Mhohamdi Heyder, Altimari Usama S, Vaghela Krunal, Vivek V, Hota Sarbeswara, Singh Devendra, Manchanda Mahesh, Shomurotova Shirin, Tomar Prakhar, Alam Mohammad Mahtab
Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq.
Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq.
Sci Rep. 2025 Aug 13;15(1):29691. doi: 10.1038/s41598-025-15538-z.
A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression (GPR), Multi-layer Perceptron (MLP), and Polynomial Regression (PR) was carried out to predict chemical concentrations of solute in a dataset with two input variables (x and y) and one output feature (C in mol/m). Employing gradient-based hyperparameter optimization, the results reveal that MLP outperforms GPR and PR with a significantly higher R score (MLP: 0.999, GPR: 0.966, PR: 0.980) and lower RMSE (MLP: 0.583, GPR: 3.022, PR: 2.370). Moreover, MLP demonstrates the lowest Average Absolute Relative Deviation (AARD%) at 2.564%, compared to GPR's 18.733% and PR's 11.327%. Five-fold cross-validation confirms MLP's reliability (R² = 0.998 ± 0.001, RMSE = 0.590 ± 0.015). These findings underscore the practical utility of machine learning models, especially MLP, for accurate chemical concentration in environmental monitoring and process optimization with particular application for adsorption process.
将人工智能（AI）与计算流体动力学相结合，以推进吸附分离过程的建模。对包括高斯过程回归（GPR）、多层感知器（MLP）和多项式回归（PR）在内的三种基于AI的回归模型进行了比较研究，以预测具有两个输入变量（x和y）和一个输出特征（以mol/m为单位的C）的数据集中溶质的化学浓度。采用基于梯度的超参数优化，结果表明，MLP的表现优于GPR和PR，其R分数显著更高（MLP：0.999，GPR：0.966，PR：0.980），均方根误差更低（MLP：0.583，GPR：3.022，PR：2.370）。此外，MLP的平均绝对相对偏差（AARD%）最低，为2.564%，而GPR为18.733%，PR为11.327%。五重交叉验证证实了MLP的可靠性（R² = 0.998 ± 0.001，RMSE = 0.590 ± 0.015）。这些发现强调了机器学习模型，特别是MLP，在环境监测中精确化学浓度和过程优化方面的实际效用，尤其适用于吸附过程。
