Department of Civil and Environmental Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea.
Bioprocess Biosyst Eng. 2011 Oct;34(8):963-73. doi: 10.1007/s00449-011-0547-6. Epub 2011 May 1.
This work proposes a sequential modelling approach using an artificial neural network (ANN) to develop four independent multivariate models that are able to predict the dynamics of biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solid (SS), and total nitrogen (TN) removal in a wastewater treatment plant (WWTP). Suitable structures of ANN models were automatically and conveniently optimized by a genetic algorithm rather than the conventional trial and error method. The sequential modelling approach, which is composed of two parts, a process disturbance estimator and a process behaviour predictor, was also presented to develop multivariate dynamic models. In particular, the process disturbance estimator was first employed to estimate the influent quality. The process behaviour predictor then sequentially predicted the effluent quality based on the estimated influent quality from the process disturbance estimator with other process variables. The efficiencies of the developed ANN models with a sequential modelling approach were demonstrated with a practical application using a data set collected from a full-scale WWTP during 2 years. The results show that the ANN with the sequential modelling approach successfully developed multivariate dynamic models of BOD, COD, SS, and TN removal with satisfactory estimation and prediction capability. Thus, the proposed method could be used as a powerful tool for the prediction of complex and nonlinear WWTP performance.
本工作提出了一种使用人工神经网络（ANN）的顺序建模方法，开发了四个独立的多元模型，能够预测污水处理厂（WWTP）中生化需氧量（BOD）、化学需氧量（COD）、悬浮固体（SS）和总氮（TN）去除的动态。遗传算法而不是传统的试错法自动方便地优化了 ANN 模型的合适结构。还提出了一种由两部分组成的顺序建模方法，即过程干扰估计器和过程行为预测器，以开发多元动态模型。特别是，首先使用过程干扰估计器来估计进水质量。然后，基于过程干扰估计器从过程干扰估计器中估计的进水质量以及其他过程变量，顺序预测出水质量。使用从 2 年期间从全尺寸 WWTP 收集的数据集进行实际应用，证明了具有顺序建模方法的开发的 ANN 模型的效率。结果表明，具有顺序建模方法的 ANN 成功地开发了 BOD、COD、SS 和 TN 去除的多元动态模型，具有令人满意的估计和预测能力。因此，该方法可以用作预测复杂和非线性 WWTP 性能的有力工具。
