Design and optimization of spherical agglomeration process based on machine learning strategy

Spherical particles stand out as high-value products with superior macroscopic properties and enhanced downstream processing efficiency. In this study, an integrated digital design strategy, combining artificial neural networks (ANN) and genetic algorithms (GA) has been employed to optimize the spherical agglomeration (SA) process. Initially, a dataset of benzoic acid SA processes was created, which was subsequently employed for training and testing the ANN model. An environmental impact sustainability index (STI) was constructed to assess the environmental effects associated with each operational variable in the SA process. To attain multi-objective optimization, a GA was employed in combination with the ANN model. In addition, a Score function was formulated to generate Pareto fronts, tailored to meet the specific needs of real scenarios, considering variations in the assigned weights. Furthermore, the model was adapted for aspirin SA process, enhancing predictive abilities with only 20% of original data on operating conditions.