Design and Optimization of Heat Exchangers Using Large Language Models

Sandeep Mishra, Vishal Sudam Jadhav, Shirish Karande, Venkataramana Runkana

Keywords: Heat Exchangers, Design and Optimization, Large Language Models, Retrieval Augmented Generation

TL;DR: The HxLLM framework uses Large Language Models (LLMs) and retrieval augmented generation (RAG) to automate and enhance the design and optimization of heat exchangers, improving efficiency and cost-effectiveness in process industries.

Abstract: Heat exchangers (HEs) are essential in process industries for efficient thermal energy transfer. Their design and optimization are crucial for improving energy efficiency, reducing costs, and ensuring reliable system performance. However, these tasks are complex due to varying fluid properties, phase changes, and fouling. This study proposes the HxLLM framework, utilizing Large Language Models (LLMs) to aid in the design and optimization of HEs. The framework identifies the mathematical model for heat transfer in HEs, followed by retrieval-augmented generation (RAG) based code generation and correction. In this study, a repository was created by extracting mathematical models from relevant literature along with common errors observed in such tasks. These repositories, combined with carefully crafted prompts, were used to extract the mathematical model and generate the corresponding code within this framework. We observed that LLMs can effectively identify and generate initial code for mathematical models, though first responses often needed corrections. The RAG approach for code correction significantly enhanced code accuracy. This study demonstrates that LLMs, with a RAG framework, can automate and improve the design and optimization process of HEs, offering a promising tool for engineers and researchers to achieve better efficiency and cost-effectiveness.

Submission Number: 12