Shen Wenhao, Huang Feini, Zhang Xuewen, Zhu Yuefei, Chen Xiaoquan, Akbarjon Nishonov
State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, China E-mail:
Water Sci Technol. 2018 Aug;78(1-2):310-319. doi: 10.2166/wst.2018.299.
Chemical oxygen demand (COD), an important indicative measure of the amount of oxidizable pollutants in wastewater, is often analyzed off-line due to the expensive sensor required for on-line analysis. However, its off-line analysis is time-consuming. An on-line COD estimation method was developed with photoelectrocatalytic (PEC) technology. Based on the on-line data of the oxidation-reduction potential (ORP), dissolved oxygen (DO) and pH of wastewater, four different artificial neural network methods were applied to develop working models for COD estimation. Six different batches of sequence batch reactor (SBR) effluent from a paper mill were treated with PEC oxidation for 90 minutes, and 546 data points were collected from the on-line measurements of ORP, DO and pH, and the off-line COD analysis. After having training and validation with 75% and 25% of data, and evaluation with four statistical criteria (R, RMSE, MAE and MAPE), the estimation results indicated that the developed radial basis neural network (RBNN) model demonstrated the highest precision. Subsequently, the application of the RBNN model to a new batch of SBR effluent from the paper mill revealed that the RBNN model was acceptable for COD estimation during the PEC advanced treatment process of papermaking wastewater, which implied its possible application in the future.
化学需氧量（COD）是衡量废水中可氧化污染物含量的一项重要指标，由于在线分析所需的传感器成本高昂，其分析通常采用离线方式。然而，离线分析耗时较长。利用光电催化（PEC）技术开发了一种在线COD估算方法。基于废水的氧化还原电位（ORP）、溶解氧（DO）和pH值的在线数据，应用四种不同的人工神经网络方法建立了COD估算工作模型。对某造纸厂六批不同的序批式反应器（SBR）出水进行了90分钟的PEC氧化处理，通过对ORP、DO和pH值的在线测量以及离线COD分析，收集了546个数据点。在使用75%的数据进行训练和验证，并采用四个统计标准（R、RMSE、MAE和MAPE）进行评估后，估算结果表明所开发的径向基神经网络（RBNN）模型具有最高的精度。随后，将RBNN模型应用于造纸厂一批新的SBR出水，结果表明RBNN模型在造纸废水PEC深度处理过程中对COD估算具有可接受性，这意味着其在未来可能具有应用价值。
