Prediction of biological wastewater treatment performance using artificial neural networks

Ethical Clearance Reference Number: 2021FEBEREC-STD- 065 <br> Pre-processing data from published and unpublished previous studies treating biodiesel-, textile-, polymer-, and pulp and paper wastewater using an ABR and EGSB for artificial neural network (ANN) model simulation and developnent. <br> For ANN problems to be solved, the selection of a suitable learning rate, momentum, the number of neurons from each of the hidden layers and the activation function is crucial. Therefore, the collected data must be prepared in a Microsoft Excel spreadsheet format with input and output columns. A training file is then created with samples of the whole problem domain to select the required parameters. Three data sets are used: a training data set, test data set and validation data set. When the training process takes place, the neural network will be tested against the testing data to determine accuracy, and training will be stopped when the mean average error remains the same for a period of time.

伦理审查批准编号：2021FEBEREC-STD-065
本数据集预处理了来自已发表及未发表的既往研究数据，相关研究采用厌氧折流板反应器（ABR）与膨胀颗粒污泥床反应器（EGSB）处理生物柴油废水、纺织废水、聚合物废水及浆纸废水，用于人工神经网络（artificial neural network，ANN）的模型模拟与开发。
对于待求解的人工神经网络任务而言，选取合适的学习率、动量项、各隐藏层的神经元数量以及激活函数至关重要。因此，需将采集得到的数据整理为Microsoft Excel电子表格格式，并设置输入列与输出列；随后需基于全问题域的样本创建训练文件，以筛选所需的模型参数。
本次实验共使用三类数据集：训练数据集、测试数据集与验证数据集。在训练过程中，需通过测试数据集对神经网络进行测试以评估其精度；当平均误差在一段时间内保持稳定时，即可终止训练。