Abstract Papermaking wastewater accounts for a large proportion of industrial wastewater, and it is essential to obtain accurate and reliable effluent indices in real-time. Considering the complexity, nonlinearity, and time variability of wastewater treatment processes, a dynamic kernel extreme learning machine (DKELM) method is proposed to predict the key quality indices of effluent chemical oxygen demand (COD). A time lag coefficient is introduced and a kernel function is embedded into the extreme learning machine (ELM) to extract dynamic information and obtain better prediction accuracy. A case study for modeling a wastewater treatment process is demonstrated to evaluate the performance of the proposed DKELM. The results illustrate that both training and prediction accuracy of the DKELM model is superior to other models. For the prediction of the quality indices of effluent COD, the determinate coefficient of the DKELM model is increased by 27.52 %, 21.36 %, 10.42 %, and 10.81 %, compared with partial least squares, ELM, dynamic ELM, and kernel ELM, respectively.

中文翻译：

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使用基于动态内核的极限学习机预测造纸废水处理过程中的出水水质

摘要 造纸废水在工业废水中占很大比例，实时获取准确可靠的出水指标至关重要。考虑到废水处理过程的复杂性、非线性和时间可变性，提出了一种动态核极限学习机（DKELM）方法来预测出水化学需氧量（COD）的关键质量指标。在极限学习机（ELM）中引入时滞系数并嵌入核函数以提取动态信息，获得更好的预测精度。展示了一个用于模拟废水处理过程的案例研究，以评估所提议的 DKELM 的性能。结果表明，DKELM 模型的训练和预测精度均优于其他模型。