Tomperi Jani, Sorsa Aki, Ruuska Jari, Ruusunen Mika
Control Engineering Research Group, Environmental and Chemical Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014 Oulu, Finland.
J Environ Manage. 2025 Jan;373:123680. doi: 10.1016/j.jenvman.2024.123680. Epub 2024 Dec 14.
Continuous monitoring of chemical oxygen demand (COD) is essential to ensure efficient and sustainable wastewater treatment and regulatory compliance. However, traditional hardware measurements are laborious, infrequent and costly. In this research, a cost-effective real-time alternative is presented. ARMAX, ARX, Neural Network and PLSR model structures were identified and tested for finding data-based model for real-time estimation of effluent COD in a full-scale industrial WWTP. To aim for estimating effluent COD without physically measuring it, a novel chain of two estimators was created by connecting in series the identified influent and effluent COD models. A comprehensive and systematic model identification was carried out to find the model inputs, delays, parameters and training windows using an exhaustive search algorithm. The results showed that using solely linear model structure it is possible to identify sufficiently accurate (R: 0.67, MAPE: 7.33%) and practical (interpretable and implementable) data-based estimation model which has predictive ability even up to 20 h horizon. As the series-connected model structure reaches the required margin of error it has potential for real-world industrial usage alongside or even replacing the hardware online sensor. Estimation model enhances resiliency and provides real-time insights into effluent quality in varying operating conditions and during unexpected disturbances.
持续监测化学需氧量（COD）对于确保高效且可持续的废水处理以及符合监管要求至关重要。然而，传统的硬件测量方法既费力、频率低又成本高。在本研究中，提出了一种经济高效的实时替代方案。识别并测试了自回归滑动平均（ARMAX）、自回归（ARX）、神经网络和偏最小二乘回归（PLSR）模型结构，以找到基于数据的模型，用于在一座全尺寸工业污水处理厂中实时估算出水COD。为了在不进行物理测量的情况下估算出水COD，通过将已识别的进水和出水COD模型串联起来，创建了一种新型的双估计器链。使用穷举搜索算法进行了全面且系统的模型识别，以找到模型输入、延迟、参数和训练窗口。结果表明，仅使用线性模型结构就有可能识别出足够准确（相关系数R：0.67，平均绝对百分比误差MAPE：7.33%）且实用（可解释且可实施）的基于数据的估计模型，该模型甚至在长达20小时的预测范围内都具有预测能力。由于串联模型结构达到了所需的误差范围，它在实际工业应用中具有潜力，可与在线硬件传感器并行使用，甚至取而代之。估计模型增强了弹性，并能在不同运行条件下以及意外干扰期间提供有关出水水质的实时洞察。
