Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network

Abstract

A new method is required to address the challenge of predicting process parameters in high-temperature, high-pressure industrial processes. This study proposes a multi-model Long Short-Term Memory (LSTM) network prediction algorithm with irregular time interval sequences to predict the silicon yield in converter steelmaking. The experimental results demonstrate that this algorithm performs better than comparable neural network models in classifying high-dimensional, redundant industrial production data with noise and outliers. The algorithm is evaluated using data from a steel plant. The proposed algorithm has lower errors for predicting the alloy yield than other neural network models. An average mean absolute error (MAE) of less than 0.01 confirms the algorithm's feasibility and practicality.