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

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.本研究基于包含2460个数据点的建模数据集，采用经粒子群优化（Particle Swarm Optimization, PSO）、莱文贝格-马夸尔特（Levenberg–Marquardt, LM）算法及遗传算法（Genetic Algorithm, GA）优化的人工神经网络（Artificial Neural Network, ANN），构建了石灰饱和系数（Lime Saturation Factor, LSF）预测模型。研究所筛选得到的可靠输入变量包括石灰、二氧化硅、氧化铝及氧化铁。为提升模型的实用性与易用性，本研究将所构建的模型转化为数学方程，并进一步集成至应用软件当中。本研究采用包含100个数据点的验证数据集对各模型的性能开展对比验证，结果显示莱文贝格-马夸尔特优化人工神经网络（LM-ANN）模型表现最优，可被推荐用于石灰饱和系数的预测估算。