Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design

TL;DR

This paper presents an autonomous AI framework that leverages web retrieval and knowledge graphs to automate the creation of regulation-compliant process diagrams, accelerating manufacturing scale-up from material discovery.

Contribution

It introduces a novel agentic system combining multimodal data retrieval and graph-based knowledge synthesis for automated process diagram generation.

Findings

Successfully generates regulation-compliant diagrams with minimal expert input

Demonstrates high accuracy in open-domain question answering tasks

Enhances industrial process design efficiency

Abstract

Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of precise and regulation-compliant diagrams remains a significant challenge, particularly in scaling breakthroughs from material discovery to industrial production in an era of automation and digitalization. This paper introduces an autonomous agentic framework to address these challenges through a twostage approach involving knowledge acquisition and generation. The framework integrates specialized sub-agents for retrieving and synthesizing multimodal data from publicly available online sources and constructs ontological knowledge graphs using a Graph Retrieval-Augmented Generation (Graph RAG) paradigm. These capabilities enable the automation of diagram generation and open-domain question answering (ODQA) tasks with high contextual accuracy. Extensive empirical experiments demonstrate the frameworks ability to deliver regulation-compliant diagrams with minimal expert intervention, highlighting its practical utility for industrial applications.