ArticleScientific reports2025
Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning analysis and simulation of pharmaceutical drying process based on prediction of concentration distribution and mass transfer.Scientific reports · 2025Article
- Recent Advances and Future Perspectives on Heat and Mass Transfer Mechanisms Enhanced by Preformed Porous Media in Vacuum Freeze-Drying of Agricultural and Food Products.Foods (Basel, Switzerland) · 2025Review
- Optimization of the Vacuum Microwave Drying of Tilapia Fillets Using Response Surface Analysis.Foods (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
Funding
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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 R
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