Engineering Training Center, Nanjing Vocational University of Industry Technology, Nanjing, Jiangsu, 210023, China.
Center for Advanced Chemistry, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang, 550000, Vietnam.
Environ Res. 2022 Nov;214(Pt 3):113953. doi: 10.1016/j.envres.2022.113953. Epub 2022 Aug 4.
A popular approach to select optimal adsorbents is to perform parallel experiments on adsorbents based on an initially decided goal such as specified product purity, efficiency, or binding capacity. To screen optimal adsorbents, we focused on the max adsorption capacity of the candidates at equilibrium in this work because the adsorption capacity of each adsorbent is strongly dependent on certain conditions. A data-driven machine learning tool for predicting the max adsorption capacity (Q) of 19 pharmaceutical compounds on 88 biochars was developed. The range of values of Q (mean 48.29 mg/g) was remarkably large, with a high number of outliers and large variability. Modified biochars enhanced the Q and surface area values compared with the original biochar, with a statistically significant difference (Chi-square value = 7.21-18.25, P < 0.005). K- nearest neighbors (KNN) was found to be the most optimal algorithm with a root mean square error (RMSE) of 23.48 followed by random forest and Cubist with RMSE of 26.91 and 29.56, respectively, whereas linear regression and regularization were the worst algorithms. KNN model achieved R of 0.92 and RMSE of 16.62 for the testing data. A web app was developed to facilitate the use of the KNN model, providing a reliable solution for saving time and money in unnecessary lab-scale adsorption experiments while selecting appropriate biochars for pharmaceutical adsorption.
一种常用的选择最佳吸附剂的方法是根据初始设定的目标（如特定的产品纯度、效率或结合容量）在吸附剂上进行平行实验。为了筛选最佳的吸附剂，我们在这项工作中主要关注平衡时候选物的最大吸附容量，因为每个吸附剂的吸附容量强烈依赖于某些条件。本文开发了一种数据驱动的机器学习工具，用于预测 19 种药物化合物在 88 种生物炭上的最大吸附容量（Q）。Q 值（平均值为 48.29mg/g）的变化范围非常大，存在大量异常值和较大的可变性。与原始生物炭相比，改性生物炭提高了 Q 值和比表面积值，且差异具有统计学意义（卡方值为 7.21-18.25，P<0.005）。结果发现，K-最近邻（KNN）算法是最优化的算法，其均方根误差（RMSE）为 23.48，其次是随机森林和 Cubist，RMSE 分别为 26.91 和 29.56，而线性回归和正则化则是最差的算法。KNN 模型在测试数据上的 R 为 0.92，RMSE 为 16.62。还开发了一个网络应用程序，以方便使用 KNN 模型，为节省时间和金钱提供了可靠的解决方案，避免在不必要的实验室规模的吸附实验中浪费时间和金钱，同时为药物吸附选择合适的生物炭。
