mlsanityData to 7 Oct 2026

Large Language Model Agent for User-friendly Chemical Process Simulations

arXiv 2601.11650 ↗First posted 15 Jan 2026physics.chem-ph cs.AIcited by 5

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Abstract (arXiv)

Modern process simulators enable detailed process design, simulation, and optimization; however, constructing and interpreting simulations is time-consuming and requires expert knowledge. This limits early exploration by inexperienced users. To address this, a large language model (LLM) agent is integrated with AVEVA Process Simulation (APS) via Model Context Protocol (MCP), allowing natural language interaction with rigorous process simulations. An MCP server toolset enables the LLM to communicate programmatically with APS using Python, allowing it to execute complex simulation tasks from plain-language instructions. Two water-methanol separation case studies assess the framework across different task complexities and interaction modes. The first shows the agent autonomously analyzing flowsheets, finding improvement opportunities, and iteratively optimizing, extracting data, and presenting results clearly. The framework benefits both educational purposes, by translating technical concepts and demonstrating workflows, and experienced practitioners by automating data extraction, speeding routine tasks, and supporting brainstorming. The second case study assesses autonomous flowsheet synthesis through both a step-by-step dialogue and a single prompt, demonstrating its potential for novices and experts alike. The step-by-step mode gives reliable, guided construction suitable for educational contexts; the single-prompt mode constructs fast baseline flowsheets for later refinement. While current limitations such as oversimplification, calculation errors, and technical hiccups mean expert oversight is still needed, the framework's capabilities in analysis, optimization, and guided construction suggest LLM-based agents can become valuable collaborators.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 24 of its 59 references that are in the map. First posted on arXiv 15 Jan 2026.

Builds on

24 of 59 references.

References · 24

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
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12 more
  1. 2025A Survey on Code Generation with LLM-based Agentscited by 104
  2. 2024ChemLLM: A Chemical Large Language Modelcited by 100
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Modelscited by 3
  2. 2026Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulationscited by 2
  3. 2026Schema-Bound LLM Control of Scientific Instrumentation through Model Context Protocol Skillscited by 0
  4. 2026CRAFTS: Collaborative Role-Adaptive Fine-Tuning of LLM Agents for Chemical Process Simulationcited by 0
  5. 2026Post-Edit Re-Verification in Simulator-Backed Engineering Agents: A Controlled Comparison of Verification-Cadence Guidancecited by 0

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  3. 2026From Text to Simulation: A Multi-Agent LLM Workflow for Automated Chemical Process Design
  4. 2025Multi-agent systems for chemical engineering: A review and perspective
  5. 2025Solving Context Window Overflow in AI Agents
  6. 2024Validation of the Scientific Literature via Chemputation Augmented by Large Language Models
  7. 2025MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools
  8. 2025Large Language Models for Water Distribution Systems Modeling and Decision-Making