemical, biochemical, and related industries by transforming raw materials into products. Yet, it faces complex challenges such as managing continuous or batch operations, maintaining quality control, and ensuring safety. Artificial intelligence (AI) has emerged as a valuable tool to tackle these issues more efficiently.
This article discusses how AI, especially hybrid AI that blends machine learning (ML) with symbolic AI based on first principles, can enhance process systems engineering (PSE) methods and tools. The goal is to create smarter solutions that address key areas within process manufacturing.
Key Areas Where AI Impacts Process Manufacturing
- Chemical Product Design: AI aids in computer-aided molecular and mixture design. Improvements in representing molecular structures and predicting properties help speed up product development.
- Process Synthesis and Design: Hybrid AI methods assist in identifying optimal processing routes and designs. Sustainability and efficiency are important criteria being factored into these AI-driven approaches.
- Process Control and Monitoring: AI techniques such as neural networks and reinforcement learning support process control. Challenges remain around safety, system stability, and adapting to changing operational conditions.
- Process Safety and Hazards: AI reduces the effort and time required for hazard analysis and risk identification, improving overall plant safety management.
Looking Forward: Challenges and Opportunities
There’s still work ahead to fully realize AI’s potential in process manufacturing. Key challenges and opportunities include:
- Chemical Product Design: Better use of chemical libraries, faster computational algorithms, and enhanced hybrid AI approaches to handle complexity are needed.
- Process Synthesis and Design: Building unified databases of process flowsheets, integrating sustainability factors more deeply, and improving the synergy between optimization methods and hybrid AI are essential steps.
- Process Control and Monitoring: Developing AI that adapts to operational changes, manages limited feedback, incorporates diverse measurement data, and supports AI-augmented control algorithms will be critical.
- Process Safety and Hazards: Creating databases of hazardous chemicals, advancing language models for safety analysis, and better integrating hazard data into AI tools are top priorities.
The future lies in developing AI-augmented PSE tools that seamlessly transfer data into model-based simulations and optimization techniques. This will help ensure safer, more reliable, and efficient decision-making in process manufacturing.
For product development professionals, understanding these AI-driven advances in process manufacturing can open new paths to innovate and improve efficiency across the product lifecycle.
For those interested in expanding their AI skills relevant to manufacturing and product development, exploring specialized AI courses can be valuable. Check out Complete AI Training for courses tailored to different skill levels and job roles.
Read the full open access paper for detailed insights: A Perspective on Artificial Intelligence for Process Manufacturing.
Your membership also unlocks:
