Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, 210037, China.
Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, SCNU Environmental Research Institute, South China Normal University, Guangzhou, 510006, China.
Bioprocess Biosyst Eng. 2021 Nov;44(11):2345-2359. doi: 10.1007/s00449-021-02608-5. Epub 2021 Jul 5.
It is of great importance to obtain accurate effluent quality indices in time for pulping and papermaking wastewater treatment processes. However, considering the complex characteristics of industrial wastewater treatment systems, conventional modeling methods such as partial least squares (PLS) and artificial neural networks (ANN) cannot achieve satisfactory prediction accuracy. As a supervised metric learning method, neighborhood component analysis (NCA) is able to significantly improve the prediction performance by training an appropriate model in metric space using the distance between samples for papermaking wastewater treatment processes. The results on two data sets show that NCA has a higher prediction accuracy compared with PLS and ANN. Specifically, NCA has the highest determination coefficient (R) and the lowest root mean square error in a benchmark simulation data set. On the other hand, the results on the data from an industrial wastewater process indicate that NCA has better modeling accuracy and its R increases by 32.80% and 29.08% compared with PLS and ANN, respectively. NCA provides a feasible way to realize online monitoring and automatic control in wastewater treatment processes.
对于制浆造纸废水处理过程，及时获得准确的出水质量指标非常重要。然而，考虑到工业废水处理系统的复杂特性，传统的建模方法（如偏最小二乘法（PLS）和人工神经网络（ANN））无法达到令人满意的预测精度。作为一种有监督的度量学习方法，近邻成分分析（NCA）能够通过在度量空间中使用样本之间的距离训练合适的模型，显著提高造纸废水处理过程的预测性能。在两个数据集上的结果表明，NCA 比 PLS 和 ANN 具有更高的预测精度。具体来说，NCA 在基准模拟数据集中具有最高的确定系数（R）和最低的均方根误差。另一方面，在工业废水过程的数据上的结果表明，NCA 具有更好的建模精度，与 PLS 和 ANN 相比，其 R 分别提高了 32.80%和 29.08%。NCA 为废水处理过程中的在线监测和自动控制提供了一种可行的方法。
