Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84155-z |
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| author | Turki Al Hagbani Jawaher Abdullah Alamoudi Majed A. Bajaber Huda Ibrahim Alsayed Halah Jawad Al-fanhrawi |
| author_facet | Turki Al Hagbani Jawaher Abdullah Alamoudi Majed A. Bajaber Huda Ibrahim Alsayed Halah Jawad Al-fanhrawi |
| author_sort | Turki Al Hagbani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-598f0a456b664f3aa6f7c6ee92920ce1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-598f0a456b664f3aa6f7c6ee92920ce12025-01-12T12:22:39ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-84155-zTheoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distributionTurki Al Hagbani0Jawaher Abdullah Alamoudi1Majed A. Bajaber2Huda Ibrahim Alsayed3Halah Jawad Al-fanhrawi4Department of Pharmaceutics, College of Pharmacy, University of HailDepartment of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint Abdulrahman UniversityChemistry Department, Faculty of Science, King Khalid UniversityAccounting Department, Faculty of Business School, King Khalid UniversityScientific Affairs Department, Al-Mustaqbal UniversityAbstract 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.https://doi.org/10.1038/s41598-024-84155-zFreeze dryingModelingBiopharmaceuticalsTemperature distributionMulti-layer PerceptronFireworks Algorithm |
| spellingShingle | Turki Al Hagbani Jawaher Abdullah Alamoudi Majed A. Bajaber Huda Ibrahim Alsayed Halah Jawad Al-fanhrawi Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution Scientific Reports Freeze drying Modeling Biopharmaceuticals Temperature distribution Multi-layer Perceptron Fireworks Algorithm |
| title | Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution |
| title_full | Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution |
| title_fullStr | Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution |
| title_full_unstemmed | Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution |
| title_short | Theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution |
| title_sort | theoretical investigations on analysis and optimization of freeze drying of pharmaceutical powder using machine learning modeling of temperature distribution |
| topic | Freeze drying Modeling Biopharmaceuticals Temperature distribution Multi-layer Perceptron Fireworks Algorithm |
| url | https://doi.org/10.1038/s41598-024-84155-z |
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