Computational and intelligence modeling analysis of pharmaceutical freeze drying for prediction of temperature in the process

Abstract

Accurate temperature prediction is crucial for various scientific and engineering applications, yet it remains challenging due to the complex relationships between spatial coordinates and temperature variations. This study addresses this challenge by exploring advanced machine learning models to improve prediction accuracy, filling the research gap in optimizing predictive models for temperature estimation in freeze drying of pharmaceuticals. We employed Support Vector Regression (SVR), Poisson Regression (POR), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models, each enhanced using the Cheetah Optimizer (CO), to estimate temperature based on spatial coordinates (X, Y, Z) in the domain of process. The methodology involved training and testing these models on a dataset comprising spatial and temperature data, with a focus on improving accuracy through optimization techniques. Validation of the models showed that the CO-SVR model outperformed the others, achieving an R2 score of 0.953, Mean Squared Error (MSE) of 9.893, and Mean Absolute Error (MAE) of 2.549. This represents a significant improvement over the CO-ANFIS model, which obtained an R2 of 0.863, MSE of 23.583, and MAE of 3.912. The CO-POR model showed the lowest predictive capability, with an R2 score of 0.753, MSE of 57.608, and MAE of 6.756. These findings underscore the effectiveness of the Cheetah Optimizer in enhancing the accuracy of SVR models for temperature prediction, suggesting its potential for broader applications in predictive modeling where accuracy is paramount.