The ibuprofen (IB) residuals in water resources are classified as toxic and non-biodegradable contaminants. In the previous study, the Zn-decorated S,P,B co-doped CN (Zn-SPB@CN) nanosheet exhibited significant effectiveness in adsorbing IB from aqueous solutions. However, the operating conditions were not optimized for the adsorption process. The current study modeled the adsorption of IB onto Zn-SPB@CN nanosheets employing central composite design (CCD) and machine learning (ML) methods under various operating conditions. The operating conditions included adsorbent mass, initial IB concentration, and pH. The CCD model revealed a mean squared error (MSE) value of 36.56. The ML investigations showed MSE values of 28.12 for the artificial neural network (ANN), 10.12 for the decision tree (DT), 8.68 for the linear regression (LR), and 3.70 for the random forest (RF) models. The RF model demonstrated high reliability in predicting IB removal across various conditions compared to other methods. Using the RF model, a maximum removal efficiency of 98 % was achieved under the optimized operating conditions, containing a pH of 7, an initial concentration of 59 mg/L, and an adsorbent mass of 0.020 g.

中文翻译：

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模拟布洛芬在 Zn 修饰的 S、P、B 共掺杂 C2N 纳米片上的吸附：机器学习和中心复合材料设计方法

水资源中的布洛芬 (IB) 残留物被归类为有毒和不可生物降解的污染物。在之前的研究中，Zn修饰的S、P、B共掺杂CN（Zn-SPB@CN）纳米片在从水溶液中吸附IB方面表现出显着的有效性。然而，吸附过程的操作条件并未优化。目前的研究采用中心复合设计（CCD）和机器学习（ML）方法在各种操作条件下模拟了IB在Zn-SPB@CN纳米片上的吸附。操作条件包括吸附剂质量、初始 IB 浓度和 pH 值。 CCD 模型显示均方误差 (MSE) 值为 36.56。 ML 研究显示，人工神经网络 (ANN) 的 MSE 值为 28.12，决策树 (DT) 的 MSE 值为 10.12，线性回归 (LR) 的 MSE 值为 8.68，随机森林 (RF) 模型的 MSE 值为 3.70。与其他方法相比，RF 模型在预测各种条件下的 IB 去除方面表现出高度可靠性。使用RF模型，在pH为7、初始浓度为59 mg/L、吸附剂质量为0.020 g的优化操作条件下，最大去除效率达到98%。