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开发混合计算模型来模拟化学反应堆中的传热和温度预测
Kamal Y Thajudeen1, Mohammed Muqtader Ahmed2, Saad Ali Alshehri3
1Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha, 62529, Saudi Arabia. kthajudeen@kku.edu.sa.
Scientific reports
|April 26, 2025
概括
本研究介绍了一种混合计算模型,将传热和机器学习结合起来,用于模拟液相化学反应堆. 深度神经网络在预测温度分布方面实现了高精度 (R2=0.99147).
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 准确的温度预测对于优化液相化学反应器性能至关重要.
- 传统的模拟方法可能需要大量的计算资源.
- 整合机器学习为提高模拟效率提供了一个潜在的途径.
研究的目的:
- 开发和评估一种混合计算模型,用于模拟管状化学反应堆中的温度分布.
- 为了比较与计算流体动力学 (CFD) 集成的各种机器学习模型的性能.
- 评估超参数优化的有效性,以提高模型准确性.
主要方法:
- 开发一种混合模型,将传热原理与机器学习算法结合起来.
- 使用计算流体动力学 (CFD) 进行详细的过程模拟.
- 研究贝叶斯回归,支持向量机,深度神经网络和基于注意力的深度神经网络模型.
- 使用水母群群优化器进行超参数调整.
主要成果:
- 深度神经网络模型表现出卓越的预测准确性,达到0.99147.7的R2得分.
- 贝叶斯山脊回归模型也表现出良好的表现,R2得分为0.86039.
- 使用水母群群优化器进行超参数优化,有效地提高了模型性能.
结论:
- 与CFD集成的机器学习提供了一种强大而准确的方法来模拟液相化学反应堆.
- 开发的混合型号显示了对优化反应堆设计和操作的承诺.
- 深度神经网络是复杂化学过程建模的高效方法.
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