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 Pre-processing data from published and unpublished previous studies treating biodiesel-, textile-, polymer-, and pulp and paper wastewater using an ABR and EGSB for artificial neural network (ANN) model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.

伦理审查编号：2021FEBEREC-STD-065 <br>预处理来自已发表和未发表的既往研究数据，这些研究采用厌氧折流板反应器（Anaerobic Baffled Reactor, ABR）和膨胀颗粒污泥床（Expanded Granular Sludge Bed, EGSB）处理生物柴油、纺织、聚合物及制浆造纸废水，旨在用于人工神经网络（ANN）模型的模拟与开发。<br>对于待解决的ANN问题而言，选择合适的学习率、动量、各隐藏层神经元数量及激活函数至关重要。因此，所收集的数据需以Microsoft Excel电子表格格式整理，包含输入列与输出列。随后，利用覆盖整个问题域的样本创建训练文件，以筛选所需参数。研究使用三类数据集：训练数据集、测试数据集及验证数据集。训练过程中，将通过测试数据对神经网络进行准确性验证；当平均误差在一段时间内保持稳定时，训练即停止。