Dolatabadi Maryam, Mehrabpour Marjan, Esfandyari Morteza, Ahmadzadeh Saeid
Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran.
Environmental Science and Technology Research Center, Department of Environmental Health Engineering, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
MethodsX. 2020 Apr 18;7:100885. doi: 10.1016/j.mex.2020.100885. eCollection 2020.
Artificial Neural Networks (ANNs) model and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate and predict the removal efficiency of tetracycline (TC) using the adsorption process from aqueous solutions. The obtained results demonstrated that the optimum condition for removal efficiency of TC were 1.5 g L modified zeolite (MZ), pH of 8.0, initial TC concentration of 10.0 mg L, and reaction time of 60 min. Among the different back-propagation algorithms, the Marquardt-Levenberg learning algorithm was selected for ANN Model. The log sigmoid transfer function (log sig) at the hidden layer with ten neurons in the first layer and a linear transfer function were used for prediction of the removal efficiency. Accordingly, a correlation coefficient, mean square error, and absolute error percentage of 0.9331, 0.0017, and 0.56% were obtained for the total dataset, respectively. The results revealed that the ANN has great performance in predicting the removal efficiency of TC.•ANNs used to estimate and predict tetracycline antibiotic removal using the adsorption process from aqueous solutions.•The model's predictive performance evaluated by MSE, MAPE, and R.
利用人工神经网络（ANNs）模型和自适应神经模糊推理系统（ANFIS），通过吸附过程对四环素（TC）从水溶液中的去除效率进行估计和预测。所得结果表明，TC去除效率的最佳条件为1.5 g/L改性沸石（MZ）、pH值为8.0、初始TC浓度为10.0 mg/L以及反应时间为60分钟。在不同的反向传播算法中，为ANN模型选择了Marquardt-Levenberg学习算法。在第一层具有十个神经元的隐藏层使用对数Sigmoid传递函数（log sig）以及线性传递函数来预测去除效率。因此，总数据集的相关系数、均方误差和绝对误差百分比分别为0.9331、0.0017和0.56%。结果表明，ANN在预测TC的去除效率方面具有出色的性能。
•使用人工神经网络通过吸附过程对四环素抗生素的去除进行估计和预测。
•通过均方误差、平均绝对百分比误差和相关系数评估模型的预测性能。
