Forecasting the Required Quantity of Cement Manufacturing Materials Using Time Series and Q-Network Techniques

In the era of Industry 4.0, accurate prediction of industrial process parameters is essential for optimising operations, lowering costs, and enhancing product quality. Traditional statistical methods often struggle to capture the complex temporal dependencies within industrial processes. This study explores the use of Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Q-Network models to predict material quantities in an industrial dataset. The dataset was pre-processed to address missing values and outliers, and the models were evaluated based on Mean Squared Error (MSE), R², and accuracy. The results show that the LSTM model achieved an MSE of 14.253 and an R² of 0.700. The BiLSTM model greatly outperformed it, with an MSE of 0.714 and an R² of 0.985. The Q-Network model produced an MSE of 0.005 and an R² of 0.992. These findings demonstrate the Q-Network’s superior ability to capture temporal dependencies within the data.