A multimode structured prediction model based on dynamic attribution graph attention network for complex industrial processes

Abstract

Complex industrial processes with dynamic and time-varying characteristics, as well as diverse operating conditions, pose challenges in developing accurate real-time online prediction models for the key performance indicator (KPI). To address these challenges, a KPI prediction framework is proposed in this study, which utilizes a multimode structured prediction model based on a dynamic-attribution graph attention network (DAGAT). First, a multimode clustering method based on a Gaussian mixture model is designed to divide and locate industrial multimode operational data. Second, to address the limitations of traditional deep network models in accurately capturing the complex relationships between process variables and KPI, a graph structure is used to model multi-sensor time series data. Subsequently, a novel DAGAT is proposed to improve node prediction performance and interpretation of industrial graph data. Considering the cumulative effect of large-scale equipment, the DAGAT incorporates knowledge of the industrial process, enabling it to capture the influence of chemical-reaction variables. Simultaneously, the Huber loss function is used to further optimize the prediction results. Moreover, the model can calculate in real-time the contribution of each individual process variable to KPI, providing guidance for optimal control. Finally, the effectiveness and superiority of the proposed method are validated through an industrial case.