Applications for Predicting Cracking Outlet Temperatures for Ethylene Cracker Furnaces Based on the GAC-BiLSTM-AM Method

In order to achieve a good generalization ability across different prediction tasks and ensure the reliability of prediction results, this paper proposes a GAC-BiLSTM-AM prediction method based on ensemble learning. This method employs a dual-channel convolutional neural network to comprehensively extract and integrate sample features, which are then input into a composite analysis module consisting of a bidirectional long short-term memory, an attention mechanism, and a fully connected layer to achieve in-depth analysis and processing of information, jointly constructing an efficient base learner. Meanwhile, during the parameter iteration tuning process of the base learner, a mistake correction mechanism is introduced to dynamically adjust sample weights in order to correct potential attention biases, and further combined with the gold rush optimizer to optimize the key structural parameters of the model. The application evaluation in multiple benchmark prediction tasks confirms that the GAC-BiLSTM-AM method possesses excellent generalization performance.