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LLMs for Chemical Engineering

Prof. Schweidtmann (TU Delft) presents on the potential of large language models in the chemical engineering domain, discussing the challenges of integrating these models into the engineering domain and the need for more accessible and usable industry data. He also introduces a tool called digitization companion (DigiCo) for processing industry data and discussed the concept of rule-based systems and their application in safety-critical areas. The conversation ends with a discussion on the future of AI in the chemical engineering industry, the potential of combining different models, and the importance of foundational models for the chemical engineering domain. Prof. Artur M. Schweidtmann is an assistant professor of chemical engineering at Delft University of Technology (TU Delft), where he leads the Process Intelligence Research Group. His research focuses on integrating artificial intelligence (AI), machine learning (ML), and process systems engineering (PSE) to advance chemical engineering applications. This includes developing algorithms for process optimization, surrogate modeling, and molecular property prediction. Potential of Large Language Models (LLMs) in Engineering Artur discussed the rapid development of LLMs and their transformative potential in software development, machine translation, and process engineering. He emphasized that engineering knowledge is often encoded in diverse data formats, making full LLM integration an ongoing challenge. Applications in Chemical Engineering Artur explored AI’s role in process automation, error detection in diagrams, hazard report generation, and data-driven engineering assistance. He stressed that AI should support, not replace, engineers and noted limitations in AI-generated chemical engineering visuals. Challenges in Integrating AI Key challenges include the gap between general-purpose AI performance and industrial co-pilots, limited structured data, and heterogeneous, unmachine-readable datasets. Artur emphasized integrating mechanistic knowledge into AI and leveraging underutilized data. Data Limitations in the Chemical Industry Artur highlighted difficulties in research due to limited access to confidential industry data, reliance on patents and publications, and the need for tools like the digitization companion to extract and structure process flow sheet data for AI applications. Machine Learning for Flow Sheets Artur suggested encoding flow sheets as graphs, knowledge structures, or strings for LLM compatibility. He demonstrated a generative AI model for auto-correction in engineering applications, emphasizing the balance between rule-based systems and AI. AI and Safety-Critical Systems Artur discussed rule-based systems for safety applications, using graph-based inference to detect and correct errors. He introduced a supervised learning transformer model but noted its limited accuracy due to a lack of contextual understanding. Future of AI in Chemical Engineering Artur and John explored multi-agent AI systems, industrial collaboration, and the potential for AI-enhanced chemical databases. Artur noted the industry’s conservative stance on data sharing and intellectual property but suggested openness might increase over time. Model Combination for Process Engineering They discussed integrating multiple specialized AI agents (e.g., for reaction pathways, reactor design) and incorporating time-series data for improved chemical engineering workflows. They highlighted the need for multimodal models and foundational models tailored to the field. The meeting concluded with optimism about AI’s adoption in the next 1-3 years.