Data-driven approaches are extensively employed to model complex chemical engineering processes

such as hydrotreating

to address the challenges of mechanism-based methods demanding deep process understanding. However

the development of such models requires specialized expertise in data science

limiting their broader application. Large language models (LLMs)

such as GPT-4

have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4

through well-engineered prompts

facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range)

including refined diesel and refined gas oil

using feedstock properties

operating conditions

and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications

and the designed prompts hold promise for adaptation to other complex processes.