An LLM-Based Intelligent Agent and Its Application in Making the Lanolin Saponification Process Greener

Area of Science:

• Green Chemistry
• Chemical Engineering
• Artificial Intelligence

Background:

• Traditional batch saponification for lanolin alcohol is energy-intensive and generates substantial waste.
• Current methods conflict with green chemistry principles due to inefficiency and environmental impact.

Purpose of the Study:

• To develop an efficient and sustainable saponification process for lanolin alcohol.
• To address limitations of batch processing by integrating large language models (LLMs) with microfluidic technology.

Main Methods:

• An LLM-based intelligent agent, SapoMind (version 1.0), was developed using LLMs and a continuous-flow microreactor.
• SapoMind enabled automated parameter recommendation, experimental design, and execution for process optimization.
• Supervised fine-tuning enhanced the system's performance in optimizing saponification.

Main Results:

• Optimal continuous-flow saponification achieved at 70 °C and 9 min residence time, meeting European Pharmacopoeia standards.
• The optimized process reduced carbon emissions by 53% and solid waste by 37% compared to batch methods.
• The greenness score improved from 82 to 93, indicating enhanced sustainability.

Conclusions:

• LLM-driven intelligent agents are effective for optimizing green chemical processes.
• SapoMind demonstrates significant environmental and operational advantages for lanolin alcohol production.
• This approach represents a novel integration of AI and microfluidics for sustainable chemical manufacturing.