LLM-Powered Copilot for Aspen Plus: Enabling Human–AI Collaborative Process Design

Process simulation software such as Aspen Plus has become indispensable in chemical engineering design, yet the translation of engineering intent into software operations remains a significant barrier for practitioners. This study presents a Large Language Model (LLM)-powered Copilot framework that integrates large language models (Claude Sonnet 4.5) with the Model Context Protocol (MCP) to enable human–AI collaborative process design. Unlike fully automated approaches, the proposed Copilot paradigm positions engineers as decision-makers while delegating routine software operations to AI assistants. The framework comprises 42 modular tools organized into 7 functional categories, covering the complete simulation workflow from model initialization to result analysis. A hierarchical Skills knowledge system was developed to guide AI operations and reduce hallucination errors. The effectiveness of the framework was validated through three case studies: (1) water–ethanol binary distillation achieving consistent semantic accuracy across six independent runs with ±0.1% purity deviation after fine-tuning; (2) pressure-swing distillation where the Copilot proactively identified thermodynamic limitations of the azeotrope system and autonomously optimized to 97.86 wt % ethanol; and (3) literature-based isopropyl alcohol (IPA) extractive distillation reconstruction with full structure fidelity and <1% energy duty error. The results demonstrated that the Copilot achieved high structural accuracy in model construction while substantially reducing model construction time compared to manual operations. The human–AI collaborative approach maintained engineering oversight while lowering the expertise barrier for process simulation. These findings highlight the potential of LLM-powered Copilots to transform chemical process design by enabling engineers to focus on design objectives rather than software mechanics.