The Influence of Particle Swarm Optimization-Back Propagation Neural Network Hyperparameter Selection on the Prediction Accuracy of Converter Endpoint Temperature

The converter is a complex, high temperature, high pressure reactor with limited internal moitoring. At present, data-driven models mainly focus on the prediction differences between algorithms, and there is relatively little analysis of the impact of different hyperparameters on prediction accuracy. Taking a 120 t converter in a Chinese steel plant as an example, this paper explores the application of particle swarm optimization-back propagation neural network (PSO-BP) in converter temperature prediction. First, the Pauta criterion or Box plot method was used to preprocess the data by prescreening. Subsequently, the influence of the activation function, learning rate, and number of hidden layer nodes of BP on the prediction accuracy of the endpoint temperature were explored. Then we investigated the influence of PSO parameters on the optimal result of BP initial value. Comparing the temperature prediction hit rate before and after optimization, the BP model has hit rates of 63.64%, 79.22%, and 87.45% within +/- 10, +/- 15, and +/- 20 degrees C, respectively, and the PSO-BP model has hit rates of 68.40%, 84.85%, and 94.81%, respectively. In comparison, PSO-BP extracts data features more accurately, has higher stability, and has better accuracy in predicting the endpoint temperature of the converter. This article establishes a particle swarm optimization-back propagation neural network (PSO-BP) model for predicting converter endpoint temperature, explores the influence of hyperparameters on the accuracy of PSO-BP prediction, reveals the principle of PSO for BP, and obtains the optimal parameter selection scheme for the model. Data validation confirms PSO-BP's effectiveness in extracting data features and achieving high prediction accuracy.image (c) 2024 WILEY-VCH GmbH