Optimized Artificial Neural Network-Based Mathematical Model and Software Application for Predicting Raw Mix Lime Saturation Factor for High-Quality Cement Production

This study develops LSF predictive models by employing artificial neural networks (ANN) optimized with particle swarm optimization (PSO), Levenberg–Marquardt (LM), and genetic algorithms (GA), using model dataset of two thousand four hundred and sixty data points. Dependable variables selected were lime, silica, alumina, and iron oxide. To enhance the practicality and ease of use, the models were converted into mathematical equations and further integrated into software application. The models' performance was compared using verification dataset of one hundred data points and LM-ANN model presented the best performance and strongly recommended for LSF estimation.