Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis
Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis
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DOI：
10.1590/s0104-66322002000400002
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发表时间：
2002-12
影响因子：
1.2
通讯作者：
K. Oliveira-Esquerre;M. Mori;R. Bruns
中科院分区：
文献类型：
--
作者：
K. Oliveira-Esquerre;M. Mori;R. Bruns
这项工作提出了一种方法来预测的生物废水处理厂在RIPASA S/A纤维素e Papel，在巴西的主要纸浆和造纸厂之一的输出流的生化需氧量（BOD）。当数据在被馈送到反向传播神经网络之前使用主成分分析（PCA）进行预处理时，可以实现最佳的预测性能。分析了输入变量的影响，并在优化情况下得到了满意的预测结果。
This work presents a way to predict the biochemical oxygen demand (BOD) of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation.