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Abstract This study investigates the application of various neural network-based models for predicting temperature distribution in freeze drying process of biopharmaceuticals. For heat-sensitive biopharmaceutical products, freeze drying is preferred to prevent degradation of pharmaceutical compounds. The modeling framework is based on CFD (Computational Fluid Dynamics) and machine learning (ML). The ML models explored include the Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Fully Connected Neural Network (FCNN), and Deep Neural Network (DNN). Model optimization is achieved through the Fireworks Algorithm (FWA). Results reveal promising performance across all models, with the MLP demonstrating the highest accuracy on both test and training datasets, achieving an R2 score of 0.99713 and 0.99717 respectively. The SLP also exhibits strong performance, with an R2 of 0.88903 on the test dataset. The FCNN and DNN models also perform admirably, achieving R2 scores of 0.99158 and 0.99639 on the test dataset respectively. These results highlight the efficiency of neural network-driven models, specifically the MLP, in precisely forecasting temperature values based on spatial coordinates. Additionally, the integration of the Fireworks Algorithm for model refinement yields advantages in improving the predictive performance of these models.

This study investigates the application of various neural network-based models for predicting temperature distribution in freeze drying process of biopharmaceuticals. For heat-sensitive biopharmaceutical products, freeze drying is preferred to prevent degradation of pharmaceutical compounds. The modeling framework is based on CFD (Computational Fluid Dynamics) and machine learning (ML). The ML models explored include the Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Fully Connected Neural Network (FCNN), and Deep Neural Network (DNN). Model optimization is achieved through the Fireworks Algorithm (FWA). Results reveal promising performance across all models, with the MLP demonstrating the highest accuracy on both test and training datasets, achieving an R2 score of 0.99713 and 0.99717 respectively. The SLP also exhibits strong performance, with an R2 of 0.88903 on the test dataset. The FCNN and DNN models also perform admirably, achieving R2 scores of 0.99158 and 0.99639 on the test dataset respectively. These results highlight the efficiency of neural network-driven models, specifically the MLP, in precisely forecasting temperature values based on spatial coordinates. Additionally, the integration of the Fireworks Algorithm for model refinement yields advantages in improving the predictive performance of these models. [ABSTRACT FROM AUTHOR]