A long sequence NOx emission prediction model for rotary kilns based on transformer

Abstract

Time-series prediction is of great practical value in industrial scenarios such as rotary kilns, especially for long sequence time-series prediction. Accurate long sequence NOx emission predictions help us monitor rotary kiln operations in advance to plan and control NOx emissions according to emission policies and production requirements. However, in actual industrial scenarios, the NOx emission pattern is dominated by long-term trends rather than simply repetitive patterns. Existing NOx prediction models are not effective in capturing long-term dependencies. Therefore, this paper proposes a novel model based on Transformer to solve this problem. First, we propose a novel series decomposition architecture based on LSTM and self-attention, which is embedded inside the Transformer. The architecture allows self-attention at the sub-series level and provides short-term trend and position information. In addition, the model designs a one-step inference structure to improve the error accumulation phenomenon under traditional inference methods for long sequence prediction and reduce the inference time. We conducted extensive experiments on two real-world datasets with different sampling intervals, which validated the model’s effectiveness. It achieves a relative improvement of 53.2% and 43.4% in prediction accuracy compared to popular NOx emission prediction methods.