One of the main issues in thermoplastic polymer production plants is fouling in reactors. It happens when undesired materials build up on the reactor surfaces, decreasing the effectiveness of heat transfer and raising operating expenses. It is possible to avoid excessive accumulation, enhance reactor performance, and more efficiently schedule maintenance by accurately forecasting the fouling factor over time. In order to predict the fouling factor using time series data, we created a fusion-based deep learning model in this study that incorporates LSTM, GRU, and Attention mechanism. The proposed model is designed to capture both long-term dependencies and shortterm variations, while the Attention mechanism helps focus on critical time steps. Using actual data from a PE-EVA industrial plant from Versalis SpA, we trained and evaluated the model. The results showed strong performance, with an R2 score of 0.87, MSE of 4.74 10-3, RMSE of 0.04691, and SMAPE of 12.53%. Our model outperformed the traditional time series models. These findings show that combining different deep learning models improves the accuracy and reliability of fouling factor forecasts.
