PRTNet: Combustion State Recognition Model of Municipal Solid Waste Incineration Process Based on Enhanced Res-Transformer and Multi-Scale Feature Guided Aggregation

Accurate identification of the combustion state in municipal solid waste incineration (MSWI) processes is crucial for achieving efficient, low-emission, and safe operation. However, existing methods often struggle with stable and reliable recognition due to insufficient feature extraction capabilities when confronted with challenges such as complex flame morphology, blurred boundaries, and significant noise in flame images. To address this, this paper proposes a novel hybrid architecture model named PRTNet, which aims to enhance the accuracy and robustness of combustion state recognition through multi-scale feature enhancement and adaptive fusion mechanisms. First, a local-semantic enhanced residual network is constructed to establish spatial correlations between fine-grained textures and macroscopic combustion patterns. Subsequently, a feature-adaptive fusion Transformer is designed, which models long-range dependencies and high-frequency details in parallel via deformable attention and local convolutions, and achieves adaptive fusion of global and local features through a gating mechanism. Finally, a cross-scale feature guided aggregation module is proposed to fuse shallow detailed information with deep semantic features under dual-attention guidance. Experiments conducted on a flame image dataset from an MSWI plant in Beijing show that PRTNet achieves an accuracy of 96.29% in the combustion state classification task, with precision, recall, and F1-score all exceeding 96%, significantly outperforming numerous mainstream baseline models. Ablation studies further validate the effectiveness and synergistic effects of each module. The proposed method provides a reliable solution for intelligent flame state recognition in complex industrial scenarios, contributing to the advancement of intelligent and sustainable development in municipal solid waste incineration processes.