A progressive fine-tuning strategy for domain-specific large language models in wastewater treatment plants safety

The management of safety in wastewater treatment plants (WWTPs) is faced with fundamental challenges, including sparse domain knowledge, dynamic evolution of safety protocols, and the necessity for highly reliable decision-making. While traditional risk assessment methods and expert systems provide essential support, they struggle to integrate multi-source heterogeneous knowledge to mitigate high-consequence, low-frequency(HCLF) risks. Existing general-purpose large language models (LLMs) demonstrate significant deficiencies in domain-specific knowledge, meanwhile, traditional fine-tuning methods are susceptible to catastrophic forgetting and knowledge conflicts during continual learning, rendering them unsuitable for direct application in this context. To address these challenges, this study proposes a progressive fine-tuning strategy to develop a domain-specific LLM tailored specifically for WWTP safety management. First, a domain-specific dataset is constructed through specialized dataset engineering. Subsequently, the proposed progressive fine-tuning strategy partitions domain knowledge into sequential stages, enabling the model to gradually learn and consolidate core knowledge at each stage before proceeding to the next. This orderly accumulation process ensures the deep integration of knowledge. The model is deployed and continuously optimized using vLLM, and direct preference optimization (DPO). The experimental results demonstrate that the progressive fine-tuning strategy effectively mitigates knowledge conflicts arising from multi-task fine-tuning. This approach not only ensures precise adherence to bottom-line safety protocols and enhances the model's depth of domain understanding in WWTP safety management, but also facilitates more coordinated capability allocation and knowledge integration across different professional tasks. By progressively refining the model, the proposed approach achieves superior task-specific performance even compared to models with substantially larger parameter scales, offering an effective and reproducible pathway for addressing analogous domain adaptation challenges.