Thajudeen Kamal Y, Ahmed Mohammed Muqtader, Alshehri Saad Ali
Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha, 62529, Saudi Arabia.
Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, AlKharj, 11942, Saudi Arabia.
Sci Rep. 2025 Apr 26;15(1):14628. doi: 10.1038/s41598-025-99937-2.
Computational modeling based on heat transfer was developed to model temperature distribution in a liquid-phase chemical reactor. The model is hybrid by combining heat transfer and machine learning in simulation of the process. The simulated process is a tubular chemical reactor for synthesis of a target compound. CFD (Computational Fluid Dynamics) was employed for the simulations and linked to machine learning for advanced modeling. The models under investigation include Bayesian Ridge Regression, Support Vector Machine, Deep Neural Network, and Attention-based Deep Neural Network. Hyper-parameter optimization is carried out using the Jellyfish Swarm Optimizer to enhance model performance. The Bayesian Ridge Regression model exhibited a commendable performance with a score of 0.86039 using R metric. The Deep Neural Network model, showcasing exceptional predictive accuracy, obtained an outstanding R score of 0.99147. It was indicated that machine learning integrated CFD is a useful method for simulation and optimization of liquid-phase chemical reactors.
基于传热的计算模型被开发用于模拟液相化学反应器中的温度分布。该模型在过程模拟中通过结合传热和机器学习实现了混合。模拟过程是一个用于合成目标化合物的管式化学反应器。采用计算流体动力学（CFD）进行模拟，并与机器学习相结合以进行高级建模。所研究的模型包括贝叶斯岭回归、支持向量机、深度神经网络和基于注意力的深度神经网络。使用水母群优化器进行超参数优化以提高模型性能。贝叶斯岭回归模型使用R指标的得分为0.86039，表现出值得称赞的性能。深度神经网络模型展示了卓越的预测准确性，获得了0.99147的出色R分数。结果表明，机器学习与CFD相结合是模拟和优化液相化学反应器的一种有用方法。
