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.

中文翻译：

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基于机器学习策略的球形团聚过程设计与优化


球形颗粒作为高价值产品脱颖而出，具有优异的宏观性能和更高的下游加工效率。在本研究中，采用结合人工神经网络（ANN）和遗传算法（GA）的集成数字设计策略来优化球形团聚（SA）过程。最初，创建了苯甲酸 SA 过程的数据集，随后用于训练和测试 ANN 模型。构建环境影响可持续性指数 (STI) 来评估与 SA 流程中每个运营变量相关的环境影响。为了实现多目标优化，将遗传算法与人工神经网络模型结合使用。此外，还制定了评分函数来生成帕累托前沿，考虑到分配权重的变化，为满足实际场景的特定需求而量身定制。此外，该模型适用于阿司匹林 SA 流程，仅用 20% 的原始操作条件数据即可增强预测能力。