Alardhi Saja Mohsen, Fiyadh Seef Saadi, Salman Ali Dawood, Adelikhah Mohammademad
Nanotechnology and Advanced Materials Research Center, University of Technology, Baghdad, Iraq.
Nanotechnology & Catalysis Research Centre (NANOCAT), IPS Building, University of Malaya, 50603 Kuala Lumpur, Malaysia.
Heliyon. 2023 Jan 10;9(1):e12888. doi: 10.1016/j.heliyon.2023.e12888. eCollection 2023 Jan.
In this study, methyl orange (MO) dye removal by adsorption utilizing activated carbon made from date seeds (DPAC) was modeled using an artificial neural network (ANN) technique. Instrumental investigations such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET) analysis were used to assess the physicochemical parameters of adsorbent. By changing operational parameters including adsorbent dosage (0.01-0.03 g), solution pH 3-8, initial dye concentration (5-20 mg/L), and contact time (2-60 min), the viability of date seeds for the adsorptive removal of methyl orange dye from aqueous solution was assessed in a batch procedure. The system followed the pseudo 2nd order kinetic model for DPAC adsorbent, according to the kinetic study (R2 = 0.9973). The mean square error (MSE), relative root mean square error (RRMSE), root mean square error (RMSE), mean absolute percentage error (MAPE), relative error (RE), and correlation coefficient (R) were used to measure the ANN model performance. The maximum RE was 8.24% for the ANN model. Two isotherm models, Langmuir and Freundlich, were studied to fit the equilibrium data. Compared with the Freundlich isotherm model (R = 0.72), the Langmuir model functioned better as an adsorption isotherm with R of 0.9902. Thus, this study demonstrates that the dye removal process can be predicted using an ANN technique, and it also suggests that adsorption onto DPAC may be employed as a main treatment for dye removal from wastewater.
在本研究中，利用枣核制备的活性炭（DPAC）通过吸附去除甲基橙（MO）染料的过程采用人工神经网络（ANN）技术进行建模。采用X射线衍射（XRD）、扫描电子显微镜（SEM）和布鲁诺尔-埃米特-泰勒（BET）分析等仪器研究方法来评估吸附剂的物理化学参数。通过改变操作参数，包括吸附剂用量（0.01 - 0.03 g）、溶液pH值3 - 8、初始染料浓度（5 - 20 mg/L）和接触时间（2 - 60分钟），采用分批法评估枣核从水溶液中吸附去除甲基橙染料的可行性。根据动力学研究，该体系遵循DPAC吸附剂的准二级动力学模型（R2 = 0.9973）。使用均方误差（MSE）、相对均方根误差（RRMSE）、均方根误差（RMSE）、平均绝对百分比误差（MAPE）、相对误差（RE）和相关系数（R）来衡量ANN模型的性能。ANN模型的最大相对误差为8.24%。研究了两种等温线模型，即朗缪尔模型和弗伦德里希模型，以拟合平衡数据。与弗伦德里希等温线模型（R = 0.72）相比，朗缪尔模型作为吸附等温线的拟合效果更好，R为0.9902。因此，本研究表明可以使用ANN技术预测染料去除过程，并且还表明吸附到DPAC上可作为从废水中去除染料的主要处理方法。
