Prediction of liquid ammonia yield using a novel deep learning‐based heterogeneous pruning ensemble model.

Liquid ammonia yield is the main index characterizing the process output of ammonia synthesis, the prediction of which is crucial for process control and optimization. However, the industrial process of ammonia synthesis involves multiple variables and strong nonlinearity, making it difficult to be accurately predicted using conventional mechanism‐driven model, or a certain type of data‐driven model. Therefore, a deep learning‐based heterogeneous pruning ensemble (DL‐HPE) model is proposed to overcome the limitations of conventional models. In this model, a "VDiv" pruning strategy that trades off diversity and accuracy is proposed and successfully applied to select the optimal subset from nine representative base models. Finally, the deep learning is employed to integrate the outputs of the involving models to generate the final prediction. The DL‐HPE method was applied to modeling the UCI standard dataset and the historical dataset collected from an ammonia plant in China. The results show that the method is superior to single model or other ensemble models both in accuracy and in robustness. [ABSTRACT FROM AUTHOR]