在混合物沸池中预测气泡离开直径的ANN使用修改后的ReLULU
1Department of Chemical Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Khuzestan, Iran.
Heliyon
|June 4, 2024
概括
研究人员使用修改的人工神经网络 (ANN) 改进了泡离开直径预测. 与现有的相关性相比,这种新模型显著减少了预测错误,有助于工业应用.
科学领域:
- * 热力学和流体力学
- * 热传递和相变现象
背景情况:
- *精确预测气泡离开直径对于优化工业沸系统至关重要.
- * 现有的水溶液中的气泡直径预测相关性显示出显著的偏差 (约. 40%) 由于复杂的流体相互作用.
- * 数学建模的局限性源于液体溶液中的异质沸介质和质量转移复杂性.
研究的目的:
- * 开发一个更准确的预测模型,用于泡离开直径在池沸.
- * 解决现有的预测相关性的不一致性和局限性.
- * 调查修改的人工神经网络 (ANN) 对此预测任务的应用.
主要方法:
- * 在氨酸和乙烯糖醇水溶液中实验测量气泡出口直径.
- * 修改了经典的人工神经网络 (ANN),具有灵活的直线单元 (ReLU) 激活功能.
- * 实施基因算法来训练修改后的ANN,取代传统的反向传播.
主要成果:
- * 修改后的ANN在预测泡离开直径时实现了10%的绝对平均误差.
- * 这种准确性在实验测量不确定性的边际内.
- * 修改后的ReLU激活功能的灵活性提高了预测性能.
结论:
- * 开发的ANN模型在泡离开直径预测方面的现有相关性方面取得了显著的改进.
- * 修改后的激活功能和遗传算法训练提高了模型的准确性和适应性.
- *精确的泡直径预测对于工业沸过程的设计,操作和优化至关重要.
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